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	10	4000		int	No
n_samples	range	100	1000		int	No
threshold	range	0.2	0.8		float	No
outlier_detector_kwargs	fixed			\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}

Failed	Succeeded	Running	Total
0	418	2	420

name	min/max
ACCURACY	max
RUNTIME	min

start_time,end_time,run_time,program_string,recent_samples_size,n_samples,threshold,outlier_detector_kwargs,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
1742404135,1742404193,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2089 n_samples 534 threshold 0.4880471825599671 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2089,534,0.4880471825599671,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.48493457943925233,40.49141597747803,0,None,i7088,40.49141597747803,707.078125,694.4715401785714,-1,0,3297392
1742404137,1742404195,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 108 n_samples 669 threshold 0.3074942871928215 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",108,669,0.3074942871928215,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.48485981308411213,42.635582447052,0,None,i7020,42.635582447052,710.05078125,695.0055803571429,-1,0,3297582
1742404105,1742404200,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 382 n_samples 378 threshold 0.6715214857831597 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",382,378,0.6715214857831597,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5669408099688473,83.24758076667786,0,None,i7051,83.24758076667786,812.78515625,745.16015625,-1,0,3297165
1742404104,1742404213,109,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2967 n_samples 972 threshold 0.34286774694919586 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2967,972,0.34286774694919586,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5941682242990655,92.32121515274048,0,None,i7127,92.32121515274048,870.93359375,777.1435546875,-1,0,3297053
1742404137,1742404214,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2330 n_samples 627 threshold 0.570513110794127 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2330,627,0.570513110794127,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5390155763239876,59.080878257751465,0,None,i7086,59.080878257751465,777.46875,723.86474609375,-1,0,3297410
1742404165,1742404228,63,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2233 n_samples 813 threshold 0.6658900454640388 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2233,813,0.6658900454640388,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.46210591900311526,48.220457315444946,0,None,i7165,48.220457315444946,728.75,701.7421875,-1,0,3297814
1742404137,1742404233,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2627 n_samples 694 threshold 0.43472759258002047 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2627,694,0.43472759258002047,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5825794392523365,79.6238660812378,0,None,i7009,79.6238660812378,846.6015625,758.50625,-1,0,3297623
1742404193,1742404244,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2005 n_samples 269 threshold 0.7733552770689132 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2005,269,0.7733552770689132,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.33581308411214955,35.896564245224,0,None,i7053,35.896564245224,686.875,686.6048177083334,-1,0,3297931
1742404163,1742404251,88,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 223 n_samples 156 threshold 0.5421582013368607 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",223,156,0.5421582013368607,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5899688473520249,78.74886083602905,0,None,i7165,78.74886083602905,843.9609375,755.323046875,-1,0,3297647
1742404162,1742404252,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 842 n_samples 650 threshold 0.7060882236808539 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",842,650,0.7060882236808539,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5560996884735202,75.83203721046448,0,None,i7026,75.83203721046448,816.98828125,748.612890625,-1,0,3297780
1742404140,1742404255,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 745 n_samples 788 threshold 0.535706744901836 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",745,788,0.535706744901836,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5718130841121495,96.88928604125977,0,None,i7098,96.88928604125977,846.86328125,761.642578125,-1,0,3297351
1742404136,1742404257,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3603 n_samples 738 threshold 0.78894537165761 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3603,738,0.78894537165761,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5630654205607477,103.0871524810791,0,None,i7133,103.0871524810791,833.68359375,760.1463341346154,-1,0,3297274
1742404138,1742404266,128,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1467 n_samples 901 threshold 0.7493279833346607 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1467,901,0.7493279833346607,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.577196261682243,113.41761517524719,0,None,i7063,113.41761517524719,859.51171875,772.4944196428571,-1,0,3297467
1742404138,1742404280,142,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3252 n_samples 859 threshold 0.41014631595462564 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3252,859,0.41014631595462564,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.603190031152648,127.03914856910706,0,None,i7043,127.03914856910706,906.1796875,791.7208333333333,-1,0,3297530
1742404192,1742404288,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 584 n_samples 502 threshold 0.31980426590889693 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",584,502,0.31980426590889693,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.590778816199377,85.85349559783936,0,None,i7145,85.85349559783936,885.734375,776.4708806818181,-1,0,3297829
1742404162,1742404289,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3065 n_samples 459 threshold 0.7379675155505538 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3065,459,0.7379675155505538,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5847850467289719,112.62392854690552,0,None,i7092,112.62392854690552,883.203125,776.919921875,-1,0,3297701
1742404137,1742404290,153,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2693 n_samples 188 threshold 0.7063181577250361 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2693,188,0.7063181577250361,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5977694704049844,137.8378963470459,0,None,i7015,137.8378963470459,927.56640625,807.088134765625,-1,0,3297609
1742404109,1742404299,190,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3475 n_samples 300 threshold 0.6437933895736934 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3475,300,0.6437933895736934,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6158380062305296,175.4327495098114,0,None,i7132,175.4327495098114,1021.15234375,849.04609375,-1,0,3297234
1742404162,1742404302,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1729 n_samples 884 threshold 0.2382522322237492 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1729,884,0.2382522322237492,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6043738317757009,124.59479975700378,0,None,i7125,124.59479975700378,949.640625,818.5796875,-1,0,3297680
1742404163,1742404303,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3951 n_samples 925 threshold 0.5834502698853612 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3951,925,0.5834502698853612,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6017570093457943,130.3091320991516,0,None,i7086,130.3091320991516,939.6015625,819.2099609375,-1,0,3297723
1742404192,1742404306,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1120 n_samples 764 threshold 0.618828265927732 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1120,764,0.618828265927732,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5825545171339563,101.81914710998535,0,None,i7119,101.81914710998535,885.1171875,785.0270432692307,-1,0,3297850
1742404162,1742404336,174,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2491 n_samples 228 threshold 0.28016456160694364 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2491,228,0.28016456160694364,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.61601246105919,157.24404573440552,0,None,i7024,157.24404573440552,961.7890625,824.2174479166666,-1,0,3297784
1742404137,1742404340,203,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 985 n_samples 259 threshold 0.4526823472231627 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",985,259,0.4526823472231627,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6177445482866044,187.9888310432434,0,None,i7072,187.9888310432434,1047.01953125,863.3727678571429,-1,0,3297444
1742404193,1742404345,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3341 n_samples 581 threshold 0.20347787626087666 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3341,581,0.20347787626087666,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6100685358255452,137.17150473594666,0,None,i7082,137.17150473594666,967.51171875,822.108154296875,-1,0,3297904
1742404105,1742404348,243,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1818 n_samples 557 threshold 0.3699252160266042 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1818,557,0.3699252160266042,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6182554517133957,227.4480812549591,0,None,i7019,227.4480812549591,1023.2734375,852.261875,-1,0,3297203
1742404142,1742404381,239,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1595 n_samples 491 threshold 0.6041856907308103 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1595,491,0.6041856907308103,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6114267912772585,225.43043184280396,0,None,i7027,225.43043184280396,966.51953125,834.33625,-1,0,3297552
1742404162,1742404398,236,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1358 n_samples 390 threshold 0.4021547798067332 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1358,390,0.4021547798067332,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6141183800623053,219.54666876792908,0,None,i7064,219.54666876792908,1035.5625,860.7180989583334,-1,0,3297744
1742404137,1742404450,313,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3863 n_samples 421 threshold 0.2743687259033323 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3863,421,0.2743687259033323,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6139065420560748,295.1617262363434,0,None,i7050,295.1617262363434,1132.12890625,906.9512939453125,-1,0,3297501
1742404137,1742404652,515,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1207 n_samples 147 threshold 0.23415624257177114 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1207,147,0.23415624257177114,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6104423676012461,496.74831891059875,0,None,i7110,496.74831891059875,1355.39453125,1023.9481670673077,-1,0,3297323
1742404194,1742405014,820,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3712 n_samples 115 threshold 0.3673718743026257 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3712,115,0.3673718743026257,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6101059190031153,804.3056778907776,0,None,i7098,804.3056778907776,1625.21484375,1097.5732774849398,-1,0,3297869
1742405689,1742405734,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 459 threshold 0.40516047176851233 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,459,0.40516047176851233,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.38616822429906544,30.95675492286682,0,None,i7128,30.95675492286682,687.71484375,687.5930989583334,-1,0,3305985
1742405689,1742405734,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.42328971962616824,34.074578285217285,0,None,i7102,34.074578285217285,688.44140625,688.2936197916666,-1,0,3306017
1742405689,1742405741,52,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 35 n_samples 839 threshold 0.661105025509307 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",35,839,0.661105025509307,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3601993769470405,39.98389387130737,0,None,i7122,39.98389387130737,688.15234375,687.8984375,-1,0,3305991
1742405690,1742405741,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 19 n_samples 629 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",19,629,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3361993769470405,40.45497989654541,0,None,i7099,40.45497989654541,687.8046875,687.7745535714286,-1,0,3306021
1742405688,1742405746,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2392 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2392,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5295202492211838,47.37524175643921,0,None,i7118,47.37524175643921,755.94140625,713.66796875,-1,0,3305996
1742405689,1742405747,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 61 n_samples 955 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",61,955,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3249221183800623,45.05485558509827,0,None,i7077,45.05485558509827,688.19921875,688.0245535714286,-1,0,3306052
1742405690,1742405760,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2360 n_samples 645 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2360,645,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5596261682242991,55.66987943649292,0,None,i7052,55.66987943649292,795.140625,735.39990234375,-1,0,3306075
1742405689,1742405765,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2545 n_samples 648 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2545,648,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5112398753894081,62.10088896751404,0,None,i7143,62.10088896751404,773.59375,727.6098090277778,-1,0,3305964
1742405689,1742405766,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2697 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2697,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5632897196261683,64.81465148925781,0,None,i7050,64.81465148925781,819.82421875,749.76953125,-1,0,3306080
1742405690,1742405767,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2802 n_samples 958 threshold 0.6904875627893872 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2802,958,0.6904875627893872,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5391775700934579,67.89517450332642,0,None,i7154,67.89517450332642,813.4921875,739.9244791666666,-1,0,3305950
1742405690,1742405772,82,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2565 n_samples 1000 threshold 0.5611651361610568 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2565,1000,0.5611651361610568,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5295576323987539,69.81764316558838,0,None,i7115,69.81764316558838,784.08203125,725.5286458333334,-1,0,3306002
1742405689,1742405772,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 820 n_samples 1000 threshold 0.3768008670397152 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",820,1000,0.3768008670397152,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5779563862928349,71.1731309890747,0,None,i7083,71.1731309890747,839.0234375,761.158203125,-1,0,3306042
1742405689,1742405772,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2643 n_samples 1000 threshold 0.789720780113858 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2643,1000,0.789720780113858,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.38675389408099686,70.82110452651978,0,None,i7095,70.82110452651978,699.6484375,693.837109375,-1,0,3306028
1742405689,1742405773,84,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 799 n_samples 929 threshold 0.2790200074210682 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",799,929,0.2790200074210682,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5853333333333334,72.5462019443512,0,None,i7152,72.5462019443512,847.58984375,764.113671875,-1,0,3305957
1742405690,1742405780,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 419 n_samples 698 threshold 0.4043078410090385 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",419,698,0.4043078410090385,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5595015576323987,74.59769606590271,0,None,i7034,74.59769606590271,796.84375,736.893359375,-1,0,3306101
1742405688,1742405784,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3293 n_samples 1000 threshold 0.7001173985994537 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3293,1000,0.7001173985994537,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5591526479750779,85.98956489562988,0,None,i7067,85.98956489562988,859.1171875,768.9840198863636,-1,0,3306062
1742405688,1742405784,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 908 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",908,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5939563862928349,81.28911137580872,0,None,i7090,81.28911137580872,858.73046875,771.8441051136364,-1,0,3306035
1742405689,1742405785,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3095 n_samples 1000 threshold 0.6116415026452431 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3095,1000,0.6116415026452431,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5657445482866044,80.55125880241394,0,None,i7082,80.55125880241394,857.78515625,771.05859375,-1,0,3306046
1742405689,1742405792,103,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2506 n_samples 363 threshold 0.5024777827003389 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2506,363,0.5024777827003389,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5984922118380063,88.92053079605103,0,None,i7110,88.92053079605103,877.66015625,775.0163352272727,-1,0,3306009
1742405689,1742405804,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2567 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2567,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5938816199376947,101.97253704071045,0,None,i7026,101.97253704071045,901.58984375,790.9362980769231,-1,0,3306110
1742405690,1742405811,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 890 n_samples 340 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",890,340,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5762616822429907,108.39944267272949,0,None,i7046,108.39944267272949,862.1953125,767.0621995192307,-1,0,3306090
1742405690,1742405815,125,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1647 n_samples 982 threshold 0.5167332859683517 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1647,982,0.5167332859683517,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5966604361370716,111.87282872200012,0,None,i7175,111.87282872200012,907.76171875,796.50390625,-1,0,3305944
1742405690,1742405816,126,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 895 n_samples 761 threshold 0.2145797106821084 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",895,761,0.2145797106821084,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5950280373831776,108.72883796691895,0,None,i7029,108.72883796691895,886.390625,780.5516826923077,-1,0,3306106
1742405690,1742405816,126,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3698 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3698,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6072274143302181,115.05232787132263,0,None,i7060,115.05232787132263,928.828125,806.5184151785714,-1,0,3306066
1742405690,1742405818,128,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1617 n_samples 1000 threshold 0.46697670581707545 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1617,1000,0.46697670581707545,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5998006230529594,112.61071729660034,0,None,i7055,112.61071729660034,912.8125,799.8342633928571,-1,0,3306070
1742405689,1742405836,147,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3942 n_samples 1000 threshold 0.2784713956869346 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3942,1000,0.2784713956869346,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.606766355140187,133.18578815460205,0,None,i7133,133.18578815460205,951.01953125,820.3828125,-1,0,3305977
1742405688,1742405880,192,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1122 n_samples 100 threshold 0.7563713432168951 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1122,100,0.7563713432168951,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6033271028037384,179.47460651397705,0,None,i7039,179.47460651397705,1033.203125,847.1427734375,-1,0,3306095
1742405688,1742405906,218,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1361 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1361,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6116760124610592,202.65376806259155,0,None,i7138,202.65376806259155,1052.31640625,861.1255095108696,-1,0,3305970
1742405690,1742405906,216,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 183 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,183,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6146168224299066,203.54816699028015,0,None,i7069,203.54816699028015,1071.8515625,874.3624320652174,-1,0,3306058
1742405748,1742407491,1743,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3345 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3345,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6057445482866044,1577.145005941391,0,None,i7048,1577.145005941391,1001.125,821.2013916015625,-1,0,3306085
1742408787,1742408894,107,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 367 n_samples 261 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",367,261,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6181557632398754,94.812087059021,0,None,i7128,94.812087059021,879.0703125,775.5930989583334,-1,0,3317525
1742408817,1742408894,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 364 n_samples 538 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",364,538,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.568423676012461,64.78104400634766,0,None,i7158,64.78104400634766,818.6328125,747.7543402777778,-1,0,3317555
1742408787,1742408913,126,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3573 n_samples 745 threshold 0.20811917400289276 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3573,745,0.20811917400289276,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6099065420560748,118.17392373085022,0,None,i7132,118.17392373085022,957.34375,816.8950892857143,-1,0,3317515
1742408817,1742408914,97,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1209 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1209,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.598791277258567,86.48883938789368,0,None,i7143,86.48883938789368,889.61328125,782.7762784090909,-1,0,3317582
1742408793,1742408945,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2809 n_samples 315 threshold 0.37981825796804297 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2809,315,0.37981825796804297,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.61201246105919,139.07564163208008,0,None,i7152,139.07564163208008,978.47265625,829.533935546875,-1,0,3317534
1742408814,1742408945,131,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 443 n_samples 201 threshold 0.4765523284994592 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",443,201,0.4765523284994592,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6077133956386293,120.57209968566895,0,None,i7183,120.57209968566895,938.0390625,813.2080729166667,-1,0,3317537
1742408794,1742408945,151,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1793 n_samples 724 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1793,724,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6118006230529595,138.59307551383972,0,None,i7186,138.59307551383972,975.7265625,828.325439453125,-1,0,3317528
1742408787,1742408946,159,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 629 threshold 0.5401733413441312 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,629,0.5401733413441312,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6100934579439252,146.75420212745667,0,None,i7129,146.75420212745667,992.55078125,839.3430606617648,-1,0,3317522
1742408848,1742408951,103,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 762 n_samples 607 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",762,607,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5980186915887851,89.84965872764587,0,None,i7150,89.84965872764587,898.01171875,784.3352272727273,-1,0,3317628
1742408787,1742408951,164,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1669 n_samples 633 threshold 0.27291233481534083 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1669,633,0.27291233481534083,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6047476635514019,154.26015710830688,0,None,i7138,154.26015710830688,992.9921875,839.9539930555555,-1,0,3317509
1742408794,1742408954,160,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 592 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,592,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.595588785046729,143.6770315170288,0,None,i7169,143.6770315170288,879.953125,788.9441636029412,-1,0,3317531
1742408848,1742408962,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 735 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,735,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5818691588785047,102.79463601112366,0,None,i7146,102.79463601112366,864.80078125,774.0558894230769,-1,0,3317633
1742408817,1742408971,154,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3503 n_samples 673 threshold 0.41034544666363637 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3503,673,0.41034544666363637,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6078629283489096,140.09442400932312,0,None,i7151,140.09442400932312,951.94140625,820.072998046875,-1,0,3317563
1742408817,1742408976,159,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3828 n_samples 686 threshold 0.2096894467009888 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3828,686,0.2096894467009888,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6109657320872274,144.86611700057983,0,None,i7164,144.86611700057983,1006.0546875,842.8471966911765,-1,0,3317549
1742408848,1742408981,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3528 n_samples 821 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3528,821,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6089595015576323,122.70974159240723,0,None,i7146,122.70974159240723,935.6796875,813.62890625,-1,0,3317638
1742408878,1742408993,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2897 n_samples 640 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2897,640,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5935451713395639,102.09816884994507,0,None,i7146,102.09816884994507,915.40625,797.7515024038462,-1,0,3317691
1742408879,1742409020,141,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3795 n_samples 775 threshold 0.43265160186022694 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3795,775,0.43265160186022694,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6075514018691589,126.689688205719,0,None,i7153,126.689688205719,953.87890625,821.5450520833333,-1,0,3317684
1742408848,1742409020,172,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1386 n_samples 303 threshold 0.6110728729045154 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1386,303,0.6110728729045154,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6090591900311526,161.629328250885,0,None,i7142,161.629328250885,1021.7734375,851.8704769736842,-1,0,3317645
1742408878,1742409044,166,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3836 n_samples 364 threshold 0.7387576666338951 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3836,364,0.7387576666338951,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6049844236760125,155.9134590625763,0,None,i7158,155.9134590625763,985.94140625,835.7994791666666,-1,0,3317677
1742408818,1742409048,230,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 366 n_samples 100 threshold 0.3655963730643889 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",366,100,0.3655963730643889,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6257445482866043,215.4518096446991,0,None,i7139,215.4518096446991,1095.73046875,882.2431640625,-1,0,3317588
1742408817,1742409060,243,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1646 n_samples 362 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1646,362,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6144423676012462,227.86958265304565,0,None,i7145,227.86958265304565,1151.40234375,911.96890625,-1,0,3317576
1742408786,1742409065,279,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1660 n_samples 293 threshold 0.271775129558476 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1660,293,0.271775129558476,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6143302180685358,264.5242049694061,0,None,i7131,264.5242049694061,1195.90625,929.7446120689655,-1,0,3317519
1742408854,1742409073,219,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1619 n_samples 532 threshold 0.3093038037887295 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1619,532,0.3093038037887295,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6162242990654205,204.46100974082947,0,None,i7175,204.46100974082947,1038.43359375,859.6759510869565,-1,0,3317656
1742408814,1742409078,264,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 332 threshold 0.4902224508175335 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,332,0.4902224508175335,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6209595015576324,250.67734479904175,0,None,i7182,250.67734479904175,1118.0234375,903.837890625,-1,0,3317539
1742408879,1742409084,205,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1593 n_samples 697 threshold 0.3459384639604962 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1593,697,0.3459384639604962,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6097943925233645,190.7131862640381,0,None,i7161,190.7131862640381,959.50390625,830.4937855113636,-1,0,3317674
1742408850,1742409094,244,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2992 n_samples 289 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2992,289,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6117757009345794,227.35949683189392,0,None,i7136,227.35949683189392,1078.26953125,864.8553125,-1,0,3317651
1742408875,1742409094,219,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1777 n_samples 582 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1777,582,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6076760124610592,202.74504327774048,0,None,i7168,202.74504327774048,1040.7265625,862.3048573369565,-1,0,3317666
1742408818,1742409094,276,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1578 n_samples 269 threshold 0.44958466730534313 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1578,269,0.44958466730534313,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6138566978193146,263.18821835517883,0,None,i7149,263.18821835517883,1181.28515625,929.19921875,-1,0,3317570
1742408879,1742409108,229,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3950 n_samples 553 threshold 0.4371394451761442 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3950,553,0.4371394451761442,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6200996884735203,217.05095791816711,0,None,i7183,217.05095791816711,1037.796875,858.3445638020834,-1,0,3317669
1742408856,1742409228,372,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 316 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,316,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6151651090342679,357.87527203559875,0,None,i7163,357.87527203559875,1253.20703125,963.6371299342105,-1,0,3317661
1742410541,1742410599,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 249 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",249,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.500398753894081,42.136502504348755,0,None,i7130,42.136502504348755,731.17578125,705.6997767857143,-1,0,3321791
1742410537,1742410613,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2372 n_samples 477 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2372,477,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5255576323987539,62.61939978599548,0,None,i7182,62.61939978599548,770.5,723.6124131944445,-1,0,3321763
1742410557,1742410621,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2176 n_samples 434 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2176,434,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5556386292834891,55.237645864486694,0,None,i7182,55.237645864486694,769.06640625,722.4169921875,-1,0,3321795
1742410572,1742410624,52,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 249 n_samples 907 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",249,907,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4842990654205607,40.30216860771179,0,None,i7105,40.30216860771179,730.98828125,707.0630580357143,-1,0,3321838
1742410556,1742410626,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2273 n_samples 425 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2273,425,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4935327102803738,61.155821084976196,0,None,i7153,61.155821084976196,747.6484375,712.7482638888889,-1,0,3321800
1742410572,1742410629,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 98 n_samples 269 threshold 0.6876796831838226 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",98,269,0.6876796831838226,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5014454828660436,43.88263010978699,0,None,i7133,43.88263010978699,727.40234375,702.1127232142857,-1,0,3321825
1742410541,1742410630,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2415 n_samples 502 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2415,502,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5901806853582554,79.39085912704468,0,None,i7158,79.39085912704468,839.953125,756.53046875,-1,0,3321769
1742410541,1742410631,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2430 n_samples 203 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2430,203,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5603738317757009,77.17875218391418,0,None,i7138,77.17875218391418,831.578125,752.709375,-1,0,3321782
1742410572,1742410642,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2476 n_samples 861 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2476,861,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5445856697819315,59.01338338851929,0,None,i7150,59.01338338851929,797.08984375,731.02734375,-1,0,3321812
1742410572,1742410643,71,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 507 n_samples 962 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",507,962,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.45125233644859813,55.949326038360596,0,None,i7145,55.949326038360596,720.8125,703.07666015625,-1,0,3321816
1742410577,1742410654,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2148 n_samples 873 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2148,873,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.46553271028037385,45.031818866729736,0,None,i7124,45.031818866729736,712.8359375,695.4659598214286,-1,0,3321829
1742410603,1742410660,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2117 n_samples 390 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2117,390,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5298566978193147,43.772249937057495,0,None,i7122,43.772249937057495,746.14453125,711.0033482142857,-1,0,3321888
1742410541,1742410662,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3407 n_samples 394 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3407,394,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5994143302180686,110.29272508621216,0,None,i7151,110.29272508621216,913.671875,799.0178571428571,-1,0,3321776
1742410572,1742410674,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2507 n_samples 517 threshold 0.34130281007111574 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2507,517,0.34130281007111574,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5962492211838006,78.13166379928589,0,None,i7101,78.13166379928589,846.55078125,751.33046875,-1,0,3321841
1742410603,1742410680,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2415 n_samples 503 threshold 0.5719639259157455 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2415,503,0.5719639259157455,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5734953271028037,65.62519836425781,0,None,i7152,65.62519836425781,809.640625,741.69140625,-1,0,3321867
1742410544,1742410685,141,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2897 n_samples 352 threshold 0.6623475410057413 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2897,352,0.6623475410057413,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5882866043613707,128.42542433738708,0,None,i7136,128.42542433738708,894.9375,779.6580729166667,-1,0,3321785
1742410541,1742410687,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1996 n_samples 888 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1996,888,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6097445482866044,136.5036277770996,0,None,i7153,136.5036277770996,968.37109375,825.4052734375,-1,0,3321772
1742410604,1742410694,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2952 n_samples 721 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2952,721,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5459439252336449,75.62235283851624,0,None,i7144,75.62235283851624,808.76171875,748.0765625,-1,0,3321872
1742410573,1742410694,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1059 n_samples 938 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1059,938,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.493607476635514,76.1049256324768,0,None,i7121,76.1049256324768,683.59375,640.75078125,-1,0,3321834
1742410603,1742410698,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2567 n_samples 517 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2567,517,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6058940809968847,82.89619326591492,0,None,i7110,82.89619326591492,886.08984375,783.8032670454545,-1,0,3321892
1742410610,1742410707,97,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2329 n_samples 145 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2329,145,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5677133956386293,85.44958329200745,0,None,i7137,85.44958329200745,823.7421875,742.8039772727273,-1,0,3321879
1742410572,1742410718,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1135 n_samples 595 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1135,595,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6121495327102804,133.82265210151672,0,None,i7158,133.82265210151672,943.30078125,814.765625,-1,0,3321804
1742410576,1742410722,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1953 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1953,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6061931464174455,135.24297499656677,0,None,i7182,135.24297499656677,951.05859375,818.70361328125,-1,0,3321849
1742410609,1742410724,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2901 n_samples 337 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2901,337,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5788909657320872,102.8108766078949,0,None,i7124,102.8108766078949,850.12890625,760.1781850961538,-1,0,3321884
1742410572,1742410744,172,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3188 n_samples 401 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3188,401,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6214205607476635,160.94172263145447,0,None,i7152,160.94172263145447,1030.33203125,857.7485608552631,-1,0,3321807
1742410537,1742410747,210,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3356 n_samples 390 threshold 0.49835049437258777 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3356,390,0.49835049437258777,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6113520249221184,198.28131794929504,0,None,i7161,198.28131794929504,1013.2890625,844.2917258522727,-1,0,3321766
1742410604,1742410776,172,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1410 n_samples 609 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1410,609,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6182305295950156,155.88368391990662,0,None,i7186,155.88368391990662,969.390625,829.5642361111111,-1,0,3321861
1742410603,1742410787,184,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3280 n_samples 379 threshold 0.4119075276536257 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3280,379,0.4119075276536257,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6077507788161993,173.94531846046448,0,None,i7097,173.94531846046448,1039.171875,859.28203125,-1,0,3321897
1742410574,1742410832,258,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3374 n_samples 393 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3374,393,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6206604361370717,242.22675108909607,0,None,i7141,242.22675108909607,1059.4296875,874.5049189814815,-1,0,3321820
1742410596,1742410900,304,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1995 n_samples 391 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1995,391,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6111028037383177,294.383882522583,0,None,i7164,294.383882522583,1186,935.258056640625,-1,0,3321856
1742412863,1742412915,52,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 31 n_samples 193 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",31,193,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5340685358255451,37.39049315452576,0,None,i7153,37.39049315452576,721.0703125,699.6067708333334,-1,0,3326485
1742412863,1742412928,65,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 354 n_samples 600 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",354,600,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4670529595015576,50.87905144691467,0,None,i7131,50.87905144691467,735.44921875,710.44189453125,-1,0,3326504
1742412863,1742412934,71,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 215 n_samples 385 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",215,385,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5804984423676013,58.81098651885986,0,None,i7145,58.81098651885986,794.08984375,731.17138671875,-1,0,3326495
1742412868,1742412939,71,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 271 n_samples 538 threshold 0.7607549334447525 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",271,538,0.7607549334447525,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4976323987538941,55.35750722885132,0,None,i7163,55.35750722885132,699.68359375,644.994140625,-1,0,3326481
1742412878,1742412942,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 301 n_samples 833 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",301,833,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.41258566978193145,50.60176873207092,0,None,i7164,50.60176873207092,697.23828125,691.79296875,-1,0,3326515
1742412894,1742412958,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2321 n_samples 752 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2321,752,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5250965732087227,52.030102252960205,0,None,i7158,52.030102252960205,775.328125,727.130859375,-1,0,3326540
1742412864,1742412959,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3063 n_samples 608 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3063,608,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5465545171339564,81.88314247131348,0,None,i7186,81.88314247131348,816.28515625,752.58203125,-1,0,3326476
1742412896,1742412961,65,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 72 n_samples 340 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",72,340,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.523601246105919,48.50012683868408,0,None,i7141,48.50012683868408,728.65625,701.9358258928571,-1,0,3326559
1742412894,1742412971,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2750 n_samples 822 threshold 0.7904582623271478 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2750,822,0.7904582623271478,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.525208722741433,64.01107335090637,0,None,i7159,64.01107335090637,781.18359375,733.2669270833334,-1,0,3326537
1742412877,1742412973,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2825 n_samples 812 threshold 0.40355247969916413 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2825,812,0.40355247969916413,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5980436137071651,85.0545597076416,0,None,i7182,85.0545597076416,868.64453125,772.6345880681819,-1,0,3326512
1742412896,1742412973,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 124 n_samples 566 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",124,566,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5009470404984424,42.235239028930664,0,None,i7163,42.235239028930664,698.1171875,656.41015625,-1,0,3326532
1742412894,1742412977,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 221 n_samples 211 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",221,211,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5357507788161994,68.16855883598328,0,None,i7175,68.16855883598328,772.41796875,720.8359375,-1,0,3326524
1742412894,1742412983,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 894 n_samples 531 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",894,531,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.550803738317757,78.56658053398132,0,None,i7154,78.56658053398132,829.0546875,748.73046875,-1,0,3326544
1742412877,1742412986,109,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1047 n_samples 890 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1047,890,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5966230529595016,94.95592308044434,0,None,i7186,94.95592308044434,893.359375,784.11328125,-1,0,3326509
1742412917,1742412987,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2455 n_samples 919 threshold 0.47062549184015123 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2455,919,0.47062549184015123,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.534380062305296,56.79559397697449,0,None,i7132,56.79559397697449,779.23828125,726.2333984375,-1,0,3326606
1742412865,1742412987,122,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1269 n_samples 887 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1269,887,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5947663551401869,110.41148567199707,0,None,i7124,110.41148567199707,916.828125,802.9227120535714,-1,0,3326507
1742412918,1742412988,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2332 n_samples 921 threshold 0.7443979741415468 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2332,921,0.7443979741415468,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.46063551401869157,59.019384145736694,0,None,i7183,59.019384145736694,727.0546875,701.41748046875,-1,0,3326569
1742412918,1742413001,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3667 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3667,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5027414330218068,72.96294784545898,0,None,i7175,72.96294784545898,775.30078125,731.53828125,-1,0,3326574
1742412863,1742413003,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 611 n_samples 333 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",611,333,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6157009345794393,128.2008934020996,0,None,i7139,128.2008934020996,943.83984375,812.628125,-1,0,3326500
1742412917,1742413006,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3467 n_samples 895 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3467,895,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5308909657320873,77.70880889892578,0,None,i7150,77.70880889892578,802.0625,740.2265625,-1,0,3326588
1742412894,1742413021,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3149 n_samples 715 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3149,715,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5960623052959502,115.28433084487915,0,None,i7182,115.28433084487915,928.6328125,802.1607142857143,-1,0,3326520
1742412897,1742413024,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2944 n_samples 548 threshold 0.4323195869797412 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2944,548,0.4323195869797412,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6069906542056075,116.19378614425659,0,None,i7139,116.19378614425659,910.90234375,795.4015066964286,-1,0,3326564
1742412894,1742413027,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 781 n_samples 418 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",781,418,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6103177570093458,120.03496670722961,0,None,i7151,120.03496670722961,946.234375,808.7600446428571,-1,0,3326548
1742412917,1742413032,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3263 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3263,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5913021806853582,103.42961955070496,0,None,i7139,103.42961955070496,899.10546875,788.8777043269231,-1,0,3326600
1742412917,1742413039,122,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2991 n_samples 760 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2991,760,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5974828660436137,109.61127471923828,0,None,i7154,109.61127471923828,901.53125,786.7734375,-1,0,3326583
1742412895,1742413065,170,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3262 n_samples 655 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3262,655,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5784797507788162,153.580335855484,0,None,i7169,153.580335855484,833.640625,762.4281684027778,-1,0,3326528
1742412917,1742413069,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 813 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,813,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6076635514018691,141.37961649894714,0,None,i7143,141.37961649894714,990.390625,836.6803768382352,-1,0,3326594
1742412863,1742413079,216,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2467 n_samples 100 threshold 0.4056278681846749 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2467,100,0.4056278681846749,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6169221183800623,204.74463844299316,0,None,i7145,204.74463844299316,1106.78515625,885.9512567934783,-1,0,3326493
1742412895,1742413093,198,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 991 n_samples 477 threshold 0.23171180113051149 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",991,477,0.23171180113051149,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6121370716510903,183.82734298706055,0,None,i7144,183.82734298706055,953.44140625,818.6752232142857,-1,0,3326554
1742412863,1742413684,821,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1906 n_samples 100 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1906,100,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.595626168224299,807.9700162410736,0,None,i7151,807.9700162410736,1328.859375,1059.2596479668675,-1,0,3326489
1742416519,1742416558,39,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2003 n_samples 796 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2003,796,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3070031152647975,26.873469829559326,0,None,i7131,26.873469829559326,688.3515625,688.0953125,-1,0,3331848
1742416499,1742416569,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 491 n_samples 984 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",491,984,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5339190031152647,60.84782552719116,0,None,i7133,60.84782552719116,784.58203125,729.9748263888889,-1,0,3331830
1742416519,1742416570,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 100 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,100,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5481495327102803,37.19655728340149,0,None,i7132,37.19655728340149,712.328125,695.296875,-1,0,3331842
1742416519,1742416577,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 222 n_samples 771 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",222,771,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5094579439252337,45.17265200614929,0,None,i7182,45.17265200614929,741.19140625,708.3370535714286,-1,0,3331833
1742416521,1742416586,65,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 206 n_samples 477 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",206,477,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5484361370716511,50.4004864692688,0,None,i7129,50.4004864692688,773.24609375,728.90380859375,-1,0,3331854
1742416519,1742416595,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 506 n_samples 829 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",506,829,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5554267912772586,67.38053250312805,0,None,i7128,67.38053250312805,808.20703125,738.0164930555555,-1,0,3331857
1742416541,1742416599,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 267 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,267,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.43998753894080994,41.94858908653259,0,None,i7161,41.94858908653259,693.1484375,689.8699776785714,-1,0,3331871
1742416499,1742416602,103,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2321 n_samples 303 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2321,303,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.532809968847352,72.89151000976562,0,None,i7135,72.89151000976562,775.890625,726.630859375,-1,0,3331826
1742416542,1742416607,65,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 85 n_samples 924 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",85,924,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.42336448598130844,49.891833543777466,0,None,i7137,49.891833543777466,684.91796875,677.0747767857143,-1,0,3331874
1742416556,1742416607,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 105 n_samples 807 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",105,807,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.44714018691588786,36.01486134529114,0,None,i7130,36.01486134529114,707.1953125,693.271484375,-1,0,3331891
1742416556,1742416608,52,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 184 threshold 0.5656602017486396 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,184,0.5656602017486396,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4430903426791277,40.022737979888916,0,None,i7183,40.022737979888916,687.5546875,686.267578125,-1,0,3331877
1742416526,1742416609,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2426 n_samples 276 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2426,276,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5406355140186916,69.80385112762451,0,None,i7175,69.80385112762451,819.40234375,743.6788194444445,-1,0,3331863
1742416556,1742416613,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 331 n_samples 943 threshold 0.4578832640285351 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",331,943,0.4578832640285351,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5069408099688474,48.91570425033569,0,None,i7152,48.91570425033569,748.44140625,709.30078125,-1,0,3331881
1742416559,1742416616,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2267 n_samples 638 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2267,638,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.44961993769470404,48.82442665100098,0,None,i7130,48.82442665100098,724.0546875,700.2527901785714,-1,0,3331906
1742416556,1742416620,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2249 n_samples 316 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2249,316,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5169345794392524,54.352720499038696,0,None,i7122,54.352720499038696,758.26171875,718.2890625,-1,0,3331896
1742416519,1742416627,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2450 n_samples 395 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2450,395,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6042492211838006,97.35981822013855,0,None,i7145,97.35981822013855,887.421875,781.2610677083334,-1,0,3331836
1742416519,1742416627,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 289 n_samples 240 threshold 0.35134588686983026 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",289,240,0.35134588686983026,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6190280373831776,93.34861922264099,0,None,i7130,93.34861922264099,871.63671875,769.5384114583334,-1,0,3331851
1742416526,1742416628,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1297 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1297,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.477993769470405,87.20502734184265,0,None,i7139,87.20502734184265,762.98828125,724.2386363636364,-1,0,3331868
1742416556,1742416632,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 628 n_samples 914 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",628,914,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5599750778816199,62.362980365753174,0,None,i7132,62.362980365753174,816.44921875,747.5789930555555,-1,0,3331887
1742416556,1742416633,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2447 n_samples 785 threshold 0.74474418999717 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2447,785,0.74474418999717,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4997507788161994,62.85635042190552,0,None,i7138,62.85635042190552,767.71875,722.4114583333334,-1,0,3331883
1742416557,1742416653,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 646 n_samples 490 threshold 0.7754790240001461 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",646,490,0.7754790240001461,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5638130841121496,80.9204740524292,0,None,i7183,80.9204740524292,827.62109375,753.9350142045455,-1,0,3331878
1742416541,1742416656,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1240 n_samples 621 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1240,621,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5677881619937695,98.62623143196106,0,None,i7136,98.62623143196106,815.75,742.2643229166666,-1,0,3331876
1742416519,1742416664,145,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1853 n_samples 846 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1853,846,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6084610591900311,132.78451299667358,0,None,i7131,132.78451299667358,965.5234375,824.67041015625,-1,0,3331845
1742416520,1742416665,145,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1591 n_samples 304 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1591,304,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6014828660436137,135.29785704612732,0,None,i7132,135.29785704612732,944.65234375,811.467041015625,-1,0,3331839
1742416520,1742416666,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1714 n_samples 324 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1714,324,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6035763239875389,135.89879250526428,0,None,i7127,135.89879250526428,945.43359375,817.676513671875,-1,0,3331860
1742416556,1742416671,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 746 n_samples 175 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",746,175,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.590803738317757,104.50660562515259,0,None,i7182,104.50660562515259,905.671875,794.19921875,-1,0,3331879
1742416539,1742416674,135,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 652 n_samples 255 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",652,255,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5661059190031152,119.46080660820007,0,None,i7140,119.46080660820007,861.08984375,764.7653459821429,-1,0,3331873
1742416525,1742416678,153,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 299 n_samples 147 threshold 0.34956198527422405 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",299,147,0.34956198527422405,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.626417445482866,139.1785924434662,0,None,i7153,139.1785924434662,960.1328125,818.09130859375,-1,0,3331865
1742416556,1742416696,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1507 n_samples 283 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1507,283,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6135451713395639,128.45121383666992,0,None,i7119,128.45121383666992,943.08984375,806.9895833333334,-1,0,3331901
1742416547,1742416778,231,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 750 n_samples 283 threshold 0.45184408744762594 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",750,283,0.45184408744762594,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6134080996884735,213.3069932460785,0,None,i7137,213.3069932460785,949.05078125,815.13427734375,-1,0,3331875
1742419587,1742419658,71,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2297 n_samples 534 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2297,534,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5536199376947041,56.92416071891785,0,None,i7139,56.92416071891785,795.69921875,731.8798828125,-1,0,3335554
1742419617,1742419675,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2187 n_samples 756 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2187,756,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49389408099688475,44.247889280319214,0,None,i7153,44.247889280319214,732.40625,705.5368303571429,-1,0,3335571
1742419600,1742419683,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 412 n_samples 633 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",412,633,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5731464174454829,70.03499937057495,0,None,i7183,70.03499937057495,813.359375,740.4231770833334,-1,0,3335556
1742419618,1742419688,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 252 n_samples 649 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",252,649,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5342803738317757,57.99640917778015,0,None,i7151,57.99640917778015,761.15234375,718.4150390625,-1,0,3335577
1742419617,1742419694,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2337 n_samples 472 threshold 0.3515001036680243 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2337,472,0.3515001036680243,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5838629283489096,66.32607960700989,0,None,i7182,66.32607960700989,822.3046875,747.0724826388889,-1,0,3335562
1742419618,1742419694,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2627 n_samples 940 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2627,940,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5676884735202492,68.28580808639526,0,None,i7158,68.28580808639526,816.234375,743.5928819444445,-1,0,3335569
1742419600,1742419696,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 349 n_samples 406 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",349,406,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6026292834890966,81.87001776695251,0,None,i7163,81.87001776695251,851.88671875,766.7748579545455,-1,0,3335558
1742419648,1742419705,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2210 n_samples 665 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2210,665,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5168971962616823,45.930060148239136,0,None,i7150,45.930060148239136,749.93359375,711.94921875,-1,0,3335603
1742419647,1742419711,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 236 n_samples 426 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",236,426,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4916635514018692,54.07003855705261,0,None,i7153,54.07003855705261,738.90625,707.97265625,-1,0,3335594
1742419617,1742419713,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1842 n_samples 815 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1842,815,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5617694704049844,85.97838735580444,0,None,i7152,85.97838735580444,812.41796875,743.9364346590909,-1,0,3335575
1742419620,1742419715,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 321 n_samples 314 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",321,314,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6220685358255452,82.03948187828064,0,None,i7182,82.03948187828064,874.87109375,778.7638494318181,-1,0,3335579
1742419647,1742419717,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 253 n_samples 501 threshold 0.4434391470640317 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",253,501,0.4434391470640317,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5462180685358256,57.646132707595825,0,None,i7153,57.646132707595825,775.8359375,721.4921875,-1,0,3335596
1742419617,1742419719,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1706 n_samples 760 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1706,760,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5567227414330218,90.64664697647095,0,None,i7152,90.64664697647095,801.09765625,749.5725911458334,-1,0,3335573
1742419640,1742419729,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 792 n_samples 905 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",792,905,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.48557009345794394,74.2254810333252,0,None,i7164,74.2254810333252,760.484375,719.598046875,-1,0,3335581
1742419647,1742419730,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2789 n_samples 887 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2789,887,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.583588785046729,74.62801313400269,0,None,i7182,74.62801313400269,856.0703125,766.626953125,-1,0,3335587
1742419648,1742419731,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2463 n_samples 937 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2463,937,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5318130841121496,70.6587266921997,0,None,i7154,70.6587266921997,780.95703125,731.904296875,-1,0,3335592
1742419618,1742419733,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1602 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1602,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6054330218068535,104.20446372032166,0,None,i7175,104.20446372032166,922.5390625,801.9756610576923,-1,0,3335564
1742419618,1742419739,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3458 n_samples 952 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3458,952,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.596,107.69114446640015,0,None,i7164,107.69114446640015,923.234375,797.7518028846154,-1,0,3335566
1742419647,1742419742,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2816 n_samples 626 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2816,626,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5310280373831776,83.20011496543884,0,None,i7151,83.20011496543884,786.671875,734.7421875,-1,0,3335598
1742419660,1742419749,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 227 n_samples 303 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",227,303,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5965358255451714,78.43235516548157,0,None,i7146,78.43235516548157,834.375,752.327734375,-1,0,3335610
1742419660,1742419749,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3139 n_samples 848 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3139,848,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5262429906542057,77.63621735572815,0,None,i7146,77.63621735572815,776.0703125,725.919921875,-1,0,3335612
1742419678,1742419755,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2283 n_samples 542 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2283,542,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4481121495327103,65.01980209350586,0,None,i7162,65.01980209350586,720.08203125,703.0217013888889,-1,0,3335616
1742419647,1742419755,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1879 n_samples 931 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1879,931,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5637507788161994,96.39046096801758,0,None,i7158,96.39046096801758,841.73046875,757.21484375,-1,0,3335590
1742419660,1742419761,101,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1790 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1790,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49603738317757007,86.1722412109375,0,None,i7146,86.1722412109375,783.5234375,733.7535511363636,-1,0,3335608
1742419617,1742419764,147,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2710 n_samples 323 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2710,323,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6139065420560748,136.83380246162415,0,None,i7152,136.83380246162415,966.85546875,820.167236328125,-1,0,3335574
1742419639,1742419768,129,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1158 n_samples 229 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1158,229,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5892461059190031,118.20907759666443,0,None,i7159,118.20907759666443,935.95703125,805.5401785714286,-1,0,3335585
1742419647,1742419787,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1118 n_samples 696 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1118,696,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5974454828660436,129.452486038208,0,None,i7151,129.452486038208,924.45703125,791.9674479166666,-1,0,3335600
1742419679,1742419794,115,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3848 n_samples 846 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3848,846,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5469657320872274,99.20550680160522,0,None,i7175,99.20550680160522,809.92578125,745.7503255208334,-1,0,3335614
1742419659,1742419800,141,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3692 n_samples 324 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3692,324,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6052710280373832,125.85743761062622,0,None,i7150,125.85743761062622,939.8984375,811.1591145833333,-1,0,3335606
1742419647,1742420015,368,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 322 n_samples 153 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",322,153,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6246853582554517,323.02313137054443,0,None,i7160,323.02313137054443,968.0625,825.4286830357142,-1,0,3335560
1742424278,1742424324,46,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 100 threshold 0.45925874484278784 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,100,0.45925874484278784,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5073271028037383,35.96096682548523,0,None,i7180,35.96096682548523,703.515625,692.2962239583334,-1,0,3344275
1742424277,1742424336,59,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 150 n_samples 347 threshold 0.4466948104153626 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",150,347,0.4466948104153626,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5475887850467289,48.43456554412842,0,None,i7142,48.43456554412842,749.33984375,703.1763392857143,-1,0,3344293
1742424277,1742424355,78,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 262 n_samples 315 threshold 0.43123503650485095 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",262,315,0.43123503650485095,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5934080996884735,66.08880949020386,0,None,i7152,66.08880949020386,800.61328125,736.5160590277778,-1,0,3344286
1742424278,1742424356,78,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 340 n_samples 850 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",340,850,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5199376947040498,62.33650517463684,0,None,i7144,62.33650517463684,656.24609375,620.8901909722222,-1,0,3344291
1742424280,1742424358,78,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 210 n_samples 297 threshold 0.5182325139528104 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",210,297,0.5182325139528104,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5699190031152648,63.32598614692688,0,None,i7134,63.32598614692688,805.078125,740.3363715277778,-1,0,3344298
1742424280,1742424364,84,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2209 n_samples 734 threshold 0.4885945156223471 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2209,734,0.4885945156223471,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.48082242990654206,54.164271116256714,0,None,i7136,54.164271116256714,722.9140625,693.71630859375,-1,0,3344296
1742424278,1742424368,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3069 n_samples 912 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3069,912,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5053707165109034,76.63327026367188,0,None,i7164,76.63327026367188,786.37890625,731.4140625,-1,0,3344280
1742424262,1742424370,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 359 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",359,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5773208722741433,95.3341224193573,0,None,i7183,95.3341224193573,855.05078125,768.6591796875,-1,0,3344270
1742424282,1742424374,92,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 522 n_samples 1000 threshold 0.48596555987866086 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",522,1000,0.48596555987866086,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5407476635514019,57.32434153556824,0,None,i7133,57.32434153556824,779.1796875,722.4365234375,-1,0,3344300
1742424278,1742424375,97,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 450 n_samples 385 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",450,385,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6140436137071651,86.99823379516602,0,None,i7150,86.99823379516602,886.84375,781.7492897727273,-1,0,3344288
1742424279,1742424375,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 179 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",179,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5614330218068536,82.4406168460846,0,None,i7162,82.4406168460846,789.8203125,735.0102982954545,-1,0,3344283
1742424284,1742424387,103,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 323 n_samples 328 threshold 0.3636745206607061 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",323,328,0.3636745206607061,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6134953271028037,89.74865055084229,0,None,i7186,89.74865055084229,859.01171875,764.8842329545455,-1,0,3344301
1742424308,1742424390,82,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 272 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",272,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5585794392523364,67.45687556266785,0,None,i7180,67.45687556266785,828.640625,747.9431423611111,-1,0,3344314
1742424322,1742424397,75,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 299 n_samples 311 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",299,311,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5246105919003116,63.864136695861816,0,None,i7186,63.864136695861816,771.62109375,722.6545138888889,-1,0,3344320
1742424303,1742424400,97,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 631 n_samples 585 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",631,585,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5168598130841121,80.74433422088623,0,None,i7185,80.74433422088623,771.734375,722.4431818181819,-1,0,3344305
1742424307,1742424403,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2405 n_samples 432 threshold 0.3325026198021909 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2405,432,0.3325026198021909,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5922118380062306,84.57048988342285,0,None,i7171,84.57048988342285,864.3828125,770.3096590909091,-1,0,3344318
1742424302,1742424404,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 457 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",457,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5800747663551402,91.9962465763092,0,None,i7180,91.9962465763092,878.421875,780.705078125,-1,0,3344309
1742424337,1742424426,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1032 n_samples 679 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1032,679,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5331838006230529,78.73308229446411,0,None,i7180,78.73308229446411,829.34375,747.278515625,-1,0,3344332
1742424307,1742424428,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1762 n_samples 666 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1762,666,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5762118380062305,107.5141372680664,0,None,i7171,107.5141372680664,886.390625,779.2749399038462,-1,0,3344317
1742424308,1742424428,120,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 631 n_samples 707 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",631,707,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5895451713395639,107.07776665687561,0,None,i7183,107.07776665687561,856.73828125,765.7662259615385,-1,0,3344311
1742424303,1742424430,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 416 n_samples 293 threshold 0.34100295619744525 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",416,293,0.34100295619744525,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6022928348909657,113.68728017807007,0,None,i7186,113.68728017807007,915.23828125,794.40234375,-1,0,3344304
1742424277,1742424431,154,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2547 n_samples 212 threshold 0.4563077728652028 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2547,212,0.4563077728652028,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6133333333333333,139.66034364700317,0,None,i7171,139.66034364700317,960.0078125,820.361083984375,-1,0,3344278
1742424312,1742424435,123,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2424 n_samples 800 threshold 0.3649819043217139 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2424,800,0.3649819043217139,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5367476635514019,106.54254722595215,0,None,i7168,106.54254722595215,784.26953125,732.5378605769231,-1,0,3344319
1742424322,1742424441,119,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2497 n_samples 184 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2497,184,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.576398753894081,107.00979948043823,0,None,i7183,107.00979948043823,862.6796875,764.7998798076923,-1,0,3344322
1742424309,1742424451,142,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2552 n_samples 325 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2552,325,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6122367601246106,126.18819427490234,0,None,i7176,126.18819427490234,935.3359375,805.99375,-1,0,3344316
1742424302,1742424460,158,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3075 n_samples 606 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3075,606,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6005607476635514,147.19872426986694,0,None,i7183,147.19872426986694,940.41796875,806.5769761029412,-1,0,3344307
1742424308,1742424485,177,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 380 n_samples 100 threshold 0.500212255777559 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",380,100,0.500212255777559,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6001370716510903,163.00802159309387,0,None,i7183,163.00802159309387,950.3515625,814.5559210526316,-1,0,3344313
1742424338,1742424535,197,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3637 n_samples 636 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3637,636,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6092585669781931,186.14178109169006,0,None,i7183,186.14178109169006,984.11328125,826.7790178571429,-1,0,3344330
1742424279,1742424536,257,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2585 n_samples 171 threshold 0.3295883596918423 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2585,171,0.3295883596918423,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6210965732087227,237.2880094051361,0,None,i7140,237.2880094051361,1023.33203125,855.3001802884615,-1,0,3344294
1742424324,1742424541,217,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3089 n_samples 361 threshold 0.45969183185206264 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3089,361,0.45969183185206264,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6115763239875389,200.36752128601074,0,None,i7168,200.36752128601074,985.6328125,840.8109714673913,-1,0,3344324
1742430310,1742430374,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 242 n_samples 709 threshold 0.5449726138262126 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",242,709,0.5449726138262126,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.48892211838006233,53.61358880996704,0,None,i7161,53.61358880996704,741.5078125,709.0810546875,-1,0,3349206
1742430310,1742430393,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2384 n_samples 570 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2384,570,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.572834890965732,71.59664463996887,0,None,i7161,71.59664463996887,815.5703125,748.04140625,-1,0,3349208
1742430323,1742430399,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 896 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",896,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.46578193146417446,64.0082540512085,0,None,i7158,64.0082540512085,740.87890625,713.21875,-1,0,3349222
1742430323,1742430400,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 261 n_samples 577 threshold 0.44461848890175754 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",261,577,0.44461848890175754,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5437258566978194,63.95604681968689,0,None,i7183,63.95604681968689,765.3515625,721.0711805555555,-1,0,3349214
1742430340,1742430404,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 197 n_samples 596 threshold 0.4623201156330807 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",197,596,0.4623201156330807,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5044859813084113,51.241711139678955,0,None,i7161,51.241711139678955,742.5546875,710.76025390625,-1,0,3349241
1742430363,1742430414,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 60 n_samples 482 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",60,482,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4733707165109034,38.87044835090637,0,None,i7155,38.87044835090637,709.609375,693.849609375,-1,0,3349256
1742430363,1742430414,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 157 threshold 0.4239781393727134 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,157,0.4239781393727134,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49090342679127724,36.15434956550598,0,None,i7155,36.15434956550598,697.7109375,689.6048177083334,-1,0,3349258
1742430339,1742430416,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2467 n_samples 578 threshold 0.31123529474235023 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2467,578,0.31123529474235023,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5833395638629284,68.42062878608704,0,None,i7172,68.42062878608704,833.66015625,752.8763020833334,-1,0,3349229
1742430340,1742430417,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2345 n_samples 571 threshold 0.3848395362953012 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2345,571,0.3848395362953012,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5604735202492211,61.80424237251282,0,None,i7163,61.80424237251282,794.875,735.1414930555555,-1,0,3349236
1742430323,1742430418,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2771 n_samples 476 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2771,476,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5725109034267912,86.46635580062866,0,None,i7158,86.46635580062866,841.06640625,757.2283380681819,-1,0,3349223
1742430323,1742430418,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5201246105919003,80.76214599609375,0,None,i7172,80.76214599609375,780.89453125,736.92578125,-1,0,3349216
1742430340,1742430423,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 786 n_samples 1000 threshold 0.6552490343099004 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",786,1000,0.6552490343099004,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5244610591900312,69.57190465927124,0,None,i7158,69.57190465927124,801.4609375,733.9852430555555,-1,0,3349243
1742430340,1742430424,84,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2276 n_samples 581 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2276,581,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5423551401869159,69.02260565757751,0,None,i7162,69.02260565757751,770.94140625,721.5251736111111,-1,0,3349239
1742430341,1742430443,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3483 n_samples 536 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3483,536,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5573707165109034,88.59040641784668,0,None,i7171,88.59040641784668,829.2578125,756.0788352272727,-1,0,3349230
1742430370,1742430447,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 463 n_samples 902 threshold 0.390002343171099 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",463,902,0.390002343171099,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5277133956386293,62.71089744567871,0,None,i7155,62.71089744567871,778.69140625,727.1905381944445,-1,0,3349268
1742430363,1742430452,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 664 n_samples 601 threshold 0.4195529788516411 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",664,601,0.4195529788516411,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5870031152647975,79.31296992301941,0,None,i7180,79.31296992301941,872.890625,768.287890625,-1,0,3349252
1742430370,1742430453,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 346 n_samples 209 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",346,209,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5365233644859813,72.22250080108643,0,None,i7155,72.22250080108643,806.7734375,742.725390625,-1,0,3349270
1742430370,1742430453,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2913 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2913,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.46979439252336447,72.47120189666748,0,None,i7154,72.47120189666748,740.296875,712.60078125,-1,0,3349272
1742430310,1742430457,147,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 911 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",911,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6032274143302181,136.62448596954346,0,None,i7159,136.62448596954346,965.953125,822.40576171875,-1,0,3349211
1742430370,1742430465,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2347 n_samples 367 threshold 0.45030405051643096 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2347,367,0.45030405051643096,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5862305295950155,81.63941240310669,0,None,i7164,81.63941240310669,850.125,765.4584517045455,-1,0,3349262
1742430370,1742430466,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2888 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2888,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5859190031152648,81.6364586353302,0,None,i7171,81.6364586353302,853.62890625,767.0713778409091,-1,0,3349260
1742430370,1742430466,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1114 n_samples 1000 threshold 0.5167722394190176 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1114,1000,0.5167722394190176,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5807601246105919,83.41748642921448,0,None,i7159,83.41748642921448,869.2421875,773.5763494318181,-1,0,3349264
1742430343,1742430470,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 949 n_samples 608 threshold 0.32495310529182375 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",949,608,0.32495310529182375,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5936573208722742,114.35144662857056,0,None,i7183,114.35144662857056,912.47265625,799.6369977678571,-1,0,3349250
1742430323,1742430482,159,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 736 threshold 0.36961145532921647 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,736,0.36961145532921647,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.612822429906542,145.0427987575531,0,None,i7158,145.0427987575531,984.46875,835.4129136029412,-1,0,3349219
1742430340,1742430486,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3816 n_samples 885 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3816,885,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.605183800623053,134.56067514419556,0,None,i7155,134.56067514419556,961.1328125,819.054931640625,-1,0,3349247
1742430363,1742430541,178,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6072772585669782,167.3221354484558,0,None,i7164,167.3221354484558,952.8828125,817.51953125,-1,0,3349254
1742430340,1742430545,205,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 260 n_samples 100 threshold 0.2897243516452328 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",260,100,0.2897243516452328,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6353146417445483,192.6470115184784,0,None,i7156,192.6470115184784,1024.265625,848.0266335227273,-1,0,3349245
1742430340,1742430569,229,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3561 n_samples 351 threshold 0.5414141351310386 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3561,351,0.5414141351310386,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6120872274143302,214.8634376525879,0,None,i7165,214.8634376525879,1015.43359375,853.63427734375,-1,0,3349233
1742430370,1742430582,212,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1165 n_samples 352 threshold 0.41134720405465575 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1165,352,0.41134720405465575,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6173333333333333,200.56034517288208,0,None,i7156,200.56034517288208,1027.08984375,852.4650135869565,-1,0,3349266
1742430325,1742430635,310,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 263 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,263,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6065420560747663,291.20630621910095,0,None,i7156,291.20630621910095,1014.78125,849.9237060546875,-1,0,3349226
1742437482,1742437540,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 138 n_samples 467 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",138,467,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5182180685358255,47.8523063659668,0,None,i7167,47.8523063659668,742.0234375,708.0864955357143,-1,0,3354019
1742437482,1742437547,65,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 385 n_samples 781 threshold 0.45886568542038203 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",385,781,0.45886568542038203,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5312647975077882,56.074942111968994,0,None,i7180,56.074942111968994,771.16015625,720.07568359375,-1,0,3354013
1742437484,1742437555,71,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 575 n_samples 737 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",575,737,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.48279127725856696,59.14938402175903,0,None,i7172,59.14938402175903,758.38671875,716.18359375,-1,0,3354027
1742437504,1742437568,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 133 n_samples 283 threshold 0.30778146353358155 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",133,283,0.30778146353358155,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5760747663551402,55.03066349029541,0,None,i7169,55.03066349029541,776.609375,725.02978515625,-1,0,3354031
1742437482,1742437572,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2718 n_samples 736 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2718,736,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.593981308411215,80.8639395236969,0,None,i7171,80.8639395236969,869.03515625,776.9367897727273,-1,0,3354017
1742437513,1742437583,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 143 n_samples 503 threshold 0.5560978907408871 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",143,503,0.5560978907408871,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49793146417445483,55.24315166473389,0,None,i7165,55.24315166473389,731.953125,703.701171875,-1,0,3354040
1742437485,1742437588,103,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 306 n_samples 284 threshold 0.3407250391400243 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",306,284,0.3407250391400243,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6102180685358255,90.12641716003418,0,None,i7178,90.12641716003418,876.28515625,772.2833806818181,-1,0,3354015
1742437525,1742437589,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2318 n_samples 1000 threshold 0.6588026888617263 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2318,1000,0.6588026888617263,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.45165109034267914,51.48334240913391,0,None,i7169,51.48334240913391,724.9296875,702.2578125,-1,0,3354054
1742437512,1742437595,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 691 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",691,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5643239875389409,68.98335313796997,0,None,i7163,68.98335313796997,820.234375,744.1527777777778,-1,0,3354044
1742437514,1742437596,82,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2746 n_samples 735 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2746,735,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49718380062305295,70.6673731803894,0,None,i7160,70.6673731803894,782.9921875,732.748828125,-1,0,3354052
1742437543,1742437601,58,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 89 n_samples 263 threshold 0.4090574665787957 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",89,263,0.4090574665787957,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5526728971962617,48.99894452095032,0,None,i7186,48.99894452095032,749.38671875,709.3002232142857,-1,0,3354069
1742437512,1742437602,90,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1107 n_samples 866 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1107,866,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5235015576323988,79.27052593231201,0,None,i7166,79.27052593231201,792.49609375,732.230859375,-1,0,3354038
1742437505,1742437607,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1081 n_samples 561 threshold 0.789263825009889 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1081,561,0.789263825009889,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5670903426791277,87.46306085586548,0,None,i7184,87.46306085586548,844.52734375,759.3924005681819,-1,0,3354029
1742437526,1742437609,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 327 n_samples 461 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",327,461,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5784672897196261,71.36906123161316,0,None,i7160,71.36906123161316,827.265625,751.9734375,-1,0,3354064
1742437543,1742437613,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2519 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2519,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5517133956386293,57.50982141494751,0,None,i7184,57.50982141494751,782.88671875,727.37646484375,-1,0,3354071
1742437482,1742437616,134,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2688 n_samples 309 threshold 0.4529762589601112 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2688,309,0.4529762589601112,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6091713395638629,124.00800442695618,0,None,i7163,124.00800442695618,947.6328125,816.04609375,-1,0,3354025
1742437483,1742437616,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1698 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1698,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6072274143302181,121.3017098903656,0,None,i7166,121.3017098903656,929.37109375,814.1338541666667,-1,0,3354021
1742437542,1742437619,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2615 n_samples 565 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2615,565,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5303426791277258,64.29882574081421,0,None,i7158,64.29882574081421,789.1015625,735.23828125,-1,0,3354080
1742437513,1742437621,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1357 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1357,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5921121495327103,100.0120952129364,0,None,i7183,100.0120952129364,907.08984375,787.1529947916666,-1,0,3354034
1742437524,1742437633,109,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1239 n_samples 958 threshold 0.3673870860664861 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1239,958,0.3673870860664861,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5964485981308412,98.95857858657837,0,None,i7167,98.95857858657837,905.29296875,787.30859375,-1,0,3354057
1742437512,1742437640,128,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2767 n_samples 443 threshold 0.32975559887879097 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2767,443,0.32975559887879097,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6057071651090342,114.47213506698608,0,None,i7163,114.47213506698608,930.72265625,802.2335379464286,-1,0,3354047
1742437542,1742437644,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 957 n_samples 932 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",957,932,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5913644859813084,92.37497901916504,0,None,i7164,92.37497901916504,877.1875,778.8779296875,-1,0,3354076
1742437524,1742437645,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1936 n_samples 729 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1936,729,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5921869158878504,105.94382977485657,0,None,i7166,105.94382977485657,863.2734375,771.8518629807693,-1,0,3354060
1742437525,1742437652,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2924 n_samples 488 threshold 0.3426859330571813 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2924,488,0.3426859330571813,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6064423676012461,113.21131443977356,0,None,i7159,113.21131443977356,938.58984375,811.5524553571429,-1,0,3354067
1742437513,1742437653,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2446 n_samples 312 threshold 0.35638188157541284 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2446,312,0.35638188157541284,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.606803738317757,127.36312198638916,0,None,i7161,127.36312198638916,916.80078125,796.4736979166667,-1,0,3354050
1742437513,1742437666,153,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3739 n_samples 516 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3739,516,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.594803738317757,139.62319350242615,0,None,i7164,139.62319350242615,883.8359375,783.461669921875,-1,0,3354042
1742437514,1742437668,154,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2720 n_samples 612 threshold 0.3301995152957417 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2720,612,0.3301995152957417,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.585619937694704,138.6959743499756,0,None,i7170,138.6959743499756,881.1171875,780.068603515625,-1,0,3354036
1742437542,1742437682,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3135 n_samples 532 threshold 0.4063119459025737 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3135,532,0.4063119459025737,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6024548286604361,129.53469491004944,0,None,i7166,129.53469491004944,944.171875,805.2856770833333,-1,0,3354073
1742437543,1742437741,198,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2853 n_samples 427 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2853,427,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6130841121495327,181.37092304229736,0,None,i7161,181.37092304229736,954.6171875,823.6616443452381,-1,0,3354078
1742437483,1742438157,674,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 100 threshold 0.4010317285376651 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,100,0.4010317285376651,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6041744548286604,661.8498630523682,0,None,i7164,661.8498630523682,1248.30078125,1048.1251698369565,-1,0,3354023
1742446395,1742446459,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 36 n_samples 100 threshold 0.45160740204219263 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",36,100,0.45160740204219263,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5777943925233645,52.04512548446655,0,None,i7168,52.04512548446655,747.34375,713.48388671875,-1,0,3358894
1742446410,1742446474,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 338 n_samples 872 threshold 0.3862394192881933 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",338,872,0.3862394192881933,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.51598753894081,49.52662181854248,0,None,i7185,49.52662181854248,754.83203125,711.6305803571429,-1,0,3358905
1742446425,1742446489,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 121 n_samples 390 threshold 0.28168218606615725 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",121,390,0.28168218606615725,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5251464174454828,51.260271072387695,0,None,i7169,51.260271072387695,750.125,714.32421875,-1,0,3358927
1742446425,1742446495,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2321 n_samples 523 threshold 0.49761715707369136 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2321,523,0.49761715707369136,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5555389408099688,61.01588296890259,0,None,i7184,61.01588296890259,789.27734375,733.5611979166666,-1,0,3358913
1742446395,1742446497,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1450 n_samples 748 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1450,748,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5370965732087227,86.2324914932251,0,None,i7167,86.2324914932251,797.28125,742.5710227272727,-1,0,3358901
1742446395,1742446497,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1485 n_samples 811 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1485,811,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5401370716510904,89.43443584442139,0,None,i7168,89.43443584442139,793.49609375,734.8288352272727,-1,0,3358888
1742446425,1742446502,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2658 n_samples 908 threshold 0.46178665911391387 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2658,908,0.46178665911391387,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5754018691588785,66.40053677558899,0,None,i7164,66.40053677558899,818.0625,743.4917534722222,-1,0,3358952
1742446425,1742446508,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 209 n_samples 258 threshold 0.4735179973638406 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",209,258,0.4735179973638406,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5836386292834891,70.79429411888123,0,None,i7166,70.79429411888123,811.85546875,744.914453125,-1,0,3358943
1742446425,1742446508,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2360 n_samples 465 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2360,465,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5798380062305296,70.35255908966064,0,None,i7180,70.35255908966064,835.29296875,760.5828125,-1,0,3358915
1742446455,1742446519,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 115 n_samples 195 threshold 0.5113509876606323 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",115,195,0.5113509876606323,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.570018691588785,54.47397780418396,0,None,i7176,54.47397780418396,774.58984375,725.39599609375,-1,0,3358986
1742446425,1742446527,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 553 n_samples 546 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",553,546,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5974205607476636,87.74026584625244,0,None,i7166,87.74026584625244,871.9375,769.4254261363636,-1,0,3358939
1742446395,1742446528,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3359 n_samples 1000 threshold 0.35901100647518197 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3359,1000,0.35901100647518197,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.593607476635514,117.62420797348022,0,None,i7168,117.62420797348022,907.953125,790.9171316964286,-1,0,3358891
1742446409,1742446531,122,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 807 n_samples 483 threshold 0.3976775087432113 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",807,483,0.3976775087432113,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6038380062305296,107.37097835540771,0,None,i7175,107.37097835540771,912.15234375,794.0895432692307,-1,0,3358909
1742446395,1742446541,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1199 n_samples 604 threshold 0.43701830230507166 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1199,604,0.43701830230507166,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5992647975077882,132.03073859214783,0,None,i7169,132.03073859214783,926.83984375,807.657958984375,-1,0,3358883
1742446395,1742446554,159,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1658 n_samples 647 threshold 0.5026803858297743 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1658,647,0.5026803858297743,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6138816199376947,148.1884377002716,0,None,i7167,148.1884377002716,952.9609375,817.23828125,-1,0,3358898
1742446425,1742446566,141,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2450 n_samples 100 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2450,100,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5783551401869159,128.81468415260315,0,None,i7165,128.81468415260315,868.8984375,775.0666666666667,-1,0,3358947
1742446426,1742446567,141,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2991 n_samples 913 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2991,913,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.59601246105919,124.89715242385864,0,None,i7170,124.89715242385864,872.4140625,781.1888020833334,-1,0,3358924
1742446429,1742446569,140,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3582 n_samples 1000 threshold 0.37045373309028173 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3582,1000,0.37045373309028173,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5987165109034268,128.9220654964447,0,None,i7168,128.9220654964447,918.79296875,790.271875,-1,0,3358966
1742446455,1742446576,121,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3805 n_samples 655 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3805,655,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.597981308411215,105.84107208251953,0,None,i7178,105.84107208251953,888.1640625,782.2518028846154,-1,0,3358982
1742446425,1742446578,153,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3874 n_samples 756 threshold 0.28633839424541674 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3874,756,0.28633839424541674,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6109158878504672,141.7551097869873,0,None,i7163,141.7551097869873,978.97265625,831.6649816176471,-1,0,3358957
1742446455,1742446582,127,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 313 n_samples 223 threshold 0.3382183724128969 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",313,223,0.3382183724128969,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.622031152647975,111.33497619628906,0,None,i7169,111.33497619628906,901.0078125,788.9919084821429,-1,0,3358994
1742446449,1742446583,134,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1408 n_samples 628 threshold 0.44383616098629913 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1408,628,0.44383616098629913,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6076884735202492,122.75287628173828,0,None,i7185,122.75287628173828,939.27734375,815.9973958333334,-1,0,3358971
1742446425,1742446584,159,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1707 n_samples 816 threshold 0.40428551675244573 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1707,816,0.40428551675244573,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6045981308411215,145.28839588165283,0,None,i7168,145.28839588165283,953.90234375,812.8170955882352,-1,0,3358931
1742446425,1742446584,159,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 239 n_samples 100 threshold 0.3980021979453326 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",239,100,0.3980021979453326,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6203489096573209,145.15097665786743,0,None,i7167,145.15097665786743,966.3046875,819.1252297794117,-1,0,3358935
1742446455,1742446589,134,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1907 n_samples 884 threshold 0.5575470767164933 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1907,884,0.5575470767164933,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6064423676012461,119.93724179267883,0,None,i7179,119.93724179267883,940.05078125,810.5984933035714,-1,0,3358978
1742446430,1742446595,165,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3841 n_samples 649 threshold 0.4630832231486182 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3841,649,0.4630832231486182,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6078380062305296,152.17832922935486,0,None,i7178,152.17832922935486,990.14453125,840.5444878472222,-1,0,3358961
1742446455,1742446602,147,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1230 n_samples 542 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1230,542,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6179065420560748,136.60542345046997,0,None,i7184,136.60542345046997,956.15625,820.332275390625,-1,0,3358974
1742446425,1742446604,179,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 766 n_samples 100 threshold 0.6751626152480776 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",766,100,0.6751626152480776,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6046355140186915,164.2575032711029,0,None,i7176,164.2575032711029,1023.40625,851.1019736842105,-1,0,3358918
1742446455,1742446638,183,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 4000 n_samples 727 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",4000,727,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6106168224299066,168.88802456855774,0,None,i7171,168.88802456855774,1004.05859375,837.5113075657895,-1,0,3358990
1742446426,1742446660,234,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3781 n_samples 324 threshold 0.6009672271138708 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3781,324,0.6009672271138708,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6188660436137071,220.56266021728516,0,None,i7171,220.56266021728516,1070.76171875,878.3125,-1,0,3358922
1742455007,1742455071,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2019 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2019,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3073146417445483,49.18650150299072,0,None,i7178,49.18650150299072,680.3359375,679.9090401785714,-1,0,3362286
1742455017,1742455080,63,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2253 n_samples 825 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2253,825,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49854205607476637,54.91107439994812,0,None,i7179,54.91107439994812,745.3828125,712.439453125,-1,0,3362294
1742455037,1742455094,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2036 n_samples 932 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2036,932,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3309657320872274,44.006911754608154,0,None,i7182,44.006911754608154,688.05859375,687.5106026785714,-1,0,3362295
1742455017,1742455099,82,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 70 n_samples 100 threshold 0.44868348703898814 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",70,100,0.44868348703898814,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6088722741433021,71.76007223129272,0,None,i7180,71.76007223129272,804.0859375,741.849609375,-1,0,3362291
1742455037,1742455100,63,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2085 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2085,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.31186292834890966,50.681238651275635,0,None,i7178,50.681238651275635,688.3359375,687.87451171875,-1,0,3362300
1742455037,1742455107,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 295 n_samples 1000 threshold 0.4666186217712905 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",295,1000,0.4666186217712905,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5153894080996885,56.09671688079834,0,None,i7179,56.09671688079834,734.7265625,703.93115234375,-1,0,3362299
1742455037,1742455107,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 103 n_samples 154 threshold 0.4361695181294402 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",103,154,0.4361695181294402,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5968847352024922,59.96731495857239,0,None,i7178,59.96731495857239,800.58203125,732.115234375,-1,0,3362301
1742455037,1742455107,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 79 n_samples 152 threshold 0.5538755076764884 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",79,152,0.5538755076764884,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5539688473520249,58.4997456073761,0,None,i7176,58.4997456073761,755.87890625,714.64501953125,-1,0,3362302
1742455037,1742455113,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 60 n_samples 100 threshold 0.4792688044859997 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",60,100,0.4792688044859997,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5951651090342679,67.23655247688293,0,None,i7176,67.23655247688293,783.1328125,729.0412326388889,-1,0,3362303
1742455057,1742455114,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2254 n_samples 920 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2254,920,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4975327102803738,43.48868274688721,0,None,i7175,43.48868274688721,737.734375,706.0647321428571,-1,0,3362309
1742455007,1742455115,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 86 n_samples 100 threshold 0.42430636675669275 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",86,100,0.42430636675669275,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6190031152647975,96.6826491355896,0,None,i7178,96.6826491355896,820.3125,740.9986979166666,-1,0,3362289
1742455057,1742455127,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2230 n_samples 766 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2230,766,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5039626168224299,55.927286863327026,0,None,i7176,55.927286863327026,744.1015625,708.31689453125,-1,0,3362306
1742455058,1742455128,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 121 n_samples 212 threshold 0.4010310822461263 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",121,212,0.4010310822461263,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5867414330218068,57.44462180137634,0,None,i7175,57.44462180137634,789.94921875,729.921875,-1,0,3362308
1742455017,1742455131,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 118 n_samples 100 threshold 0.32005130762620493 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",118,100,0.32005130762620493,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6376947040498442,102.54673624038696,0,None,i7180,102.54673624038696,889.3984375,782.0120192307693,-1,0,3362293
1742455057,1742455134,77,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 124 n_samples 151 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",124,151,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5272149532710281,62.91097950935364,0,None,i7175,62.91097950935364,752.89453125,715.6605902777778,-1,0,3362310
1742455007,1742455140,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 103 n_samples 100 threshold 0.37086086627569315 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",103,100,0.37086086627569315,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6434392523364486,116.5869996547699,0,None,i7178,116.5869996547699,855.55859375,757.0069754464286,-1,0,3362285
1742455037,1742455144,107,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 95 n_samples 100 threshold 0.400760615128722 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",95,100,0.400760615128722,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6260560747663552,95.39229774475098,0,None,i7179,95.39229774475098,840.7421875,755.8619791666666,-1,0,3362297
1742455037,1742455151,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 823 n_samples 858 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",823,858,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5896573208722742,103.82234454154968,0,None,i7179,103.82234454154968,872.296875,771.2758413461538,-1,0,3362298
1742455037,1742455151,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2623 n_samples 284 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2623,284,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5661308411214954,101.85541105270386,0,None,i7179,101.85541105270386,845.5546875,757.7665264423077,-1,0,3362296
1742455057,1742455152,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 678 n_samples 885 threshold 0.6092014696963084 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",678,885,0.6092014696963084,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.543638629283489,81.85531497001648,0,None,i7176,81.85531497001648,806.03515625,740.6239346590909,-1,0,3362305
1742455007,1742455159,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1494 n_samples 1000 threshold 0.4169315325399771 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1494,1000,0.4169315325399771,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5901059190031153,139.47605061531067,0,None,i7178,139.47605061531067,911.4921875,779.918212890625,-1,0,3362283
1742455007,1742455159,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1894 n_samples 1000 threshold 0.46190637565944437 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1894,1000,0.46190637565944437,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6153520249221184,140.7995626926422,0,None,i7179,140.7995626926422,925.74609375,808.0606617647059,-1,0,3362281
1742455007,1742455165,158,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1853 n_samples 1000 threshold 0.26678349975091187 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1853,1000,0.26678349975091187,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6070778816199377,145.8690836429596,0,None,i7179,145.8690836429596,940.9296875,812.4087775735294,-1,0,3362279
1742455017,1742455169,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1866 n_samples 1000 threshold 0.35637501316728587 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1866,1000,0.35637501316728587,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5990529595015577,141.98774337768555,0,None,i7180,141.98774337768555,946.1640625,813.4356617647059,-1,0,3362290
1742455057,1742455171,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1103 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1103,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.597619937694704,102.72580766677856,0,None,i7176,102.72580766677856,883.23046875,776.0465745192307,-1,0,3362307
1742455017,1742455175,158,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1844 n_samples 928 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1844,928,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.609968847352025,143.41494917869568,0,None,i7180,143.41494917869568,958.12109375,815.7791819852941,-1,0,3362292
1742455068,1742455177,109,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1526 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1526,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49249844236760126,95.96578884124756,0,None,i7175,95.96578884124756,772.34375,730.5553385416666,-1,0,3362311
1742455007,1742455178,171,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1847 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1847,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6066292834890966,159.64930415153503,0,None,i7178,159.64930415153503,940.19921875,794.388671875,-1,0,3362288
1742455007,1742455184,177,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1836 n_samples 958 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1836,958,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6002616822429907,162.85808444023132,0,None,i7178,162.85808444023132,946.8515625,802.4436677631579,-1,0,3362287
1742455057,1742455203,146,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1460 n_samples 836 threshold 0.3460308795114687 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1460,836,0.3460308795114687,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6081619937694704,131.53195190429688,0,None,i7176,131.53195190429688,928.984375,801.58203125,-1,0,3362304
1742462032,1742462084,52,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 762 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,762,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.35323364485981307,30.94817590713501,0,None,i7186,30.94817590713501,687.55078125,687.349609375,-1,0,3364440
1742462058,1742462103,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 869 threshold 0.2096104176116321 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,869,0.2096104176116321,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3617196261682243,31.984399557113647,0,None,i7184,31.984399557113647,687.6015625,687.34765625,-1,0,3364450
1742462058,1742462103,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 700 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,700,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.36990654205607476,31.5374858379364,0,None,i7185,31.5374858379364,687.62109375,687.333984375,-1,0,3364448
1742462052,1742462103,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 43 n_samples 591 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",43,591,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.43652336448598134,37.68457293510437,0,None,i7185,37.68457293510437,697.3515625,690.3509114583334,-1,0,3364444
1742462052,1742462109,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 116 n_samples 388 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",116,388,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4367227414330218,47.71965265274048,0,None,i7186,47.71965265274048,711.265625,694.703125,-1,0,3364443
1742462052,1742462109,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 44 n_samples 194 threshold 0.3962725711901777 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",44,194,0.3962725711901777,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5454828660436137,46.39094352722168,0,None,i7185,46.39094352722168,730.46875,702.6456473214286,-1,0,3364446
1742462052,1742462116,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2168 n_samples 297 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2168,297,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5026292834890965,52.50644040107727,0,None,i7186,52.50644040107727,734.0390625,706.51708984375,-1,0,3364441
1742462071,1742462116,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.340797507788162,29.69667410850525,0,None,i7184,29.69667410850525,686.98046875,686.82265625,-1,0,3364454
1742462058,1742462122,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2123 n_samples 585 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2123,585,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.41313395638629286,48.686389446258545,0,None,i7184,48.686389446258545,698.8046875,689.9988839285714,-1,0,3364452
1742462052,1742462135,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 760 n_samples 536 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",760,536,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5199252336448598,71.07315683364868,0,None,i7186,71.07315683364868,782.16796875,735.2578125,-1,0,3364442
1742462072,1742462136,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 262 n_samples 663 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",262,663,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.432398753894081,54.315518856048584,0,None,i7184,54.315518856048584,722.828125,701.0927734375,-1,0,3364455
1742462058,1742462141,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 78 n_samples 137 threshold 0.33441109704390676 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",78,137,0.33441109704390676,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6090965732087228,68.22518587112427,0,None,i7184,68.22518587112427,800.1953125,734.4578993055555,-1,0,3364449
1742462072,1742462142,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 127 n_samples 476 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",127,476,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.45834267912772586,51.12517738342285,0,None,i7183,51.12517738342285,714.171875,697.56201171875,-1,0,3364458
1742462052,1742462148,96,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 497 n_samples 490 threshold 0.6369844965968239 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",497,490,0.6369844965968239,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5664797507788162,79.2488763332367,0,None,i7185,79.2488763332367,823.6328125,745.366796875,-1,0,3364445
1742462088,1742462151,63,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2141 n_samples 503 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2141,503,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.42221806853582555,50.55458950996399,0,None,i7183,50.55458950996399,705.078125,693.51708984375,-1,0,3364464
1742462092,1742462156,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 205 n_samples 707 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",205,707,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.42074766355140186,49.99270009994507,0,None,i7186,49.99270009994507,702.57421875,692.4029017857143,-1,0,3364483
1742462058,1742462160,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 755 n_samples 445 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",755,445,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5608224299065421,90.0351574420929,0,None,i7184,90.0351574420929,843.76953125,753.7890625,-1,0,3364451
1742462052,1742462160,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3115 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3115,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5939563862928349,96.7836639881134,0,None,i7185,96.7836639881134,888.71875,778.9358723958334,-1,0,3364447
1742462088,1742462170,82,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2528 n_samples 1000 threshold 0.37344242993283006 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2528,1000,0.37344242993283006,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5350903426791277,69.8978443145752,0,None,i7183,69.8978443145752,789.05859375,726.3350694444445,-1,0,3364469
1742462088,1742462183,95,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2766 n_samples 1000 threshold 0.4063012613712058 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2766,1000,0.4063012613712058,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5707040498442367,83.25896382331848,0,None,i7183,83.25896382331848,825.015625,749.3334517045455,-1,0,3364467
1742462094,1742462195,101,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 243 n_samples 906 threshold 0.5259106317420591 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",243,906,0.5259106317420591,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.49383177570093456,53.45665383338928,0,None,i7182,53.45665383338928,723.453125,700.697265625,-1,0,3364472
1742462094,1742462195,101,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 188 n_samples 744 threshold 0.5911314321832244 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",188,744,0.5911314321832244,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4790031152647975,52.33294224739075,0,None,i7182,52.33294224739075,726.87890625,702.640625,-1,0,3364477
1742462088,1742462195,107,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 529 n_samples 418 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",529,418,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5443364485981308,91.03113889694214,0,None,i7183,91.03113889694214,812.4140625,745.1513671875,-1,0,3364461
1742462072,1742462198,126,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1903 n_samples 611 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1903,611,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5939563862928349,114.40936613082886,0,None,i7183,114.40936613082886,867.10546875,774.6395089285714,-1,0,3364459
1742462094,1742462201,107,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2422 n_samples 952 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2422,952,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.44672897196261685,58.74135494232178,0,None,i7182,58.74135494232178,708.40625,696.03662109375,-1,0,3364476
1742462094,1742462227,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1192 n_samples 740 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1192,740,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5056573208722741,82.72932982444763,0,None,i7182,82.72932982444763,782.53125,735.484375,-1,0,3364480
1742462088,1742462227,139,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1896 n_samples 674 threshold 0.6814000546256195 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1896,674,0.6814000546256195,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6016448598130841,126.55480408668518,0,None,i7183,126.55480408668518,921.71875,793.8078125,-1,0,3364466
1742462094,1742462239,145,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 479 n_samples 472 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",479,472,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6029657320872274,97.95997142791748,0,None,i7182,97.95997142791748,874.63671875,769.1272786458334,-1,0,3364485
1742462094,1742462289,195,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1884 n_samples 735 threshold 0.5705608578417204 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1884,735,0.5705608578417204,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6077757009345794,149.48393964767456,0,None,i7182,149.48393964767456,945.64453125,808.9698988970588,-1,0,3364482
1742462094,1742462315,221,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1818 n_samples 297 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1818,297,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6069408099688474,174.38574171066284,0,None,i7182,174.38574171066284,984.24609375,825.486328125,-1,0,3364474
1742471199,1742471249,50,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 47 n_samples 714 threshold 0.5018537093950876 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",47,714,0.5018537093950876,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4090841121495327,38.27794027328491,0,None,i7186,38.27794027328491,690.86328125,688.8255208333334,-1,0,3370337
1742471193,1742471250,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 142 n_samples 1000 threshold 0.5260871984116383 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",142,1000,0.5260871984116383,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4326479750778816,43.70925760269165,0,None,i7186,43.70925760269165,705.6953125,692.1311383928571,-1,0,3370335
1742471199,1742471262,63,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2217 n_samples 879 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2217,879,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.38143302180685357,51.16883397102356,0,None,i7186,51.16883397102356,700.71484375,691.3408203125,-1,0,3370339
1742471199,1742471268,69,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2207 n_samples 245 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2207,245,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5361121495327102,57.52941298484802,0,None,i7186,57.52941298484802,759.5625,716.0712890625,-1,0,3370338
1742471220,1742471271,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 44 n_samples 535 threshold 0.477512401725635 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",44,535,0.477512401725635,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4468535825545171,38.63424277305603,0,None,i7185,38.63424277305603,697.2109375,689.6653645833334,-1,0,3370343
1742471223,1742471274,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 196 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,196,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4668785046728972,36.84527921676636,0,None,i7184,36.84527921676636,695.92578125,688.9296875,-1,0,3370348
1742471220,1742471277,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 123 n_samples 834 threshold 0.42824843124731443 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",123,834,0.42824843124731443,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.45882866043613707,44.62898588180542,0,None,i7185,44.62898588180542,711.16796875,694.4536830357143,-1,0,3370340
1742471220,1742471277,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 36 n_samples 356 threshold 0.47562732353791143 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",36,356,0.47562732353791143,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.47894080996884736,39.83850836753845,0,None,i7185,39.83850836753845,699.734375,690.2434895833334,-1,0,3370342
1742471220,1742471277,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 77 n_samples 783 threshold 0.4411416720158839 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",77,783,0.4411416720158839,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4275763239875389,39.61608910560608,0,None,i7185,39.61608910560608,698.59765625,690.0989583333334,-1,0,3370341
1742471220,1742471277,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 114 n_samples 947 threshold 0.44760802969874747 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",114,947,0.44760802969874747,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.43800623052959503,40.82730960845947,0,None,i7185,40.82730960845947,701.54296875,691.4190848214286,-1,0,3370346
1742471221,1742471278,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 146 n_samples 1000 threshold 0.2 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",146,1000,0.2,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4493956386292835,40.26615571975708,0,None,i7185,40.26615571975708,714.2734375,697.8186383928571,-1,0,3370344
1742471223,1742471287,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 181 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",181,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.35552647975077883,51.5919291973114,0,None,i7185,51.5919291973114,689.13671875,687.86474609375,-1,0,3370347
1742471243,1742471288,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 1000 threshold 0.49319553245062936 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,1000,0.49319553245062936,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3423302180685358,32.17897176742554,0,None,i7184,32.17897176742554,686.98828125,686.869140625,-1,0,3370353
1742471224,1742471294,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2231 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2231,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.34002492211838004,55.924652338027954,0,None,i7184,55.924652338027954,694.64453125,689.79052734375,-1,0,3370352
1742471224,1742471294,70,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 241 n_samples 1000 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",241,1000,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3483738317757009,55.26901578903198,0,None,i7184,55.26901578903198,694.546875,689.36181640625,-1,0,3370350
1742471243,1742471294,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 10 n_samples 257 threshold 0.4723405141569048 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",10,257,0.4723405141569048,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4370467289719626,35.71539759635925,0,None,i7184,35.71539759635925,688.80859375,687.7962239583334,-1,0,3370357
1742471243,1742471294,51,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 19 n_samples 747 threshold 0.5030529155069199 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",19,747,0.5030529155069199,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.37897819314641745,35.67786765098572,0,None,i7184,35.67786765098572,687.3203125,687.091796875,-1,0,3370358
1742471253,1742471298,45,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 12 n_samples 576 threshold 0.4025226445791139 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",12,576,0.4025226445791139,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3870280373831776,33.69284200668335,0,None,i7183,33.69284200668335,688.4140625,688.2766927083334,-1,0,3370364
1742471243,1742471300,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 30 n_samples 345 threshold 0.6402047954782072 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",30,345,0.6402047954782072,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4503052959501558,40.39937710762024,0,None,i7184,40.39937710762024,690.69921875,688.0452008928571,-1,0,3370354
1742471244,1742471301,57,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 136 n_samples 559 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",136,559,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.4154890965732087,46.65855169296265,0,None,i7183,46.65855169296265,698.21484375,691.3236607142857,-1,0,3370359
1742471223,1742471312,89,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2232 n_samples 286 threshold 0.5369826930824956 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2232,286,0.5369826930824956,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5786666666666667,75.37989354133606,0,None,i7184,75.37989354133606,813.58984375,740.541796875,-1,0,3370351
1742471253,1742471317,64,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 94 n_samples 749 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",94,749,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.3398504672897196,51.23502588272095,0,None,i7183,51.23502588272095,687.93359375,687.59130859375,-1,0,3370362
1742471220,1742471322,102,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 92 n_samples 100 threshold 0.29549109013103125 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",92,100,0.29549109013103125,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.6426417445482866,84.43003702163696,0,None,i7185,84.43003702163696,860.84375,765.4921875,-1,0,3370345
1742471243,1742471326,83,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2278 n_samples 466 threshold 0.4490584836418411 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2278,466,0.4490584836418411,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5542305295950156,69.22774744033813,0,None,i7184,69.22774744033813,794.02734375,730.2829861111111,-1,0,3370355
1742471193,1742471327,134,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1283 n_samples 308 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1283,308,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5962367601246106,120.57481551170349,0,None,i7186,120.57481551170349,897.43359375,792.0760416666667,-1,0,3370336
1742471253,1742471329,76,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2368 n_samples 711 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2368,711,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.45435514018691586,59.93902540206909,0,None,i7183,59.93902540206909,736.2265625,706.28173828125,-1,0,3370360
1742471253,1742471361,108,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 2980 n_samples 527 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",2980,527,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5637507788161994,92.20293378829956,0,None,i7183,92.20293378829956,843.51171875,759.0745442708334,-1,0,3370363
1742471253,1742471367,114,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 3233 n_samples 509 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",3233,509,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.571202492211838,100.73117733001709,0,None,i7183,100.73117733001709,870.875,773.3161057692307,-1,0,3370361
1742471223,1742471375,152,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1276 n_samples 266 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1276,266,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5953894080996884,140.4887957572937,0,None,i7184,140.4887957572937,924.15234375,804.0510110294117,-1,0,3370349
1742471253,1742471386,133,"module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier OCDD recent_samples_size 1027 n_samples 262 threshold 0.8 outlier_detector_kwargs \{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",1027,262,0.8,"\{\'nu\':0.5,\'kernel\':\'rbf\',\'gamma\':\'auto\'\}",0.5843489096573209,117.31127524375916,0,None,i7183,117.31127524375916,913.30078125,788.3166852678571,-1,0,3370365

Key	Value
config_yaml	None
config_toml	None
config_json	None
num_random_steps	20
max_eval	500
run_program	[['bW9kdWxlIGxvYWQgR0NDY29yZS8xMC4zLjAgUHl0aG9uICYmIHNvdXJjZSAvZGF0YS9ob3JzZS93cy9zNDEyMjQ4NS1jb21wUGVyZkREL2JlbmNobWFyay92ZW52L2Jpbi9hY3RpdmF0ZSAmJiBw…
experiment_name	OCDD_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACC-RUNTIME
mem_gb	32
parameter	[['recent_samples_size', 'range', '10', '4000', 'int'], ['n_samples', 'range', '100', '1000', 'int'], ['threshold', 'range', '0.2', '0.8', 'float'],
	['outlier_detector_kwargs', 'fixed', "\\{\\'nu\\':0.5,\\'kernel\\':\\'rbf\\',\\'gamma\\':\\'auto\\'\\}"]]
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	30
slurm_use_srun	False
time	1260
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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				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: 60px;
				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;
				color: #007bff;
			}
			
			.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;
			}
			
			.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;
				}
			
				.logo-img {
					max-height: 50px;
					pointer-events: unset;
				}
			
				.badge-img {
					max-width: 100px;
				}
			
				.nav-tab {
					font-size: 0.9rem;
					padding: 6px 12px;
				}
			
				.header_button {
					font-size: 2em;
				}
			}
			
			.header_button {
				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;
			}
			

			/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
			body{
				color: #222
			}
			
			.surface{
				background: #ece9d8
			}
			
			u{
				text-decoration: none;
				border-bottom: .5px solid #222
			}
			
			a{
				color: #00f
			}
			
			a: focus{
				outline: 1px dotted #00f
			}
			
			code,code *{
				font-family: monospace
			}
			
			pre{
				display: block;
				padding: 12px 8px;
				background-color: #000;
				color: silver;
				font-size: 1rem;
				margin: 0;
				overflow: scroll;
			}
			
			summary: focus{
				outline: 1px dotted #000
			}
			
			: :-webkit-scrollbar{
				width: 16px
			}
			
			: :-webkit-scrollbar: horizontal{
				height: 17px
			}
			
			: :-webkit-scrollbar-track{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-color: #dfdfdf;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
				display: block
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: end{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			button{
				border: none;
				background: #ece9d8;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
				border-radius: 0;
				min-width: 75px;
				min-height: 23px;
				padding: 0 12px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
			}
			
			button.focused,button: focus{
				outline: 1px dotted #000;
				outline-offset: -4px
			}
			
			label{
				display: inline-flex;
				align-items: center
			}
			
			textarea{
				padding: 3px 4px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0
			}
			
			textarea: focus{
				outline: none
			}
			
			select: focus option{
				color: #000;
				background-color: #fff
			}
			
			.vertical-bar{
				width: 4px;
				height: 20px;
				background: silver;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			&: disabled,&: disabled+label{
				color: grey;
				text-shadow: 1px 1px 0 #fff
			}
			
			input[type=radio]+label{
				line-height: 13px;
				position: relative;
				margin-left: 19px
			}
			
			input[type=radio]+label: before{
				content: "";
				position: absolute;
				top: 0;
				left: -19px;
				display: inline-block;
				width: 13px;
				height: 13px;
				margin-right: 6px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: active+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: checked+label: after{
				content: "";
				display: block;
				width: 5px;
				height: 5px;
				top: 5px;
				left: -14px;
				position: absolute;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password]{
				padding: 3px 4px;
				border: 1px solid #7f9db9;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0;
				height: 21px;
				line-height: 2
			}
			
			input[type=email]: focus,input[type=password]: focus{
				outline: none
			}
			
			input[type=range]{
				-webkit-appearance: none;
				width: 100%;
				background: transparent
			}
			
			input[type=range]: focus{
				outline: none
			}
			
			input[type=range]: :-webkit-slider-thumb{
				-webkit-appearance: none;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range]: :-moz-range-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			.is-vertical{
				display: inline-block;
				width: 4px;
				height: 150px;
				transform: translateY(50%)
			}
			
			.is-vertical>input[type=range]{
				width: 150px;
				height: 4px;
				margin: 0 16px 0 10px;
				transform-origin: left;
				transform: rotate(270deg) translateX(calc(-50% + 8px))
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-thumb{
				transform: translateY(-8px) scaleX(-1)
			}
			
			.is-vertical>input[type=range]: :-moz-range-thumb{
				transform: translateY(2px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
				transform: translateY(-10px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
				transform: translateY(0) scaleX(-1)
			}
			
			.window{
				font-size: 11px;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
				background: #ece9d8;
				padding: 3px
			}
			
			.window fieldset{
				margin-bottom: 9px
			}
			
			.title-bar{
				background: #000;
				padding: 3px 2px 3px 3px;
				display: flex;
				justify-content: space-between;
				align-items: center
			}
			
			.title-bar-text{
				font-weight: 700;
				color: #fff;
				letter-spacing: 0;
				margin-right: 24px
			}
			
			.title-bar-controls button{
				padding: 0;
				display: block;
				min-width: 16px;
				min-height: 14px
			}
			
			.title-bar-controls button: focus{
				outline: none
			}
			
			.window-body{
				margin: 8px
			}
			
			.window-body pre{
				margin: -8px
			}
			
			.status-bar{
				margin: 0 1px;
				display: flex;
				gap: 1px
			}
			
			.status-bar-field{
				box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
				flex-grow: 1;
				padding: 2px 3px;
				margin: 0
			}
			
			ul.tree-view{
				display: block;
				background: #fff;
				padding: 6px;
				margin: 0
			}
			
			ul.tree-view li{
				list-style-type: none;
				margin-top: 3px
			}
			
			ul.tree-view a{
				text-decoration: none;
				color: #000
			}
			
			ul.tree-view a: focus{
				background-color: #2267cb;
				color: #fff
			}
			
			ul.tree-view ul{
				margin-top: 3px;
				margin-left: 16px;
				padding-left: 16px;
				border-left: 1px dotted grey
			}
			
			ul.tree-view ul>li{
				position: relative
			}
			
			ul.tree-view ul>li: before{
				content: "";
				display: block;
				position: absolute;
				left: -16px;
				top: 6px;
				width: 12px;
				border-bottom: 1px dotted grey
			}
			
			ul.tree-view ul>li: last-child: after{
				content: "";
				display: block;
				position: absolute;
				left: -20px;
				top: 7px;
				bottom: 0;
				width: 8px;
				background: #fff
			}
			
			ul.tree-view ul details>summary: before{
				margin-left: -22px;
				position: relative;
				z-index: 1
			}
			
			ul.tree-view details{
				margin-top: 0
			}
			
			ul.tree-view details>summary: before{
				text-align: center;
				display: block;
				float: left;
				content: "+";
				border: 1px solid grey;
				width: 8px;
				height: 9px;
				line-height: 9px;
				margin-right: 5px;
				padding-left: 1px;
				background-color: #fff
			}
			
			ul.tree-view details[open] summary{
				margin-bottom: 0
			}
			
			ul.tree-view details[open]>summary: before{
				content: "-"
			}
			
			fieldset{
				border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
				padding: 10px;
				padding-block-start: 8px;
				margin: 0
			}
			
			legend{
				background: #ece9d8
			}
			
			menu[role=tablist]{
				position: relative;
				margin: 0 0 -2px;
				text-indent: 0;
				list-style-type: none;
				display: flex;
				padding-left: 3px
			}
			
			menu[role=tablist] button{
				z-index: 1;
				display: block;
				color: #222;
				text-decoration: none;
				min-width: unset
			}
			
			menu[role=tablist] button[aria-selected=true]{
				padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
			}
			
			menu[role=tablist] button: focus{
				outline: 1px dotted #222;outline-offset: -4px
			}
			
			menu[role=tablist].justified button{
				flex-grow: 1;text-align: center
			}
			
			[role=tabpanel]{
				padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
			}
			
			: :-webkit-scrollbar{
				width: 17px
			}
			
			: :-webkit-scrollbar-corner{
				background: #dfdfdf
			}
			
			: :-webkit-scrollbar-track: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-track: horizontal{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-position: 50%;
				background-repeat: no-repeat;
				background-color: #c8d6fb;
				background-size: 7px;
				border: 1px solid #fff;
				border-radius: 2px;
				box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
			}
			
			: :-webkit-scrollbar-thumb: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb: horizontal{
				background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				height: 17px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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stroke='%23b7cdfc' d='M11 6h1m0 1h1'/%3E%3Cpath stroke='%23a4b8d3' d='M16 6h1'/%3E%3Cpath stroke='%23cad8fd' d='M4 7h2'/%3E%3Cpath stroke='%23b6cefb' d='M11 7h1m0 1h1'/%3E%3Cpath stroke='%23bacbf4' d='M14 7h1'/%3E%3Cpath stroke='%23a0b5d3' d='M16 7h1m-1 1h1m-1 5h1'/%3E%3Cpath stroke='%23c1d3fb' d='M8 8h1'/%3E%3Cpath stroke='%23b6cdfb' d='M13 8h1m-5 5h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 8h1'/%3E%3Cpath stroke='%23b4c8f6' d='M2 9h1'/%3E%3Cpath stroke='%23c2d5fc' d='M8 9h1m-1 1h1m-3 1h2'/%3E%3Cpath stroke='%23bdd3fb' d='M9 9h1m-2 3h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 9h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 9h1'/%3E%3Cpath stroke='%239fb5d2' d='M16 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c7f6' d='M2 10h1'/%3E%3Cpath stroke='%23c3d5fd' d='M7 10h1'/%3E%3Cpath stroke='%23bad4fc' d='M9 10h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M10 10h1m1 0h1m-2 2h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 10h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 10h1m-6 4h2'/%3E%3Cpath stroke='%23adc3f6' 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13h1'/%3E%3Cpath stroke='%23dbe3f8' d='M2 14h1'/%3E%3Cpath stroke='%23b7c6f1' d='M3 14h1'/%3E%3Cpath stroke='%23b8c9f2' d='M4 14h1m3 0h1'/%3E%3Cpath stroke='%23b2c8f4' d='M11 14h1'/%3E%3Cpath stroke='%23b1c6f3' d='M12 14h1'/%3E%3Cpath stroke='%23b0c4f2' d='M13 14h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 14h1'/%3E%3Cpath stroke='%23aec0d6' d='M16 14h1'/%3E%3Cpath stroke='%23c3d4e7' d='M1 15h1'/%3E%3Cpath stroke='%23aec4e5' d='M15 15h1'/%3E%3Cpath stroke='%23edf1f3' d='M1 16h1'/%3E%3Cpath stroke='%23aac0e1' d='M2 16h1'/%3E%3Cpath stroke='%2394b1d9' d='M3 16h1'/%3E%3Cpath stroke='%2388a7d8' d='M4 16h1'/%3E%3Cpath stroke='%2383a4d3' d='M5 16h1'/%3E%3Cpath stroke='%237da0d4' d='M6 16h1m3 0h3'/%3E%3Cpath stroke='%237e9fd2' d='M7 16h1'/%3E%3Cpath stroke='%237c9fd3' d='M8 16h2'/%3E%3Cpath stroke='%2382a4d6' d='M13 16h1'/%3E%3Cpath stroke='%2394b0dd' d='M14 16h1'/%3E%3Cpath stroke='%23ecf2f7' d='M15 16h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: end{
				height: 17px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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stroke='%23aecafa' d='M13 11h1'/%3E%3Cpath stroke='%23b1c8f3' d='M14 11h1'/%3E%3Cpath stroke='%23acc2f5' d='M2 12h1'/%3E%3Cpath stroke='%23c1d2fb' d='M5 12h1'/%3E%3Cpath stroke='%23bed1fc' d='M6 12h2'/%3E%3Cpath stroke='%23bdd3fb' d='M8 12h1'/%3E%3Cpath stroke='%23b6d1fb' d='M10 12h1'/%3E%3Cpath stroke='%23afccfb' d='M12 12h1'/%3E%3Cpath stroke='%23adc9f9' d='M13 12h1m-2 1h1'/%3E%3Cpath stroke='%23b1c5f3' d='M14 12h1'/%3E%3Cpath stroke='%23aac0f3' d='M2 13h1'/%3E%3Cpath stroke='%23b7cbf9' d='M3 13h1'/%3E%3Cpath stroke='%23b9cefb' d='M4 13h1'/%3E%3Cpath stroke='%23bbcef9' d='M7 13h1'/%3E%3Cpath stroke='%23b9cffb' d='M8 13h1'/%3E%3Cpath stroke='%23b2cdfb' d='M10 13h1'/%3E%3Cpath stroke='%23b0cbf9' d='M11 13h1'/%3E%3Cpath stroke='%23aec8f7' d='M13 13h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 13h1'/%3E%3Cpath stroke='%23dbe3f8' d='M2 14h1'/%3E%3Cpath stroke='%23b7c6f1' d='M3 14h1'/%3E%3Cpath stroke='%23b8c9f2' d='M4 14h1m3 0h1'/%3E%3Cpath stroke='%23b2c8f4' d='M11 14h1'/%3E%3Cpath 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			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 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 0h17m-1 1h1m-1 14h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 1h1'/%3E%3Cpath stroke='%23fff' d='M1 1h15M0 2h1m14 0h1M0 3h1m14 0h1M0 4h1m14 0h1M0 5h1m14 0h1M0 6h1m14 0h1M0 7h1m14 0h1M0 8h1m14 0h1M0 9h1m14 0h1M0 10h1m14 0h1M0 11h1m14 0h1M0 12h1m14 0h1M0 13h1m14 0h1M0 14h1m14 0h1M1 15h14'/%3E%3Cpath stroke='%23e6eefc' d='M1 2h1'/%3E%3Cpath stroke='%23d0dffc' d='M2 2h1M1 3h1'/%3E%3Cpath stroke='%23cad8f9' d='M3 2h1M1 4h1'/%3E%3Cpath stroke='%23c4d2f7' d='M4 2h1'/%3E%3Cpath stroke='%23c0d0f7' d='M5 2h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 2h1M1 7h1'/%3E%3Cpath stroke='%23bbcdf5' d='M7 2h2'/%3E%3Cpath stroke='%23b8cbf6' d='M9 2h1M1 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 2h1M1 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 2h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 2h1'/%3E%3Cpath stroke='%23afc5f4' 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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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			        11,
			        37
			    ],
			    [
			        1742471207.7606921,
			        30,
			        11,
			        37
			    ],
			    [
			        1742471209.3086836,
			        30,
			        12,
			        40
			    ],
			    [
			        1742471209.3420773,
			        30,
			        12,
			        40
			    ],
			    [
			        1742471209.5021288,
			        30,
			        12,
			        40
			    ],
			    [
			        1742471211.118764,
			        30,
			        13,
			        43
			    ],
			    [
			        1742471211.158542,
			        30,
			        13,
			        43
			    ],
			    [
			        1742471211.3419502,
			        30,
			        13,
			        43
			    ],
			    [
			        1742471212.8472564,
			        30,
			        14,
			        47
			    ],
			    [
			        1742471212.8828475,
			        30,
			        14,
			        47
			    ],
			    [
			        1742471213.043266,
			        30,
			        14,
			        47
			    ],
			    [
			        1742471214.6166058,
			        30,
			        15,
			        50
			    ],
			    [
			        1742471214.6496851,
			        30,
			        15,
			        50
			    ],
			    [
			        1742471214.8233976,
			        30,
			        15,
			        50
			    ],
			    [
			        1742471216.4602153,
			        30,
			        16,
			        53
			    ],
			    [
			        1742471216.499462,
			        30,
			        16,
			        53
			    ],
			    [
			        1742471216.6736784,
			        30,
			        16,
			        53
			    ],
			    [
			        1742471218.413812,
			        30,
			        17,
			        57
			    ],
			    [
			        1742471218.4514744,
			        30,
			        17,
			        57
			    ],
			    [
			        1742471218.636124,
			        30,
			        17,
			        57
			    ],
			    [
			        1742471220.187569,
			        30,
			        18,
			        60
			    ],
			    [
			        1742471220.2908123,
			        30,
			        18,
			        60
			    ],
			    [
			        1742471220.4652388,
			        30,
			        18,
			        60
			    ],
			    [
			        1742471222.1842725,
			        30,
			        19,
			        63
			    ],
			    [
			        1742471222.2217438,
			        30,
			        19,
			        63
			    ],
			    [
			        1742471222.3796244,
			        30,
			        19,
			        63
			    ],
			    [
			        1742471224.0996325,
			        30,
			        20,
			        67
			    ],
			    [
			        1742471224.139497,
			        30,
			        20,
			        67
			    ],
			    [
			        1742471224.3843162,
			        30,
			        20,
			        67
			    ],
			    [
			        1742471225.9920118,
			        30,
			        21,
			        70
			    ],
			    [
			        1742471226.027225,
			        30,
			        21,
			        70
			    ],
			    [
			        1742471226.218893,
			        30,
			        21,
			        70
			    ],
			    [
			        1742471227.8775997,
			        30,
			        22,
			        73
			    ],
			    [
			        1742471227.912382,
			        30,
			        22,
			        73
			    ],
			    [
			        1742471228.0776353,
			        30,
			        22,
			        73
			    ],
			    [
			        1742471229.7196693,
			        30,
			        23,
			        77
			    ],
			    [
			        1742471229.7643,
			        30,
			        23,
			        77
			    ],
			    [
			        1742471229.9640067,
			        30,
			        23,
			        77
			    ],
			    [
			        1742471231.744234,
			        30,
			        24,
			        80
			    ],
			    [
			        1742471231.784831,
			        30,
			        24,
			        80
			    ],
			    [
			        1742471232.0147297,
			        30,
			        24,
			        80
			    ],
			    [
			        1742471233.573927,
			        30,
			        25,
			        83
			    ],
			    [
			        1742471233.6070235,
			        30,
			        25,
			        83
			    ],
			    [
			        1742471233.7981687,
			        30,
			        25,
			        83
			    ],
			    [
			        1742471235.4332075,
			        30,
			        26,
			        87
			    ],
			    [
			        1742471235.474942,
			        30,
			        26,
			        87
			    ],
			    [
			        1742471235.6855981,
			        30,
			        26,
			        87
			    ],
			    [
			        1742471237.3803205,
			        30,
			        27,
			        90
			    ],
			    [
			        1742471237.4167945,
			        30,
			        27,
			        90
			    ],
			    [
			        1742471237.5758505,
			        30,
			        27,
			        90
			    ],
			    [
			        1742471239.1504097,
			        30,
			        28,
			        93
			    ],
			    [
			        1742471239.1841493,
			        30,
			        28,
			        93
			    ],
			    [
			        1742471239.3752923,
			        30,
			        28,
			        93
			    ],
			    [
			        1742471240.9162512,
			        30,
			        29,
			        97
			    ],
			    [
			        1742471240.9842167,
			        30,
			        29,
			        97
			    ],
			    [
			        1742471241.136001,
			        30,
			        29,
			        97
			    ],
			    [
			        1742471242.767457,
			        30,
			        30,
			        100
			    ],
			    [
			        1742471245.4111915,
			        30,
			        30,
			        100
			    ]
			];
			var tab_main_worker_cpu_ram_csv_json = [
			    [
			        1742403742,
			        593.5859375,
			        39.2
			    ],
			    [
			        1742403742,
			        593.62890625,
			        41.6
			    ],
			    [
			        1742403742,
			        593.72265625,
			        41.3
			    ],
			    [
			        1742403742,
			        593.72265625,
			        39.6
			    ],
			    [
			        1742403742,
			        593.72265625,
			        44.9
			    ],
			    [
			        1742403742,
			        593.72265625,
			        41
			    ],
			    [
			        1742403742,
			        593.72265625,
			        40.9
			    ],
			    [
			        1742403831,
			        609.59765625,
			        40.9
			    ],
			    [
			        1742403831,
			        609.59765625,
			        36.4
			    ],
			    [
			        1742403831,
			        609.59765625,
			        42.7
			    ],
			    [
			        1742403831,
			        609.59765625,
			        30.8
			    ],
			    [
			        1742405478,
			        672.6171875,
			        41
			    ],
			    [
			        1742405478,
			        672.6171875,
			        44.6
			    ],
			    [
			        1742405478,
			        672.6171875,
			        41.8
			    ],
			    [
			        1742405478,
			        672.6171875,
			        31.6
			    ],
			    [
			        1742408869,
			        685.765625,
			        41.7
			    ],
			    [
			        1742408869,
			        685.765625,
			        42.1
			    ],
			    [
			        1742408869,
			        685.765625,
			        42.2
			    ],
			    [
			        1742408869,
			        685.765625,
			        38.6
			    ],
			    [
			        1742410596,
			        697.62890625,
			        42
			    ],
			    [
			        1742410596,
			        697.62890625,
			        45.1
			    ],
			    [
			        1742410596,
			        697.62890625,
			        42.3
			    ],
			    [
			        1742410596,
			        697.62890625,
			        45.1
			    ],
			    [
			        1742412919,
			        720.640625,
			        42.1
			    ],
			    [
			        1742412919,
			        720.640625,
			        41.7
			    ],
			    [
			        1742412919,
			        720.640625,
			        41.9
			    ],
			    [
			        1742412919,
			        720.640625,
			        43.1
			    ],
			    [
			        1742416559,
			        743.60546875,
			        42
			    ],
			    [
			        1742416559,
			        743.609375,
			        43.8
			    ],
			    [
			        1742416559,
			        743.609375,
			        41.3
			    ],
			    [
			        1742416559,
			        743.609375,
			        45.7
			    ],
			    [
			        1742419662,
			        710.8671875,
			        42.1
			    ],
			    [
			        1742419662,
			        710.8671875,
			        43.5
			    ],
			    [
			        1742419662,
			        710.8671875,
			        41.7
			    ],
			    [
			        1742419662,
			        710.8671875,
			        43.4
			    ],
			    [
			        1742424343,
			        797.859375,
			        41.4
			    ],
			    [
			        1742424343,
			        797.859375,
			        22
			    ],
			    [
			        1742424343,
			        797.859375,
			        22.3
			    ],
			    [
			        1742424343,
			        797.859375,
			        24.4
			    ],
			    [
			        1742430367,
			        760.9609375,
			        5.1
			    ],
			    [
			        1742430368,
			        760.9609375,
			        5.7
			    ],
			    [
			        1742430368,
			        760.9609375,
			        5.9
			    ],
			    [
			        1742430368,
			        760.9609375,
			        4.3
			    ],
			    [
			        1742437541,
			        787.9296875,
			        3.6
			    ],
			    [
			        1742437541,
			        787.9296875,
			        1.1
			    ],
			    [
			        1742437541,
			        787.9296875,
			        1
			    ],
			    [
			        1742437541,
			        787.9296875,
			        0
			    ],
			    [
			        1742446445,
			        873.94921875,
			        1.3
			    ],
			    [
			        1742446445,
			        873.94921875,
			        0.1
			    ],
			    [
			        1742446445,
			        873.94921875,
			        0.5
			    ],
			    [
			        1742446445,
			        873.94921875,
			        0
			    ],
			    [
			        1742455054,
			        816.94921875,
			        0.8
			    ],
			    [
			        1742455054,
			        816.94921875,
			        0
			    ],
			    [
			        1742455054,
			        816.94921875,
			        0.2
			    ],
			    [
			        1742455054,
			        816.94921875,
			        0
			    ],
			    [
			        1742462096,
			        819.64453125,
			        0.8
			    ],
			    [
			        1742462096,
			        819.64453125,
			        0.2
			    ],
			    [
			        1742462097,
			        819.64453125,
			        0.1
			    ],
			    [
			        1742462097,
			        819.64453125,
			        0
			    ],
			    [
			        1742471248,
			        840.65625,
			        1.4
			    ],
			    [
			        1742471248,
			        840.65625,
			        0
			    ],
			    [
			        1742471248,
			        840.65625,
			        0.5
			    ],
			    [
			        1742471248,
			        840.65625,
			        0
			    ]
			];
			var tab_main_worker_cpu_ram_headers_json = [
			    "timestamp",
			    "ram_usage_mb",
			    "cpu_usage_percent"
			];
			
			"use strict";
			
			function add_default_layout_data (layout) {
				layout["width"] = get_graph_width();
				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
				}).render(document.getElementById(table_name));
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			
				colorize_table_entries();
			
				add_colorize_to_gridjs_table();
			}
			
			function download_as_file(id, filename) {
				var text = $("#" + id).text();
				var blob = new Blob([text], {
					type: "text/plain"
				});
				var link = document.createElement("a");
				link.href = URL.createObjectURL(blob);
				link.download = filename;
				document.body.appendChild(link);
				link.click();
				document.body.removeChild(link);
			}
			
			function copy_to_clipboard_from_id (id) {
				var text = $("#" + id).text();
			
				copy_to_clipboard(text);
			}
			
			function copy_to_clipboard(text) {
				if (!navigator.clipboard) {
					let textarea = document.createElement("textarea");
					textarea.value = text;
					document.body.appendChild(textarea);
					textarea.select();
					try {
						document.execCommand("copy");
					} catch (err) {
						console.error("Copy failed:", err);
					}
					document.body.removeChild(textarea);
					return;
				}
			
				navigator.clipboard.writeText(text).then(() => {
					console.log("Text copied to clipboard");
				}).catch(err => {
					console.error("Failed to copy text:", err);
				});
			}
			
			function filterNonEmptyRows(data) {
				var new_data = [];
				for (var row_idx = 0; row_idx <  data.length; row_idx++) {
					var line = data[row_idx];
			
					var line_has_empty_data = false;
			
					for (var col_idx = 0; col_idx < line.length; col_idx++) {
						var col_header_name = tab_results_headers_json[col_idx];
						var single_data_point = line[col_idx];
			
						if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
							line_has_empty_data = true;
							continue;
						}
					}
			
					if(!line_has_empty_data) {
						new_data.push(line);
					}
				}
			
				return new_data;
			}
			
			function make_text_in_parallel_plot_nicer() {
				$(".parcoords g > g > text").each(function() {
					if (theme == "dark") {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "white")
							.css("stroke", "black")
							.css("stroke-width", "2px")
							.css("paint-order", "stroke fill");
					} else {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "black")
							.css("stroke", "unset")
							.css("stroke-width", "unset")
							.css("paint-order", "stroke fill");
					}
				});
			}
			
			function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
				if ($("#parallel-plot").data("loaded") == "true") {
					return;
				}
			
				dataArray = filterNonEmptyRows(dataArray);
				const ignoreSet = new Set(ignoreColumns);
				const numericalCols = [];
				const categoricalCols = [];
				const categoryMappings = {};
			
				headers.forEach((header, colIndex) => {
					if (ignoreSet.has(header)) return;
			
					const values = dataArray.map(row => row[colIndex]);
					if (values.every(val => !isNaN(parseFloat(val)))) {
						numericalCols.push({ name: header, index: colIndex });
					} else {
						categoricalCols.push({ name: header, index: colIndex });
						const uniqueValues = [...new Set(values)];
						categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
					}
				});
			
				const dimensions = [];
			
				numericalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => parseFloat(row[col.index])),
						range: [
							Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
							Math.max(...dataArray.map(row => parseFloat(row[col.index])))
						]
					});
				});
			
				categoricalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
						tickvals: Object.values(categoryMappings[col.name]),
						ticktext: Object.keys(categoryMappings[col.name])
					});
				});
			
				let colorScale = null;
				let colorValues = null;
			
				if (resultNames.length > 1) {
					let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
					selectBox += '<option value="none">No color</option>';
					var k = 0;
					resultNames.forEach(resultName => {
						var minMax = result_min_max[k];
						if(minMax === undefined) {
							minMax = "min [automatically chosen]"
						}
						selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
						k = k + 1;
					});
					selectBox += '</select>';
					$("#parallel-plot").before(selectBox);
			
					$("#result-select").change(function() {
						const selectedResult = $(this).val();
						if (selectedResult === "none") {
							colorValues = null;
							colorScale = null;
						} else {
							const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
							colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
			
							let minResult = Math.min(...colorValues);
							let maxResult = Math.max(...colorValues);
			
							var _result_min_max_idx = result_names.indexOf(selectedResult);
			
							let invertColor = false;
							if (result_min_max.length > _result_min_max_idx) {
								invertColor = result_min_max[_result_min_max_idx] === "max";
							}
			
							colorScale = invertColor
								? [[0, 'red'], [1, 'green']]
								: [[0, 'green'], [1, 'red']];
						}
						updatePlot();
					});
				} else {
					let invertColor = false;
					if (Object.keys(result_min_max).length == 1) {
						invertColor = result_min_max[0] === "max";
					}
			
					colorScale = invertColor
						? [[0, 'red'], [1, 'green']]
						: [[0, 'green'], [1, 'red']];
			
					const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
					colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
				}
			
				function updatePlot() {
					const trace = {
						type: 'parcoords',
						dimensions: dimensions,
						line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
						unselected: {
							line: {
								color: get_text_color(),
								opacity: 0
							}
						},
					};
			
					dimensions.forEach(dim => {
						if (!dim.line) {
							dim.line = {};
						}
						if (!dim.line.color) {
							dim.line.color = 'rgba(169,169,169, 0.01)';
						}
					});
			
					Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
			
					make_text_in_parallel_plot_nicer();
				}
			
				updatePlot();
			
				$("#parallel-plot").data("loaded", "true");
			
				make_text_in_parallel_plot_nicer();
			}
			
			function plotWorkerUsage() {
				if($("#workerUsagePlot").data("loaded") == "true") {
					return;
				}
				var data = tab_worker_usage_csv_json;
				if (!Array.isArray(data) || data.length === 0) {
					console.error("Invalid or empty data provided.");
					return;
				}
			
				let timestamps = [];
				let desiredWorkers = [];
				let realWorkers = [];
			
				for (let i = 0; i < data.length; i++) {
					let entry = data[i];
			
					if (!Array.isArray(entry) || entry.length < 3) {
						console.warn("Skipping invalid entry:", entry);
						continue;
					}
			
					let unixTime = parseFloat(entry[0]);
					let desired = parseInt(entry[1], 10);
					let real = parseInt(entry[2], 10);
			
					if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
						console.warn("Skipping invalid numerical values:", entry);
						continue;
					}
			
					timestamps.push(new Date(unixTime * 1000).toISOString());
					desiredWorkers.push(desired);
					realWorkers.push(real);
				}
			
				let trace1 = {
					x: timestamps,
					y: desiredWorkers,
					mode: 'lines+markers',
					name: 'Desired Workers',
					line: {
						color: 'blue'
					}
				};
			
				let trace2 = {
					x: timestamps,
					y: realWorkers,
					mode: 'lines+markers',
					name: 'Real Workers',
					line: {
						color: 'red'
					}
				};
			
				let layout = {
					title: "Worker Usage Over Time",
					xaxis: {
						title: get_axis_title_data("Time", "date")
					},
					yaxis: {
						title: get_axis_title_data("Number of Workers")
					},
					legend: {
						x: 0,
						y: 1
					}
				};
			
				Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
				$("#workerUsagePlot").data("loaded", "true");
			}
			
			function plotCPUAndRAMUsage() {
				if($("#mainWorkerCPURAM").data("loaded") == "true") {
					return;
				}
				var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
				var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
				var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
			
				var trace1 = {
					x: timestamps,
					y: cpuUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'CPU Usage (%)',
					type: 'scatter',
					yaxis: 'y1'
				};
			
				var trace2 = {
					x: timestamps,
					y: ramUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'RAM Usage (MB)',
					type: 'scatter',
					yaxis: 'y2'
				};
			
				var layout = {
					title: 'CPU and RAM Usage Over Time',
					xaxis: {
						title: get_axis_title_data("Timestamp", "date"),
						tickmode: 'array',
						tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
						ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
						tickangle: -45
					},
					yaxis: {
						title: get_axis_title_data("CPU Usage (%)"),
						rangemode: 'tozero'
					},
					yaxis2: {
						title: get_axis_title_data("RAM Usage (MB)"),
						overlaying: 'y',
						side: 'right',
						rangemode: 'tozero'
					},
					legend: {
						x: 0.1,
						y: 0.9
					}
				};
			
				var data = [trace1, trace2];
				Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
				$("#mainWorkerCPURAM").data("loaded", "true");
			}
			
			function plotScatter2d() {
				if ($("#plotScatter2d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter2d");
			
				var minInput = document.getElementById("minValue");
				var maxInput = document.getElementById("maxValue");
			
				if (!minInput || !maxInput) {
					minInput = document.createElement("input");
					minInput.id = "minValue";
					minInput.type = "number";
					minInput.placeholder = "Min Value";
					minInput.step = "any";
			
					maxInput = document.createElement("input");
					maxInput.id = "maxValue";
					maxInput.type = "number";
					maxInput.placeholder = "Max Value";
					maxInput.step = "any";
			
					var inputContainer = document.createElement("div");
					inputContainer.style.marginBottom = "10px";
					inputContainer.appendChild(minInput);
					inputContainer.appendChild(maxInput);
					plotDiv.appendChild(inputContainer);
				}
			
				var resultSelect = document.getElementById("resultSelect");
				if (result_names.length > 1 && !resultSelect) {
					resultSelect = document.createElement("select");
					resultSelect.id = "resultSelect";
					resultSelect.style.marginBottom = "10px";
			
					var sortedResults = [...result_names].sort();
					sortedResults.forEach(result => {
						var option = document.createElement("option");
						option.value = result;
						option.textContent = result;
						resultSelect.appendChild(option);
					});
			
					var selectContainer = document.createElement("div");
					selectContainer.style.marginBottom = "10px";
					selectContainer.appendChild(resultSelect);
					plotDiv.appendChild(selectContainer);
				}
			
				minInput.addEventListener("input", updatePlots);
				maxInput.addEventListener("input", updatePlots);
				if (resultSelect) {
					resultSelect.addEventListener("change", updatePlots);
				}
			
				updatePlots();
			
				async function updatePlots() {
					var minValue = parseFloat(minInput.value);
					var maxValue = parseFloat(maxInput.value);
					if (isNaN(minValue)) minValue = -Infinity;
					if (isNaN(maxValue)) maxValue = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var selectedResult = resultSelect ? resultSelect.value : result_names[0];
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
					if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 2) {
						console.error("Not enough columns for Scatter-Plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							let xCol = numericColumns[i];
							let yCol = numericColumns[j];
			
							let xIndex = tab_results_headers_json.indexOf(xCol);
							let yIndex = tab_results_headers_json.indexOf(yCol);
			
							let data = tab_results_csv_json.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
							}));
			
							data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
							let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
							let layout = {
								title: layoutTitle,
								xaxis: {
									title: get_axis_title_data(xCol)
								},
								yaxis: {
									title: get_axis_title_data(yCol)
								},
								showlegend: false
							};
							let subDiv = document.createElement("div");
			
							let spinnerContainer = document.createElement("div");
							spinnerContainer.style.display = "flex";
							spinnerContainer.style.alignItems = "center";
							spinnerContainer.style.justifyContent = "center";
							spinnerContainer.style.width = layout.width + "px";
							spinnerContainer.style.height = layout.height + "px";
							spinnerContainer.style.position = "relative";
			
							let spinner = document.createElement("div");
							spinner.className = "spinner";
							spinner.style.width = "40px";
							spinner.style.height = "40px";
			
							let loadingText = document.createElement("span");
							loadingText.innerText = `Loading ${layoutTitle}`;
							loadingText.style.marginLeft = "10px";
			
							spinnerContainer.appendChild(spinner);
							spinnerContainer.appendChild(loadingText);
			
							plotDiv.appendChild(spinnerContainer);
			
							await new Promise(resolve => setTimeout(resolve, 50));
			
							let colors = data.map(d => {
								if (d.result === null) {
									return 'rgb(0, 0, 0)';
								} else {
									let norm = (d.result - minResult) / (maxResult - minResult);
									if (invertColor) {
										norm = 1 - norm;
									}
									return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
								}
							});
			
							let trace = {
								x: data.map(d => d.x),
								y: data.map(d => d.y),
								mode: 'markers',
								marker: {
									size: get_marker_size(),
									color: data.map(d => d.result !== null ? d.result : null),
									colorscale: invertColor ? [
										[0, 'red'],
										[1, 'green']
									] : [
										[0, 'green'],
										[1, 'red']
									],
									colorbar: {
										title: 'Result',
										tickvals: [minResult, maxResult],
										ticktext: [`${minResult}`, `${maxResult}`]
									},
									symbol: data.map(d => d.result === null ? 'x' : 'circle'),
								},
								text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
								type: 'scatter',
								showlegend: false
							};
			
							try {
								plotDiv.replaceChild(subDiv, spinnerContainer);
							} catch (err) {
								//
							}
							Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
						}
					}
				}
			
				$("#plotScatter2d").data("loaded", "true");
			}
			
			function plotScatter3d() {
				if ($("#plotScatter3d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter3d");
				if (!plotDiv) {
					console.error("Div element with id 'plotScatter3d' not found");
					return;
				}
				plotDiv.innerHTML = "";
			
				var minInput3d = document.getElementById("minValue3d");
				var maxInput3d = document.getElementById("maxValue3d");
			
				if (!minInput3d || !maxInput3d) {
					minInput3d = document.createElement("input");
					minInput3d.id = "minValue3d";
					minInput3d.type = "number";
					minInput3d.placeholder = "Min Value";
					minInput3d.step = "any";
			
					maxInput3d = document.createElement("input");
					maxInput3d.id = "maxValue3d";
					maxInput3d.type = "number";
					maxInput3d.placeholder = "Max Value";
					maxInput3d.step = "any";
			
					var inputContainer3d = document.createElement("div");
					inputContainer3d.style.marginBottom = "10px";
					inputContainer3d.appendChild(minInput3d);
					inputContainer3d.appendChild(maxInput3d);
					plotDiv.appendChild(inputContainer3d);
				}
			
				var select3d = document.getElementById("select3dScatter");
				if (result_names.length > 1 && !select3d) {
					if (!select3d) {
						select3d = document.createElement("select");
						select3d.id = "select3dScatter";
						select3d.style.marginBottom = "10px";
						select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
			
						select3d.addEventListener("change", updatePlots3d);
			
						plotDiv.appendChild(select3d);
					}
				}
			
				minInput3d.addEventListener("input", updatePlots3d);
				maxInput3d.addEventListener("input", updatePlots3d);
			
				updatePlots3d();
			
				async function updatePlots3d() {
					var selectedResult = select3d ? select3d.value : result_names[0];
					var minValue3d = parseFloat(minInput3d.value);
					var maxValue3d = parseFloat(maxInput3d.value);
			
					if (isNaN(minValue3d)) minValue3d = -Infinity;
					if (isNaN(maxValue3d)) maxValue3d = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
					if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 3) {
						console.error("Not enough columns for 3D scatter plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							for (let k = j + 1; k < numericColumns.length; k++) {
								let xCol = numericColumns[i];
								let yCol = numericColumns[j];
								let zCol = numericColumns[k];
			
								let xIndex = tab_results_headers_json.indexOf(xCol);
								let yIndex = tab_results_headers_json.indexOf(yCol);
								let zIndex = tab_results_headers_json.indexOf(zCol);
			
								let data = tab_results_csv_json.map(row => ({
									x: parseFloat(row[xIndex]),
									y: parseFloat(row[yIndex]),
									z: parseFloat(row[zIndex]),
									result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
								}));
			
								data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
								let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
								let layout = {
									title: layoutTitle,
									scene: {
										xaxis: {
											title: get_axis_title_data(xCol)
										},
										yaxis: {
											title: get_axis_title_data(yCol)
										},
										zaxis: {
											title: get_axis_title_data(zCol)
										}
									},
									showlegend: false
								};
			
								let spinnerContainer = document.createElement("div");
								spinnerContainer.style.display = "flex";
								spinnerContainer.style.alignItems = "center";
								spinnerContainer.style.justifyContent = "center";
								spinnerContainer.style.width = layout.width + "px";
								spinnerContainer.style.height = layout.height + "px";
								spinnerContainer.style.position = "relative";
			
								let spinner = document.createElement("div");
								spinner.className = "spinner";
								spinner.style.width = "40px";
								spinner.style.height = "40px";
			
								let loadingText = document.createElement("span");
								loadingText.innerText = `Loading ${layoutTitle}`;
								loadingText.style.marginLeft = "10px";
			
								spinnerContainer.appendChild(spinner);
								spinnerContainer.appendChild(loadingText);
			
								plotDiv.appendChild(spinnerContainer);
			
								await new Promise(resolve => setTimeout(resolve, 50));
			
								let colors = data.map(d => {
									if (d.result === null) {
										return 'rgb(0, 0, 0)';
									} else {
										let norm = (d.result - minResult) / (maxResult - minResult);
										if (invertColor) {
											norm = 1 - norm;
										}
										return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
									}
								});
			
								let trace = {
									x: data.map(d => d.x),
									y: data.map(d => d.y),
									z: data.map(d => d.z),
									mode: 'markers',
									marker: {
										size: get_marker_size(),
										color: data.map(d => d.result !== null ? d.result : null),
										colorscale: invertColor ? [
											[0, 'red'],
											[1, 'green']
										] : [
											[0, 'green'],
											[1, 'red']
										],
										colorbar: {
											title: 'Result',
											tickvals: [minResult, maxResult],
											ticktext: [`${minResult}`, `${maxResult}`]
										},
									},
									text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
									type: 'scatter3d',
									showlegend: false
								};
			
								let subDiv = document.createElement("div");
								try {
									plotDiv.replaceChild(subDiv, spinnerContainer);
								} catch (err) {
									//
								}
								Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
							}
						}
					}
				}
			
				$("#plotScatter3d").data("loaded", "true");
			}
			
			async function load_pareto_graph() {
				if($("#tab_pareto_fronts").data("loaded") == "true") {
					return;
				}
			
				var data = pareto_front_data;
			
				if (!data || typeof data !== "object") {
					console.error("Invalid data format for pareto_front_data");
					return;
				}
			
				if (!Object.keys(data).length) {
					console.warn("No data found in pareto_front_data");
					return;
				}
			
				let categories = Object.keys(data);
				let allMetrics = new Set();
			
				function extractMetrics(obj, prefix = "") {
					let keys = Object.keys(obj);
					for (let key of keys) {
						let newPrefix = prefix ? `${prefix} -> ${key}` : key;
						if (typeof obj[key] === "object" && !Array.isArray(obj[key])) {
							extractMetrics(obj[key], newPrefix);
						} else {
							if (!newPrefix.includes("param_dicts") && !newPrefix.includes(" -> sems -> ") && !newPrefix.includes("absolute_metrics")) {
								allMetrics.add(newPrefix);
							}
						}
					}
				}
			
				for (let cat of categories) {
					extractMetrics(data[cat]);
				}
			
				allMetrics = Array.from(allMetrics);
			
				function extractValues(obj, metricPath, values) {
					let parts = metricPath.split(" -> ");
					let data = obj;
					for (let part of parts) {
						if (data && typeof data === "object") {
							data = data[part];
						} else {
							return;
						}
					}
					if (Array.isArray(data)) {
						values.push(...data);
					}
				}
			
				let graphContainer = document.getElementById("pareto_front_graphs_container");
				graphContainer.classList.add("invert_in_dark_mode");
				graphContainer.innerHTML = "";
			
				var already_plotted = [];
			
				for (let i = 0; i < allMetrics.length; i++) {
					for (let j = i + 1; j < allMetrics.length; j++) {
						let xMetric = allMetrics[i];
						let yMetric = allMetrics[j];
			
						let xValues = [];
						let yValues = [];
			
						for (let cat of categories) {
							let metricData = data[cat];
							extractValues(metricData, xMetric, xValues);
							extractValues(metricData, yMetric, yValues);
						}
			
						xValues = xValues.filter(v => v !== undefined && v !== null);
						yValues = yValues.filter(v => v !== undefined && v !== null);
			
						let cleanXMetric = xMetric.replace(/.* -> /g, "");
						let cleanYMetric = yMetric.replace(/.* -> /g, "");
			
						let plot_key = `${cleanXMetric}-${cleanYMetric}`;
			
						if (xValues.length > 0 && yValues.length > 0 && xValues.length === yValues.length && !already_plotted.includes(plot_key)) {
							let div = document.createElement("div");
							div.id = `pareto_front_graph_${i}_${j}`;
							div.style.marginBottom = "20px";
							graphContainer.appendChild(div);
			
							let layout = {
								title: `${cleanXMetric} vs ${cleanYMetric}`,
								xaxis: {
									title: get_axis_title_data(cleanXMetric)
								},
								yaxis: {
									title: get_axis_title_data(cleanYMetric)
								},
								hovermode: "closest"
							};
			
							let trace = {
								x: xValues,
								y: yValues,
								mode: "markers",
								marker: {
									size: get_marker_size(),
								},
								type: "scatter",
								name: `${cleanXMetric} vs ${cleanYMetric}`
							};
			
							Plotly.newPlot(div.id, [trace], add_default_layout_data(layout));
			
							already_plotted.push(plot_key);
						}
					}
				}
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			
				$("#tab_pareto_fronts").data("loaded", "true");
			}
			
			async function plot_worker_cpu_ram() {
				if($("#worker_cpu_ram_pre").data("loaded") == "true") {
					return;
				}
			
				const logData = $("#worker_cpu_ram_pre").text();
				const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
			
				const hostData = {};
			
				logData.split("\n").forEach(line => {
					line = line.trim();
					const match = line.match(regex);
					if (match) {
						const timestamp = new Date(parseInt(match[1]) * 1000);
						const hostname = match[2];
						const cpu = parseFloat(match[3]);
						const ram = parseFloat(match[4]);
			
						if (!hostData[hostname]) {
							hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
						}
			
						hostData[hostname].timestamps.push(timestamp);
						hostData[hostname].cpuUsage.push(cpu);
						hostData[hostname].ramUsage.push(ram);
					}
				});
			
				if (!Object.keys(hostData).length) {
					console.log("No valid data found");
					return;
				}
			
				const container = document.getElementById("cpuRamWorkerChartContainer");
				container.innerHTML = "";
			
				var i = 1;
			
				Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
					const chartId = `workerChart_${index}`;
					const chartDiv = document.createElement("div");
					chartDiv.id = chartId;
					chartDiv.style.marginBottom = "40px";
					container.appendChild(chartDiv);
			
					const cpuTrace = {
						x: timestamps,
						y: cpuUsage,
						mode: "lines+markers",
						name: "CPU Usage (%)",
						yaxis: "y1",
						line: {
							color: "red"
						}
					};
			
					const ramTrace = {
						x: timestamps,
						y: ramUsage,
						mode: "lines+markers",
						name: "RAM Usage (MB)",
						yaxis: "y2",
						line: {
							color: "blue"
						}
					};
			
					const layout = {
						title: `Worker CPU and RAM Usage - ${hostname}`,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date")
						},
						yaxis: {
							title: get_axis_title_data("CPU Usage (%)"),
							side: "left",
							color: "red"
						},
						yaxis2: {
							title: get_axis_title_data("RAM Usage (MB)"),
							side: "right",
							overlaying: "y",
							color: "blue"
						},
						showlegend: true
					};
			
					Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
					i++;
				});
			
				$("#plot_worker_cpu_ram_button").remove();
				$("#worker_cpu_ram_pre").data("loaded", "true");
			}
			
			function load_log_file(log_nr, filename) {
				var pre_id = `single_run_${log_nr}_pre`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							if (data.data) {
								$("#" + pre_id).html(data.data);
								$("#" + pre_id).data("loaded", true);
							} else {
								log(`No 'data' key found in response.`);
							}
			
							$("#spinner_log_" + log_nr).remove();
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#spinner_log_" + log_nr).remove();
						});
				}
			}
			
			function load_debug_log () {
				var pre_id = `here_debuglogs_go`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							$("#debug_log_spinner").remove();
			
							if (data.data) {
								try {
									$("#" + pre_id).html(data.data);
								} catch (err) {
									$("#" + pre_id).text(`Error loading data: ${err}`);
								}
			
								$("#" + pre_id).data("loaded", true);
			
								if (typeof apply_theme_based_on_system_preferences === 'function') {
									apply_theme_based_on_system_preferences();
								}
							} else {
								log(`No 'data' key found in response.`);
							}
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#debug_log_spinner").remove();
						});
				}
			}
			
			function plotBoxplot() {
				if ($("#plotBoxplot").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough numeric columns for Boxplot");
					return;
				}
			
				var resultIndex = tab_results_headers_json.findIndex(function(header) {
					return result_names.includes(header.toLowerCase());
				});
				var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
				var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
				var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
				var plotDiv = document.getElementById("plotBoxplot");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'box',
						name: col,
						boxmean: 'sd',
						marker: {
							color: 'rgb(0, 255, 0)'
						},
					};
				});
			
				let layout = {
					title: 'Boxplot of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Value")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotBoxplot").data("loaded", "true");
			}
			
			function plotHeatmap() {
				if ($("#plotHeatmap").data("loaded") === "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col => {
					if (special_col_names.includes(col) || result_names.includes(col)) {
						return false;
					}
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.every(row => {
						let value = parseFloat(row[index]);
						return !isNaN(value) && isFinite(value);
					});
				});
			
				if (numericColumns.length < 2) {
					console.error("Not enough valid numeric columns for Heatmap");
					return;
				}
			
				var columnData = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.map(row => parseFloat(row[index]));
				});
			
				var dataMatrix = numericColumns.map((_, i) =>
					numericColumns.map((_, j) => {
						let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
						return values.reduce((a, b) => a + b, 0) / values.length;
					})
				);
			
				var trace = {
					z: dataMatrix,
					x: numericColumns,
					y: numericColumns,
					colorscale: 'Viridis',
					type: 'heatmap'
				};
			
				var layout = {
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				var plotDiv = document.getElementById("plotHeatmap");
				plotDiv.innerHTML = "";
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotHeatmap").data("loaded", "true");
			}
			
			function plotHistogram() {
				if ($("#plotHistogram").data("loaded") == "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Histogram");
					return;
				}
			
				var plotDiv = document.getElementById("plotHistogram");
				plotDiv.innerHTML = "";
			
				const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
			
				let traces = numericColumns.map((col, index) => {
					let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
			
					return {
						x: data,
						type: 'histogram',
						name: col,
						opacity: 0.7,
						marker: {
							color: colorPalette[index % colorPalette.length]
						},
						autobinx: true
					};
				});
			
				let layout = {
					title: 'Histogram of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Value")
					},
					yaxis: {
						title: get_axis_title_data("Frequency")
					},
					showlegend: true,
					barmode: 'overlay'
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotHistogram").data("loaded", "true");
			}
			
			function plotViolin() {
				if ($("#plotViolin").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Violin Plot");
					return;
				}
			
				var plotDiv = document.getElementById("plotViolin");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'violin',
						name: col,
						box: {
							visible: true
						},
						line: {
							color: 'rgb(0, 255, 0)'
						},
						marker: {
							color: 'rgb(0, 255, 0)'
						},
						meanline: {
							visible: true
						},
					};
				});
			
				let layout = {
					title: 'Violin Plot of Numerical Columns',
					yaxis: {
						title: get_axis_title_data("Value")
					},
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotViolin").data("loaded", "true");
			}
			
			function plotExitCodesPieChart() {
				if ($("#plotExitCodesPieChart").data("loaded") == "true") {
					return;
				}
			
				var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
			
				var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
					counts[exitCode] = (counts[exitCode] || 0) + 1;
					return counts;
				}, {});
			
				var labels = Object.keys(exitCodeCounts);
				var values = Object.values(exitCodeCounts);
			
				var plotDiv = document.getElementById("plotExitCodesPieChart");
				plotDiv.innerHTML = "";
			
				var trace = {
					labels: labels,
					values: values,
					type: 'pie',
					hoverinfo: 'label+percent',
					textinfo: 'label+value',
					marker: {
						colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
					}
				};
			
				var layout = {
					title: 'Exit Code Distribution',
					showlegend: true
				};
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotExitCodesPieChart").data("loaded", "true");
			}
			
			function plotResultEvolution() {
				if ($("#plotResultEvolution").data("loaded") == "true") {
					return;
				}
			
				result_names.forEach(resultName => {
					var relevantColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
					);
			
					var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
					var resultIndex = tab_results_headers_json.indexOf(resultName);
			
					let data = tab_results_csv_json.map(row => ({
						x: row[xColumnIndex],
						y: parseFloat(row[resultIndex])
					}));
			
					data.sort((a, b) => a.x - b.x);
			
					let xData = data.map(item => item.x);
					let yData = data.map(item => item.y);
			
					let trace = {
						x: xData,
						y: yData,
						mode: 'lines+markers',
						name: resultName,
						line: {
							shape: 'linear'
						},
						marker: {
							size: get_marker_size()
						}
					};
			
					let layout = {
						title: `Evolution of ${resultName} over time`,
						xaxis: {
							title: get_axis_title_data("Trial-Index")
						},
						yaxis: {
							title: get_axis_title_data(resultName)
						},
						showlegend: true
					};
			
					let subDiv = document.createElement("div");
					document.getElementById("plotResultEvolution").appendChild(subDiv);
			
					Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
				});
			
				$("#plotResultEvolution").data("loaded", "true");
			}
			
			function plotResultPairs() {
				if ($("#plotResultPairs").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotResultPairs");
				plotDiv.innerHTML = "";
			
				for (let i = 0; i < result_names.length; i++) {
					for (let j = i + 1; j < result_names.length; j++) {
						let xName = result_names[i];
						let yName = result_names[j];
			
						let xIndex = tab_results_headers_json.indexOf(xName);
						let yIndex = tab_results_headers_json.indexOf(yName);
			
						let data = tab_results_csv_json
							.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
							.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								status: row[tab_results_headers_json.indexOf("trial_status")]
							}));
			
						let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
			
						let trace = {
							x: data.map(d => d.x),
							y: data.map(d => d.y),
							mode: 'markers',
							marker: {
								size: get_marker_size(),
								color: colors
							},
							text: data.map(d => `Status: ${d.status}`),
							type: 'scatter',
							showlegend: false
						};
			
						let layout = {
							xaxis: {
								title: get_axis_title_data(xName)
							},
							yaxis: {
								title: get_axis_title_data(yName)
							},
							showlegend: false
						};
			
						let subDiv = document.createElement("div");
						plotDiv.appendChild(subDiv);
			
						Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
					}
				}
			
				$("#plotResultPairs").data("loaded", "true");
			}
			
			function add_up_down_arrows_for_scrolling () {
				const upArrow = document.createElement('div');
				const downArrow = document.createElement('div');
			
				const style = document.createElement('style');
				style.innerHTML = `
					.scroll-arrow {
						position: fixed;
						right: 10px;
						z-index: 100;
						cursor: pointer;
						font-size: 25px;
						display: none;
						background-color: green;
						color: white;
						padding: 5px;
						outline: 2px solid white;
						box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
						transition: background-color 0.3s, transform 0.3s;
					}
					.scroll-arrow:hover {
						background-color: darkgreen;
						transform: scale(1.1);
					}
					#up-arrow {
						top: 10px;
					}
					#down-arrow {
						bottom: 10px;
					}
				`;
				document.head.appendChild(style);
			
				upArrow.id = "up-arrow";
				upArrow.classList.add("scroll-arrow");
				upArrow.classList.add("invert_in_dark_mode");
				upArrow.innerHTML = "&#8593;";
			
				downArrow.id = "down-arrow";
				downArrow.classList.add("scroll-arrow");
				downArrow.classList.add("invert_in_dark_mode");
				downArrow.innerHTML = "&#8595;";
			
				document.body.appendChild(upArrow);
				document.body.appendChild(downArrow);
			
				function checkScrollPosition() {
					const scrollPosition = window.scrollY;
					const pageHeight = document.documentElement.scrollHeight;
					const windowHeight = window.innerHeight;
			
					if (scrollPosition > 0) {
						upArrow.style.display = "block";
					} else {
						upArrow.style.display = "none";
					}
			
					if (scrollPosition + windowHeight < pageHeight) {
						downArrow.style.display = "block";
					} else {
						downArrow.style.display = "none";
					}
				}
			
				window.addEventListener("scroll", checkScrollPosition);
			
				upArrow.addEventListener("click", function () {
					window.scrollTo({ top: 0, behavior: 'smooth' });
				});
			
				downArrow.addEventListener("click", function () {
					window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
				});
			
				checkScrollPosition();
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			}
			
			function plotGPUUsage() {
				if ($("#tab_gpu_usage").data("loaded") === "true") {
					return;
				}
			
				Object.keys(gpu_usage).forEach(node => {
					const nodeData = gpu_usage[node];
			
					var timestamps = [];
					var gpuUtilizations = [];
					var temperatures = [];
			
					nodeData.forEach(entry => {
						try {
							var timestamp = new Date(entry[0]* 1000);
							var utilization = parseFloat(entry[1]);
							var temperature = parseFloat(entry[2]);
			
							if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
								timestamps.push(timestamp);
								gpuUtilizations.push(utilization);
								temperatures.push(temperature);
							} else {
								console.warn("Invalid data point:", entry);
							}
						} catch (error) {
							console.error("Error processing GPU data entry:", error, entry);
						}
					});
			
					var trace1 = {
						x: timestamps,
						y: gpuUtilizations,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Utilization (%)',
						type: 'scatter',
						yaxis: 'y1'
					};
			
					var trace2 = {
						x: timestamps,
						y: temperatures,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Temperature (°C)',
						type: 'scatter',
						yaxis: 'y2'
					};
			
					var layout = {
						title: 'GPU Usage Over Time - ' + node,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date"),
							tickmode: 'array',
							tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
							ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
							tickangle: -45
						},
						yaxis: {
							title: get_axis_title_data("GPU Utilization (%)"),
							overlaying: 'y',
							rangemode: 'tozero'
						},
						yaxis2: {
							title: get_axis_title_data("GPU Temperature (°C)"),
							overlaying: 'y',
							side: 'right',
							position: 0.85,
							rangemode: 'tozero'
						},
						legend: {
							x: 0.1,
							y: 0.9
						}
					};
			
					var divId = 'gpu_usage_plot_' + node;
			
					if (!document.getElementById(divId)) {
						var div = document.createElement('div');
						div.id = divId;
						div.className = 'gpu-usage-plot';
						document.getElementById('tab_gpu_usage').appendChild(div);
					}
			
					var plotData = [trace1, trace2];
			
					Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
				});
			
				$("#tab_gpu_usage").data("loaded", "true");
			}
			
			function plotResultsDistributionByGenerationMethod() {
				if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
					return;
				}
			
				var res_col = result_names[0];
				var gen_method_col = "generation_method";
			
				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',  // Zeigt nur Ausreißer außerhalb der Whiskers
						jitter: 0.5,  // Erhöht die Streuung der Punkte für bessere Sichtbarkeit
						pointpos: 0   // Position der Punkte innerhalb der Box
					};
				});
			
				var layout = {
					title: 'Distribution of Results by Generation Method',
					yaxis: {
						title: get_axis_title_data(res_col)
					},
					xaxis: {
						title: "Generation Method"
					},
					boxmode: 'group'  // Gruppiert die Boxplots nach Generation Method
				};
			
				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_method"]').forEach(el => {
					let color = el.textContent.includes("Manual") ? "green" :
						el.textContent.includes("Sobol") ? "orange" :
						el.textContent.includes("SAASBO") ? "pink" :
						el.textContent.includes("Uniform") ? "lightblue" :
						el.textContent.includes("Legacy_GPEI") ? "Sienna" :
						el.textContent.includes("BO_MIXED") ? "Aqua" :
						el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
						el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
						el.textContent.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" : "";
					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';
				}
			}
			
			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();
			});
			

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

				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>10</td><td>4000</td><td></td><td>int</td><td>No         </td></tr><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int</td><td>No         </td></tr><tr><td> threshold</td><td>range</td><td>0.2</td><td>0.8</td><td></td><td>float</td><td>No         </td></tr><tr><td> outlier_detector_kwargs</td><td>fixed</td><td></td><td></td><td>\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}</td><td></td><td></td></tr></tbody></table><br><h2>Number of evaluations:</h2>
			<table>
			<tbody>
			<tr>
			<th>Failed</th>
			<th>Succeeded</th>
			<th>Running</th>
			<th>Total</th>
			</tr>
			<tr>
			<td>0</td>
			<td>418</td>
			<td>2</td>
			<td>420</td>
			</tr>
			</tbody>
			</table>
			<h2>Result names and types:</h2>
			<br><table>
			<tr><th>name</th><th>min/max</th></tr>
			<tr>
			<td>ACCURACY</td>
			<td>max</td>
			</tr>
			<tr>
			<td>RUNTIME</td>
			<td>min</td>
			</tr>
			</table><br>
			
			<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,n_samples,threshold,outlier_detector_kwargs
0,0_0,COMPLETED,Sobol,GenerationStep_0,0.594168224299065461657676223695,92.321215152740478515625000000000,2967,972,0.342867746949195861816406250000,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.566940809968847325706065021222,83.247580766677856445312500000000,382,378,0.671521485783159732818603515625,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
2,2_0,COMPLETED,Sobol,GenerationStep_0,0.618255451713395687285412805068,227.448081254959106445312500000000,1818,557,0.369925216026604219976547938131,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.615838006230529644291493696073,175.432749509811401367187500000000,3475,300,0.643793389573693364269502126263,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.563065420560747709188831322535,103.087152481079101562500000000000,3603,738,0.788945371657610028393037282513,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.610442367601246149710902955121,496.748318910598754882812500000000,1207,147,0.234156242571771144866943359375,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.571813084112149483395626248239,96.889286041259765625000000000000,745,788,0.535706744901836007244355641888,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.484934579439252333443732823071,40.491415977478027343750000000000,2089,534,0.488047182559967096526776231258,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
8,8_0,COMPLETED,Sobol,GenerationStep_0,0.539015576323987555262817750190,59.080878257751464843750000000000,2330,627,0.570513110794127031866196375631,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
9,9_0,COMPLETED,Sobol,GenerationStep_0,0.617744548286604411302391781646,187.988831043243408203125000000000,985,259,0.452682347223162695470932703756,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.577196261682243028090510961192,113.417615175247192382812500000000,1467,901,0.749327983334660663317094986269,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.613906542056074755464578629471,295.161726236343383789062500000000,3863,421,0.274368725903332277837876063131,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.603190031152648020729145628138,127.039148569107055664062500000000,3252,859,0.410146315954625639843555973130,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.611426791277258518952919530420,225.430431842803955078125000000000,1595,491,0.604185690730810298632036392519,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.484859813084112134529135573757,42.635582447052001953125000000000,108,669,0.307494287192821513787777121252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
15,15_0,COMPLETED,Sobol,GenerationStep_0,0.597769470404984404332537906157,137.837896347045898437500000000000,2693,188,0.706318157725036144256591796875,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.582579439252336461763093211630,79.623866081237792968750000000000,2627,694,0.434727592580020472112778406881,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.589968847352024927666036546725,78.748860836029052734375000000000,223,156,0.542158201336860656738281250000,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.604373831775700920410088201606,124.594799757003784179687500000000,1729,884,0.238252232223749194073292301255,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
19,19_0,COMPLETED,Sobol,GenerationStep_0,0.584785046728971913410077831941,112.623928546905517578125000000000,3065,459,0.737967515550553843084458094381,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.601757009345794346977243094443,130.309132099151611328125000000000,3951,925,0.583450269885361194610595703125,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.614118380062305346811513118155,219.546668767929077148437500000000,1358,390,0.402154779806733198022072883759,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
22,22_0,COMPLETED,Sobol,GenerationStep_0,0.556099688473520203935152039776,75.832037210464477539062500000000,842,650,0.706088223680853888097885828756,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.616012461059190052914402713213,157.244045734405517578125000000000,2491,228,0.280164561606943640637013004380,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.462105919003115261922687295737,48.220457315444946289062500000000,2233,813,0.665890045464038848876953125000,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
25,25_0,COMPLETED,Sobol,GenerationStep_0,0.590778816199376999307446567400,85.853495597839355468750000000000,584,502,0.319804265908896934167415793127,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.582554517133956339947076230601,101.819147109985351562500000000000,1120,764,0.618828265927731990814208984375,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.610105919003115282350790948840,804.305677890777587890625000000000,3712,115,0.367371874302625689434620426255,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
28,28_0,COMPLETED,Sobol,GenerationStep_0,0.610068535825545210649067939812,137.171504735946655273437500000000,3341,581,0.203477876260876655578613281250,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.335813084112149551341275355298,35.896564245223999023437500000000,2005,269,0.773355277068913160576357768150,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.596660436137071648055041350744,111.872828722000122070312500000000,1647,982,0.516733285968351663264286344202,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
31,31_0,COMPLETED,BoTorch,GenerationStep_1,0.539177570093457902977718276816,67.895174503326416015625000000000,2802,958,0.690487562789387165906873633503,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.585333333333333372117124326905,72.546201944351196289062500000000,799,929,0.279020007421068194641833315472,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
33,33_0,COMPLETED,BoTorch,GenerationStep_1,0.511239875389408071626462515269,62.100888967514038085937500000000,2545,648,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
34,34_0,COMPLETED,BoTorch,GenerationStep_1,0.611676012461059182001577028132,202.653768062591552734375000000000,1361,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.606766355140186952610292792087,133.185788154602050781250000000000,3942,1000,0.278471395686934575230964128423,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
36,36_0,COMPLETED,BoTorch,GenerationStep_1,0.386168224299065443450018619842,30.956754922866821289062500000000,10,459,0.405160471768512331358635947254,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.360199376947040517116249702667,39.983893871307373046875000000000,35,839,0.661105025509307053788177199749,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
38,38_0,COMPLETED,BoTorch,GenerationStep_1,0.529520249221183791910050331353,47.375241756439208984375000000000,2392,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
39,39_0,COMPLETED,BoTorch,GenerationStep_1,0.529557632398753863611773340381,69.817643165588378906250000000000,2565,1000,0.561165136161056787855727634451,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.598492211838006271662493418262,88.920530796051025390625000000000,2506,363,0.502477782700338937438289121928,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
41,41_0,COMPLETED,BoTorch,GenerationStep_1,0.423289719626168237098795543716,34.074578285217285156250000000000,10,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
42,42_0,COMPLETED,BoTorch,GenerationStep_1,0.336199376947040495799967629864,40.454979896545410156250000000000,19,629,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.386753894080996862836485661319,70.821104526519775390625000000000,2643,1000,0.789720780113858045190511347755,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
44,44_0,COMPLETED,BoTorch,GenerationStep_1,0.593956386292834870310741735011,81.289111375808715820312500000000,908,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
45,45_0,COMPLETED,BoTorch,GenerationStep_1,0.577956386292834856099887019809,71.173130989074707031250000000000,820,1000,0.376800867039715214890094330258,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
46,46_0,COMPLETED,BoTorch,GenerationStep_1,0.565744548286604365117113957240,80.551258802413940429687500000000,3095,1000,0.611641502645243084756998541707,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
47,47_0,COMPLETED,BoTorch,GenerationStep_1,0.324922118380062296960630874310,45.054855585098266601562500000000,61,955,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
48,48_0,COMPLETED,BoTorch,GenerationStep_1,0.614616822429906561886525651062,203.548166990280151367187500000000,4000,183,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
49,49_0,COMPLETED,BoTorch,GenerationStep_1,0.559152647975077909947572152305,85.989564895629882812500000000000,3293,1000,0.700117398599453655272384366981,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
50,50_0,COMPLETED,BoTorch,GenerationStep_1,0.607227414330218095983582315966,115.052327871322631835937500000000,3698,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
51,51_0,COMPLETED,BoTorch,GenerationStep_1,0.599800623052959447356613509328,112.610717296600341796875000000000,1617,1000,0.466976705817075454785225474552,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.559626168224299114228870166698,55.669879436492919921875000000000,2360,645,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
53,53_0,COMPLETED,BoTorch,GenerationStep_1,0.563289719626168250421471839218,64.814651489257812500000000000000,2697,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
54,54_0,COMPLETED,BoTorch,GenerationStep_1,0.605744548286604400644250745245,1577.145005941390991210937500000000,3345,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
55,55_0,COMPLETED,BoTorch,GenerationStep_1,0.576261682242990680435923422920,108.399442672729492187500000000000,890,340,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
56,56_0,COMPLETED,BoTorch,GenerationStep_1,0.603327102803738357650331636250,179.474606513977050781250000000000,1122,100,0.756371343216895142269606822083,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.559501557632398727193390186585,74.597696065902709960937500000000,419,698,0.404307841009038493318428209022,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
58,58_0,COMPLETED,BoTorch,GenerationStep_1,0.595028037383177554886515281396,108.728837966918945312500000000000,895,761,0.214579710682108404373735766058,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.593881619937694726907295716956,101.972537040710449218750000000000,2567,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
60,60_0,COMPLETED,BoTorch,GenerationStep_1,0.604747663551401859471923216915,154.260157108306884765625000000000,1669,633,0.272912334815340829052843218960,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.609906542056074751911864950671,118.173923730850219726562500000000,3573,745,0.208119174002892759478555717578,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
62,62_0,COMPLETED,BoTorch,GenerationStep_1,0.614330218068535827136145144323,264.524204969406127929687500000000,1660,293,0.271775129558475991853327968784,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
63,63_0,COMPLETED,BoTorch,GenerationStep_1,0.610093457943925221442782458325,146.754202127456665039062500000000,4000,629,0.540173341344131197061528837366,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
64,64_0,COMPLETED,BoTorch,GenerationStep_1,0.618155763239875422065949805983,94.812087059020996093750000000000,367,261,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.611800623052959458014754545729,138.593075513839721679687500000000,1793,724,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
66,66_0,COMPLETED,BoTorch,GenerationStep_1,0.595588785046728963479267804360,143.677031517028808593750000000000,4000,592,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
67,67_0,COMPLETED,BoTorch,GenerationStep_1,0.612012461059190049361689034413,139.075641632080078125000000000000,2809,315,0.379818257968042971572941723934,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
68,68_0,COMPLETED,BoTorch,GenerationStep_1,0.607713395638629250150586358359,120.572099685668945312500000000000,443,201,0.476552328499459221244904938430,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.620959501557632354007409958285,250.677344799041748046875000000000,4000,332,0.490222450817533517142265964139,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
70,70_0,COMPLETED,BoTorch,GenerationStep_1,0.610965732087227375579630006541,144.866117000579833984375000000000,3828,686,0.209689446700988796346010190064,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.568423676012461021045396591944,64.781044006347656250000000000000,364,538,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.607862928348909647979780856986,140.094424009323120117187500000000,3503,673,0.410345446663636370665528829704,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
73,73_0,COMPLETED,BoTorch,GenerationStep_1,0.613856697819314622854847129929,263.188218355178833007812500000000,1578,269,0.449584667305343133048012305153,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.614442367601246153263616633922,227.869582653045654296875000000000,1646,362,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.598791277258566956298579953000,86.488839387893676757812500000000,1209,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.625744548286604307385516676732,215.451809644699096679687500000000,366,100,0.365596373064388913132205516376,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
77,77_0,COMPLETED,BoTorch,GenerationStep_1,0.598018691588785067381195403868,89.849658727645874023437500000000,762,607,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
78,78_0,COMPLETED,BoTorch,GenerationStep_1,0.581869158878504655341146190040,102.794636011123657226562500000000,4000,735,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.608959501557632343349268921884,122.709741592407226562500000000000,3528,821,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
80,80_0,COMPLETED,BoTorch,GenerationStep_1,0.609059190031152608568731920968,161.629328250885009765625000000000,1386,303,0.611072872904515396186297948589,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
81,81_0,COMPLETED,BoTorch,GenerationStep_1,0.611775700934579447221040027216,227.359496831893920898437500000000,2992,289,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.616224299065420533239034739381,204.461009740829467773437500000000,1619,532,0.309303803788729514412381149668,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
83,83_0,COMPLETED,BoTorch,GenerationStep_1,0.615165109034267909571269683511,357.875272035598754882812500000000,4000,316,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.607676012461059178448863349331,202.745043277740478515625000000000,1777,582,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
85,85_0,COMPLETED,BoTorch,GenerationStep_1,0.620099688473520260778570900584,217.050957918167114257812500000000,3950,553,0.437139445176144225690961775399,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
86,86_0,COMPLETED,BoTorch,GenerationStep_1,0.609794392523364536806695923588,190.713186264038085937500000000000,1593,697,0.345938463960496211946349376376,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.604984423676012461612572224112,155.913459062576293945312500000000,3836,364,0.738757666633895082597405234992,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
88,88_0,COMPLETED,BoTorch,GenerationStep_1,0.607551401869158902435685831733,126.689688205718994140625000000000,3795,775,0.432651601860226941997922267547,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
89,89_0,COMPLETED,BoTorch,GenerationStep_1,0.593545171339563859547183710674,102.098168849945068359375000000000,2897,640,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
90,90_0,COMPLETED,BoTorch,GenerationStep_1,0.525557632398753860059059661580,62.619399785995483398437500000000,2372,477,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.611352024922118375549473512365,198.281317949295043945312500000000,3356,390,0.498350494372587771163551906284,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
92,92_0,COMPLETED,BoTorch,GenerationStep_1,0.590180685358255407990668572893,79.390859127044677734375000000000,2415,502,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
93,93_0,RUNNING,BoTorch,GenerationStep_1,,,1996,888,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.599414330218068558409072466020,110.292725086212158203125000000000,3407,394,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
95,95_0,COMPLETED,BoTorch,GenerationStep_1,0.560373831775700881330237734801,77.178752183914184570312500000000,2430,203,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
96,96_0,COMPLETED,BoTorch,GenerationStep_1,0.588286604361370701887778977834,128.425424337387084960937500000000,2897,352,0.662347541005741335951029213902,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.500398753894080949855549533822,42.136502504348754882812500000000,249,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
98,98_0,COMPLETED,BoTorch,GenerationStep_1,0.555638629283489060561862515897,55.237645864486694335937500000000,2176,434,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
99,99_0,COMPLETED,BoTorch,GenerationStep_1,0.493532710280373820843635712663,61.155821084976196289062500000000,2273,425,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
100,100_0,COMPLETED,BoTorch,GenerationStep_1,0.612149532710280386282875042525,133.822652101516723632812500000000,1135,595,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
101,101_0,COMPLETED,BoTorch,GenerationStep_1,0.621420560747663497380699482164,160.941722631454467773437500000000,3188,401,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
102,102_0,COMPLETED,BoTorch,GenerationStep_1,0.544585669781931458466317508282,59.013383388519287109375000000000,2476,861,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
103,103_0,COMPLETED,BoTorch,GenerationStep_1,0.451252336448598134754917055034,55.949326038360595703125000000000,507,962,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.620660436137071669371323423547,242.226751089096069335937500000000,3374,393,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
105,105_0,COMPLETED,BoTorch,GenerationStep_1,0.501445482866043623637608561694,43.882630109786987304687500000000,98,269,0.687679683183822554326525278157,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
106,106_0,COMPLETED,BoTorch,GenerationStep_1,0.465532710280373851485791192317,45.031818866729736328125000000000,2148,873,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
107,107_0,COMPLETED,BoTorch,GenerationStep_1,0.493607476635514019758232961976,76.104925632476806640625000000000,1059,938,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
108,108_0,COMPLETED,BoTorch,GenerationStep_1,0.484299065420560725936383050794,40.302168607711791992187500000000,249,907,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
109,109_0,COMPLETED,BoTorch,GenerationStep_1,0.596249221183800637291483326408,78.131663799285888671875000000000,2507,517,0.341302810071115736434421705781,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
110,110_0,COMPLETED,BoTorch,GenerationStep_1,0.606193146417445483109531778609,135.242974996566772460937500000000,1953,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.611102803738317712500816014654,294.383882522583007812500000000000,1995,391,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.618230529595015565469395824039,155.883683919906616210937500000000,1410,609,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
113,113_0,COMPLETED,BoTorch,GenerationStep_1,0.573495327102803709173883817130,65.625198364257812500000000000000,2415,503,0.571963925915745519112931560812,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
114,114_0,COMPLETED,BoTorch,GenerationStep_1,0.545943925233644877792471561406,75.622352838516235351562500000000,2952,721,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
115,115_0,COMPLETED,BoTorch,GenerationStep_1,0.567713395638629325645752032869,85.449583292007446289062500000000,2329,145,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.578890965732087203754474558082,102.810876607894897460937500000000,2901,337,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.529856697819314659270162337634,43.772249937057495117187500000000,2117,390,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
118,118_0,COMPLETED,BoTorch,GenerationStep_1,0.605894080996884687451142781356,82.896193265914916992187500000000,2567,517,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
119,119_0,COMPLETED,BoTorch,GenerationStep_1,0.607750778816199321852309367387,173.945318460464477539062500000000,3280,379,0.411907527653625704644468896731,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.546554517133956418994955583912,81.883142471313476562500000000000,3063,608,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.497632398753894089615812390548,55.357507228851318359375000000000,271,538,0.760754933444752534654753617360,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.534068535825545143147508042603,37.390493154525756835937500000000,31,193,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
123,123_0,COMPLETED,BoTorch,GenerationStep_1,0.595626168224299035180990813387,807.970016241073608398437500000000,1906,100,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
124,124_0,COMPLETED,BoTorch,GenerationStep_1,0.616922118380062278752973270457,204.744638442993164062500000000000,2467,100,0.405627868184674889029395217221,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
125,125_0,COMPLETED,BoTorch,GenerationStep_1,0.580498442367601286129286108917,58.810986518859863281250000000000,215,385,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
126,126_0,COMPLETED,BoTorch,GenerationStep_1,0.615700934579439307370307687961,128.200893402099609375000000000000,611,333,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
127,127_0,COMPLETED,BoTorch,GenerationStep_1,0.467052959501557618526845772067,50.879051446914672851562500000000,354,600,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
128,128_0,COMPLETED,BoTorch,GenerationStep_1,0.594766355140186941952151755686,110.411485671997070312500000000000,1269,887,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.596623052959501576353318341717,94.955923080444335937500000000000,1047,890,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.598043613707165078174909922382,85.054559707641601562500000000000,2825,812,0.403552479699164134974154194424,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.412585669781931452249068570381,50.601768732070922851562500000000,301,833,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
132,132_0,COMPLETED,BoTorch,GenerationStep_1,0.596062305295950167760565818753,115.284330844879150390625000000000,3149,715,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
133,133_0,COMPLETED,BoTorch,GenerationStep_1,0.535750778816199368925765611493,68.168558835983276367187500000000,221,211,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
134,134_0,COMPLETED,BoTorch,GenerationStep_1,0.578479750778816192990916533745,153.580335855484008789062500000000,3262,655,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.500947040498442408562596028787,42.235239028930664062500000000000,124,566,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
136,136_0,COMPLETED,BoTorch,GenerationStep_1,0.525208722741433042813241627300,64.011073350906372070312500000000,2750,822,0.790458262327147775927471684554,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.525096573208722716685770137701,52.030102252960205078125000000000,2321,752,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
138,138_0,COMPLETED,BoTorch,GenerationStep_1,0.550803738317756974574024297908,78.566580533981323242187500000000,894,531,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
139,139_0,COMPLETED,BoTorch,GenerationStep_1,0.610317757009345762675422975008,120.034966707229614257812500000000,781,418,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.612137071651090325374866552011,183.827342987060546875000000000000,991,477,0.231711801130511485524721138063,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
141,141_0,COMPLETED,BoTorch,GenerationStep_1,0.523601246105918960438430076465,48.500126838684082031250000000000,72,340,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
142,142_0,COMPLETED,BoTorch,GenerationStep_1,0.606990654205607493842933308770,116.193786144256591796875000000000,2944,548,0.432319586979741210530647776977,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
143,143_0,COMPLETED,BoTorch,GenerationStep_1,0.460635514018691571980212984272,59.019384145736694335937500000000,2332,921,0.744397974141546847626216276694,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.502741433021806849446022624761,72.962947845458984375000000000000,3667,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
145,145_0,COMPLETED,BoTorch,GenerationStep_1,0.597482866043613669582157399418,109.611274719238281250000000000000,2991,760,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
146,146_0,COMPLETED,BoTorch,GenerationStep_1,0.530890965732087272144212874991,77.708808898925781250000000000000,3467,895,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.607663551401869117540854858817,141.379616498947143554687500000000,4000,813,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
148,148_0,COMPLETED,BoTorch,GenerationStep_1,0.591302180685358225176173618820,103.429619550704956054687500000000,3263,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
149,149_0,COMPLETED,BoTorch,GenerationStep_1,0.534380062305295999713905530371,56.795593976974487304687500000000,2455,919,0.470625491840151233269295971695,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.532809968847351989040816988563,72.891510009765625000000000000000,2321,303,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.533919003115264745318313543976,60.847825527191162109375000000000,491,984,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.509457943925233691651044409809,45.172652006149291992187500000000,222,771,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.604249221183800644396910684009,97.359818220138549804687500000000,2450,395,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.601482866043613673134871078219,135.297857046127319335937500000000,1591,304,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.548149532710280329439456181717,37.196557283401489257812500000000,10,100,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
156,156_0,COMPLETED,BoTorch,GenerationStep_1,0.608461059190031128274256388977,132.784512996673583984375000000000,1853,846,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.307003115264797510342020814278,26.873469829559326171875000000000,2003,796,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.619028037383177576202797354199,93.348619222640991210937500000000,289,240,0.351345886869830259513491910184,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.548436137071651064189836688456,50.400486469268798828125000000000,206,477,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.555426791277258580237230489729,67.380532503128051757812500000000,506,829,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
161,161_0,COMPLETED,BoTorch,GenerationStep_1,0.603576323987538909676686671446,135.898792505264282226562500000000,1714,324,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
162,162_0,COMPLETED,BoTorch,GenerationStep_1,0.540635514018691587523335329024,69.803851127624511718750000000000,2426,276,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
163,163_0,COMPLETED,BoTorch,GenerationStep_1,0.626417445482866042105740689294,139.178592443466186523437500000000,299,147,0.349561985274224051867264506654,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.477993769470405005517221752598,87.205027341842651367187500000000,1297,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
165,165_0,COMPLETED,BoTorch,GenerationStep_1,0.439987538940809941312437558736,41.948589086532592773437500000000,10,267,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
166,166_0,COMPLETED,BoTorch,GenerationStep_1,0.566105919003115243270940482034,119.460806608200073242187500000000,652,255,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.423364485981308436013392793029,49.891833543777465820312500000000,85,924,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
168,168_0,COMPLETED,BoTorch,GenerationStep_1,0.613408099688473540389566096565,213.306993246078491210937500000000,750,283,0.451844087447625941678097660770,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.567788161993769469049198050925,98.626231431961059570312500000000,1240,621,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
170,170_0,COMPLETED,BoTorch,GenerationStep_1,0.443090342679127724423437939549,40.022737979888916015625000000000,10,184,0.565660201748639623886560912069,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.563813084112149587312501353153,80.920474052429199218750000000000,646,490,0.775479024000146122119758729241,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
172,172_0,COMPLETED,BoTorch,GenerationStep_1,0.590803738317757010101161085913,104.506605625152587890625000000000,746,175,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
173,173_0,COMPLETED,BoTorch,GenerationStep_1,0.506940809968847383437662301731,48.915704250335693359375000000000,331,943,0.457883264028535075240711194056,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.499750778816199392462493733547,62.856350421905517578125000000000,2447,785,0.744744189997169958594724903378,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
175,175_0,COMPLETED,BoTorch,GenerationStep_1,0.559975077881619931474688200979,62.362980365753173828125000000000,628,914,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
176,176_0,COMPLETED,BoTorch,GenerationStep_1,0.447140186915887860585883117892,36.014861345291137695312500000000,105,807,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.516934579439252361865442253475,54.352720499038696289062500000000,2249,316,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
178,178_0,COMPLETED,BoTorch,GenerationStep_1,0.613545171339563877310752104677,128.451213836669921875000000000000,1507,283,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.449619937694704041586390985685,48.824426651000976562500000000000,2267,638,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
180,180_0,RUNNING,BoTorch,GenerationStep_1,,,2297,534,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.573146417445482891928065782849,70.034999370574951171875000000000,412,633,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.602629283489096612136393105175,81.870017766952514648437500000000,349,406,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.624685358255451683717751620861,323.023131370544433593750000000000,322,153,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
184,184_0,COMPLETED,BoTorch,GenerationStep_1,0.583862928348909626663498784183,66.326079607009887695312500000000,2337,472,0.351500103668024310721307301719,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.605433021806853544077853257477,104.204463720321655273437500000000,1602,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
186,186_0,COMPLETED,BoTorch,GenerationStep_1,0.595999999999999974242825828696,107.691144466400146484375000000000,3458,952,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.567688473520249203829735051841,68.285808086395263671875000000000,2627,940,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
188,188_0,COMPLETED,BoTorch,GenerationStep_1,0.493894080996884754508613468715,44.247889280319213867187500000000,2187,756,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
189,189_0,COMPLETED,BoTorch,GenerationStep_1,0.556722741433021806045644552796,90.646646976470947265625000000000,1706,760,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.613906542056074755464578629471,136.833802461624145507812500000000,2710,323,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.561769470404984372358114796953,85.978387355804443359375000000000,1842,815,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.534280373831775734494442531286,57.996409177780151367187500000000,252,649,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
193,193_0,COMPLETED,BoTorch,GenerationStep_1,0.622068535825545221307208976214,82.039481878280639648437500000000,321,314,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.485570093457943940951082595348,74.225481033325195312500000000000,792,905,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
195,195_0,COMPLETED,BoTorch,GenerationStep_1,0.589246105919003060336081034620,118.209077596664428710937500000000,1158,229,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
196,196_0,COMPLETED,BoTorch,GenerationStep_1,0.583588785046728952821126767958,74.628013134002685546875000000000,2789,887,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
197,197_0,COMPLETED,BoTorch,GenerationStep_1,0.563750778816199393794761363097,96.390460968017578125000000000000,1879,931,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.531813084112149558890791922749,70.658726692199707031250000000000,2463,937,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
199,199_0,COMPLETED,BoTorch,GenerationStep_1,0.491663551401869181045611867376,54.070038557052612304687500000000,236,426,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.546218068535825551634843577631,57.646132707595825195312500000000,253,501,0.443439147064031702338837703792,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.531028037383177609065398883104,83.200114965438842773437500000000,2816,626,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
202,202_0,COMPLETED,BoTorch,GenerationStep_1,0.597445482866043597880434390390,129.452486038208007812500000000000,1118,696,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
203,203_0,COMPLETED,BoTorch,GenerationStep_1,0.516897196261682290163719244447,45.930060148239135742187500000000,2210,665,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.605271028037383196362952730851,125.857437610626220703125000000000,3692,324,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
205,205_0,COMPLETED,BoTorch,GenerationStep_1,0.496037383177570068149009330227,86.172241210937500000000000000000,1790,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.596535825545171372041863833147,78.432355165481567382812500000000,227,303,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
207,207_0,COMPLETED,BoTorch,GenerationStep_1,0.526242990654205655687292164657,77.636217355728149414062500000000,3139,848,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.546965732087227429758513608249,99.205506801605224609375000000000,3848,846,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.448112149532710279942193665192,65.019802093505859375000000000000,2283,542,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.577320872274143304103688478790,95.334122419357299804687500000000,359,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.507327102803738272385203345038,35.960966825485229492187500000000,10,100,0.459258744842787836049069483124,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.613333333333333285963817615993,139.660343647003173828125000000000,2547,212,0.456307772865202798673323059120,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
213,213_0,COMPLETED,BoTorch,GenerationStep_1,0.505370716510903372764573759923,76.633270263671875000000000000000,3069,912,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.561433021806853616020305253187,82.440616846084594726562500000000,179,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
215,215_0,COMPLETED,BoTorch,GenerationStep_1,0.593408099688473522625997702562,66.088809490203857421875000000000,262,315,0.431235036504850954841572274745,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
216,216_0,COMPLETED,BoTorch,GenerationStep_1,0.614043613707165092385764637584,86.998233795166015625000000000000,450,385,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
217,217_0,COMPLETED,BoTorch,GenerationStep_1,0.519937694704049824245828403946,62.336505174636840820312500000000,340,850,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.547588785046728920846703658754,48.434565544128417968750000000000,150,347,0.446694810415362575373166009740,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
219,219_0,COMPLETED,BoTorch,GenerationStep_1,0.621096573208722690928595966398,237.288009405136108398437500000000,2585,171,0.329588359691842303078601617017,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.480822429906542059274698885929,54.164271116256713867187500000000,2209,734,0.488594515622347125294311354082,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.569919003115264777292736653180,63.325986146926879882812500000000,210,297,0.518232513952810447399599524942,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
222,222_0,COMPLETED,BoTorch,GenerationStep_1,0.540747663551401913650806818623,57.324341535568237304687500000000,522,1000,0.485965559878660857684451457317,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.613495327102803744701020605135,89.748650550842285156250000000000,323,328,0.363674520660706102059833710882,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
224,224_0,COMPLETED,BoTorch,GenerationStep_1,0.602292834890965744776281098893,113.687280178070068359375000000000,416,293,0.341002956197445250552391371457,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
225,225_0,COMPLETED,BoTorch,GenerationStep_1,0.516859813084112107439693772903,80.744334220886230468750000000000,631,585,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
226,226_0,COMPLETED,BoTorch,GenerationStep_1,0.600560747663551386388292030460,147.198724269866943359375000000000,3075,606,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
227,227_0,COMPLETED,BoTorch,GenerationStep_1,0.580074766355140214457719594066,91.996246576309204101562500000000,457,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
228,228_0,COMPLETED,BoTorch,GenerationStep_1,0.589545171339563855994470031874,107.077766656875610351562500000000,631,707,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
229,229_0,COMPLETED,BoTorch,GenerationStep_1,0.600137071651090314716725515609,163.008021593093872070312500000000,380,100,0.500212255777559011704624936101,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.558579439252336440446811138827,67.456875562667846679687500000000,272,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
231,231_0,COMPLETED,BoTorch,GenerationStep_1,0.612236760124610590594329551095,126.188194274902343750000000000000,2552,325,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
232,232_0,COMPLETED,BoTorch,GenerationStep_1,0.576211838006230547826191923377,107.514137268066406250000000000000,1762,666,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.592211838006230562037046638579,84.570489883422851562500000000000,2405,432,0.332502619802190924058749033065,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
234,234_0,COMPLETED,BoTorch,GenerationStep_1,0.536747663551401910098093139823,106.542547225952148437500000000000,2424,800,0.364981904321713901406809554828,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
235,235_0,COMPLETED,BoTorch,GenerationStep_1,0.524610591900311562518766095309,63.864136695861816406250000000000,299,311,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
236,236_0,COMPLETED,BoTorch,GenerationStep_1,0.576398753894081017357109431032,107.009799480438232421875000000000,2497,184,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
237,237_0,COMPLETED,BoTorch,GenerationStep_1,0.611576323987538916782114029047,200.367521286010742187500000000000,3089,361,0.459691831852062637864264615928,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.609258566978193139007657919137,186.141781091690063476562500000000,3637,636,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
239,239_0,COMPLETED,BoTorch,GenerationStep_1,0.533183800623052928102652003872,78.733082294464111328125000000000,1032,679,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
240,240_0,COMPLETED,BoTorch,GenerationStep_1,0.488922118380062331599589242614,53.613588809967041015625000000000,242,709,0.544972613826212648469038413168,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.572834890965732035361668295081,71.596644639968872070312500000000,2384,570,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.603227414330218092430868637166,136.624485969543457031250000000000,911,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.543725856697819365237478450581,63.956046819686889648437500000000,261,577,0.444618488901757535902703466490,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
244,244_0,COMPLETED,BoTorch,GenerationStep_1,0.520124610591900293776745911600,80.762145996093750000000000000000,4000,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
245,245_0,COMPLETED,BoTorch,GenerationStep_1,0.612822429906542009980796592572,145.042798757553100585937500000000,4000,736,0.369611455329216465059971596929,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
246,246_0,COMPLETED,BoTorch,GenerationStep_1,0.465781931464174459023297458771,64.008254051208496093750000000000,896,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.572510903426791228909564779315,86.466355800628662109375000000000,2771,476,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.606542056074766300355349812889,291.206306219100952148437500000000,4000,263,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.583339563862928400794771732762,68.420628786087036132812500000000,2467,578,0.311235294742350232510830210231,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
250,250_0,COMPLETED,BoTorch,GenerationStep_1,0.557370716510903418949851584330,88.590406417846679687500000000000,3483,536,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
251,251_0,COMPLETED,BoTorch,GenerationStep_1,0.612087227414330192765135052468,214.863437652587890625000000000000,3561,351,0.541414135131038642967382656934,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
252,252_0,COMPLETED,BoTorch,GenerationStep_1,0.560473520249221146549700733885,61.804242372512817382812500000000,2345,571,0.384839536295301209989361268526,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.542355140186915885003315906943,69.022605657577514648437500000000,2276,581,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
254,254_0,COMPLETED,BoTorch,GenerationStep_1,0.504485981308411268742020183709,51.241711139678955078125000000000,197,596,0.462320115633080674921728814297,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.524461059190031164689571596682,69.571904659271240234375000000000,786,1000,0.655249034309900446615415603446,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
256,256_0,COMPLETED,BoTorch,GenerationStep_1,0.635314641744548325164032576140,192.647011518478393554687500000000,260,100,0.289724351645232802709983843670,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
257,257_0,COMPLETED,BoTorch,GenerationStep_1,0.605183800623052992051498222281,134.560675144195556640625000000000,3816,885,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.593657320872274185674655200273,114.351446628570556640625000000000,949,608,0.324953105291823751699098465906,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
259,259_0,COMPLETED,BoTorch,GenerationStep_1,0.587003115264797536987373405282,79.312969923019409179687500000000,664,601,0.419552978851641111823767005262,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
260,260_0,COMPLETED,BoTorch,GenerationStep_1,0.607277258566978228593313815509,167.322135448455810546875000000000,4000,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
261,261_0,COMPLETED,BoTorch,GenerationStep_1,0.473370716510903399854015560777,38.870448350906372070312500000000,60,482,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
262,262_0,COMPLETED,BoTorch,GenerationStep_1,0.490903426791277242013933346243,36.154349565505981445312500000000,10,157,0.423978139372713391797020676677,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
263,263_0,COMPLETED,BoTorch,GenerationStep_1,0.585919003115264791503591368382,81.636458635330200195312500000000,2888,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
264,264_0,COMPLETED,BoTorch,GenerationStep_1,0.586230529595015537047686393635,81.639412403106689453125000000000,2347,367,0.450304050516430964812286674714,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
265,265_0,COMPLETED,BoTorch,GenerationStep_1,0.580760124610591899063649634627,83.417486429214477539062500000000,1114,1000,0.516772239419017620498664200568,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
266,266_0,COMPLETED,BoTorch,GenerationStep_1,0.617333333333333289516531294794,200.560345172882080078125000000000,1165,352,0.411347204054655746219282264065,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.527713395638629290118615244864,62.710897445678710937500000000000,463,902,0.390002343171098997398615892962,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
268,268_0,COMPLETED,BoTorch,GenerationStep_1,0.536523364485981257843150160625,72.222500801086425781250000000000,346,209,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
269,269_0,COMPLETED,BoTorch,GenerationStep_1,0.469794392523364467972868396828,72.471201896667480468750000000000,2913,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.531264797507788211206047890300,56.074942111968994140625000000000,385,781,0.458865685420382030113017890471,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.610218068535825497455959975923,90.126417160034179687500000000000,306,284,0.340725039140024277273255393084,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.593981308411214992126758716040,80.863939523696899414062500000000,2718,736,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.518218068535825526765847826027,47.852306365966796875000000000000,138,467,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.607227414330218095983582315966,121.301709890365600585937500000000,1698,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.604174454828660389971162203437,661.849863052368164062500000000000,4000,100,0.401031728537665110856380579207,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
276,276_0,COMPLETED,BoTorch,GenerationStep_1,0.609171339563862934696203410567,124.008004426956176757812500000000,2688,309,0.452976258960111177742646759725,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
277,277_0,COMPLETED,BoTorch,GenerationStep_1,0.482791277258566964292185730301,59.149384021759033203125000000000,575,737,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
278,278_0,COMPLETED,BoTorch,GenerationStep_1,0.567090342679127723535259519849,87.463060855865478515625000000000,1081,561,0.789263825009889008477159677568,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
279,279_0,COMPLETED,BoTorch,GenerationStep_1,0.576074766355140210905005915265,55.030663490295410156250000000000,133,283,0.307781463533581545188866357421,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
280,280_0,COMPLETED,BoTorch,GenerationStep_1,0.592112149532710296817583639495,100.012095212936401367187500000000,1357,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.585619937694703995845202371129,138.695974349975585937500000000000,2720,612,0.330199515295741707898713457325,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
282,282_0,COMPLETED,BoTorch,GenerationStep_1,0.523501557632398806241269539896,79.270525932312011718750000000000,1107,866,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
283,283_0,COMPLETED,BoTorch,GenerationStep_1,0.497931464174454829763050156544,55.243151664733886718750000000000,143,503,0.556097890740887113025792132248,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.594803738317757013653874764714,139.623193502426147460937500000000,3739,516,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.564323987538940863295522376575,68.983353137969970703125000000000,691,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.605707165109034217920225273701,114.472135066986083984375000000000,2767,443,0.329755598878790967543039869270,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
287,287_0,COMPLETED,BoTorch,GenerationStep_1,0.606803738317757024312015801115,127.363121986389160156250000000000,2446,312,0.356381881575412839957550659165,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
288,288_0,COMPLETED,BoTorch,GenerationStep_1,0.497183800623052951639380125926,70.667373180389404296875000000000,2746,735,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
289,289_0,COMPLETED,BoTorch,GenerationStep_1,0.451651090342679140121617820114,51.483342409133911132812500000000,2318,1000,0.658802688861726326585710467043,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
290,290_0,COMPLETED,BoTorch,GenerationStep_1,0.596448598130841167730409324577,98.958578586578369140625000000000,1239,958,0.367387086066486123758778603587,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
291,291_0,COMPLETED,BoTorch,GenerationStep_1,0.592186915887850440221029657550,105.943829774856567382812500000000,1936,729,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
292,292_0,COMPLETED,BoTorch,GenerationStep_1,0.578467289719626132082908043230,71.369061231613159179687500000000,327,461,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
293,293_0,COMPLETED,BoTorch,GenerationStep_1,0.606442367601246146158189276321,113.211314439773559570312500000000,2924,488,0.342685933057181313365902042278,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
294,294_0,COMPLETED,BoTorch,GenerationStep_1,0.552672897196261669883199374453,48.998944520950317382812500000000,89,263,0.409057466578795714262639648950,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
295,295_0,COMPLETED,BoTorch,GenerationStep_1,0.551713395638629311434897317667,57.509821414947509765625000000000,2519,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
296,296_0,COMPLETED,BoTorch,GenerationStep_1,0.602454828660436092491181625519,129.534694910049438476562500000000,3135,532,0.406311945902573690148074092576,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.591364485981308418693913608877,92.374979019165039062500000000000,957,932,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
298,298_0,COMPLETED,BoTorch,GenerationStep_1,0.613084112149532733937462580798,181.370923042297363281250000000000,2853,427,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
299,299_0,COMPLETED,BoTorch,GenerationStep_1,0.530342679127725813437166380027,64.298825740814208984375000000000,2615,565,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
300,300_0,COMPLETED,BoTorch,GenerationStep_1,0.599264797507788160579877967393,132.030738592147827148437500000000,1199,604,0.437018302305071659752400137222,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
301,301_0,COMPLETED,BoTorch,GenerationStep_1,0.540137071651090372448322796117,89.434435844421386718750000000000,1485,811,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
302,302_0,COMPLETED,BoTorch,GenerationStep_1,0.593607476635514053064923700731,117.624207973480224609375000000000,3359,1000,0.359011006475181970731824776522,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.577794392523364508384986493184,52.045125484466552734375000000000,36,100,0.451607402042192629387784563733,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
304,304_0,COMPLETED,BoTorch,GenerationStep_1,0.613881619937694744670864110958,148.188437700271606445312500000000,1658,647,0.502680385829774256656321540504,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.537096573208722727343911174103,86.232491493225097656250000000000,1450,748,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
306,306_0,COMPLETED,BoTorch,GenerationStep_1,0.515987538940809953302846224688,49.526621818542480468750000000000,338,872,0.386239419288193275292542239185,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
307,307_0,COMPLETED,BoTorch,GenerationStep_1,0.603838006230529633633352659672,107.370978355407714843750000000000,807,483,0.397677508743211283182006354764,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.555538940809968795342399516812,61.015882968902587890625000000000,2321,523,0.497617157073691362967338136514,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.579838006230529612317070586869,70.352559089660644531250000000000,2360,465,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
310,310_0,COMPLETED,BoTorch,GenerationStep_1,0.604635514018691533344451727316,164.257503271102905273437500000000,766,100,0.675162615248077635499157622689,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.618866043613707117465594365058,220.562660217285156250000000000000,3781,324,0.600967227113870849919408101414,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
312,312_0,COMPLETED,BoTorch,GenerationStep_1,0.596012461059190035150834319211,124.897152423858642578125000000000,2991,913,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.525146417445482849295501637243,51.260271072387695312500000000000,121,390,0.281682186066157247417152120761,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
314,314_0,COMPLETED,BoTorch,GenerationStep_1,0.604598130841121461642728718289,145.288395881652832031250000000000,1707,816,0.404285516752445728272391534119,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.620348909657320923827228398295,145.150976657867431640625000000000,239,100,0.398002197945332591988432113794,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.597420560747663587086719871877,87.740265846252441406250000000000,553,546,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
317,317_0,COMPLETED,BoTorch,GenerationStep_1,0.583638629283489085430858267500,70.794294118881225585937500000000,209,258,0.473517997363840592583983379882,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.578355140186915916977739016147,128.814684152603149414062500000000,2450,100,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
319,319_0,COMPLETED,BoTorch,GenerationStep_1,0.575401869158878476184781902703,66.400536775588989257812500000000,2658,908,0.461786659113913866558931431427,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
320,320_0,COMPLETED,BoTorch,GenerationStep_1,0.610915887850467242969898506999,141.755109786987304687500000000000,3874,756,0.286338394245416738570497727778,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
321,321_0,COMPLETED,BoTorch,GenerationStep_1,0.607838006230529637186066338472,152.178329229354858398437500000000,3841,649,0.463083223148618217113181572131,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
322,322_0,COMPLETED,BoTorch,GenerationStep_1,0.598716510903426812895133934944,128.922065496444702148437500000000,3582,1000,0.370453733090281733808524222695,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
323,323_0,COMPLETED,BoTorch,GenerationStep_1,0.607688473520249239356871839846,122.752876281738281250000000000000,1408,628,0.443836160986299133046628639931,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
324,324_0,COMPLETED,BoTorch,GenerationStep_1,0.617906542056074759017292308272,136.605423450469970703125000000000,1230,542,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
325,325_0,COMPLETED,BoTorch,GenerationStep_1,0.606442367601246146158189276321,119.937241792678833007812500000000,1907,884,0.557547076716493283932152280613,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.597981308411214995679472394841,105.841072082519531250000000000000,3805,655,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
327,327_0,COMPLETED,BoTorch,GenerationStep_1,0.570018691588785042512199652265,54.473977804183959960937500000000,115,195,0.511350987660632250175751778443,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.610616822429906558333811972261,168.888024568557739257812500000000,4000,727,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
329,329_0,COMPLETED,BoTorch,GenerationStep_1,0.622031152647975038583183504670,111.334976196289062500000000000000,313,223,0.338218372412896872702958717127,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
330,330_0,COMPLETED,BoTorch,GenerationStep_1,0.607077881619937698154387817340,145.869083642959594726562500000000,1853,1000,0.266783499750911867742786398594,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
331,331_0,COMPLETED,BoTorch,GenerationStep_1,0.615352024922118379102187191165,140.799562692642211914062500000000,1894,1000,0.461906375659444368153572213487,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.590105919003115264587222554837,139.476050615310668945312500000000,1494,1000,0.416931532539977112161722061501,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
333,333_0,COMPLETED,BoTorch,GenerationStep_1,0.643439252336448608282637451339,116.586999654769897460937500000000,103,100,0.370860866275693146221215101832,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
334,334_0,COMPLETED,BoTorch,GenerationStep_1,0.307314641744548311397267070788,49.186501502990722656250000000000,2019,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
335,335_0,COMPLETED,BoTorch,GenerationStep_1,0.600261682242990701752205495723,162.858084440231323242187500000000,1836,958,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.606629283489096615689106783975,159.649304151535034179687500000000,1847,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
337,337_0,COMPLETED,BoTorch,GenerationStep_1,0.619003115264797454386780373170,96.682649135589599609375000000000,86,100,0.424306366756692754371727005491,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
338,338_0,COMPLETED,BoTorch,GenerationStep_1,0.599052959501557680255245941225,141.987743377685546875000000000000,1866,1000,0.356375013167285870085976284827,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
339,339_0,COMPLETED,BoTorch,GenerationStep_1,0.608872274143302139037814413314,71.760072231292724609375000000000,70,100,0.448683487038988138007056249990,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
340,340_0,COMPLETED,BoTorch,GenerationStep_1,0.609968847352024945429604940728,143.414949178695678710937500000000,1844,928,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
341,341_0,COMPLETED,BoTorch,GenerationStep_1,0.637694704049844185433926213591,102.546736240386962890625000000000,118,100,0.320051307626204928347135592048,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
342,342_0,COMPLETED,BoTorch,GenerationStep_1,0.498542056074766370965534179049,54.911074399948120117187500000000,2253,825,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
343,343_0,COMPLETED,BoTorch,GenerationStep_1,0.330965732087227404445428646795,44.006911754608154296875000000000,2036,932,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
344,344_0,COMPLETED,BoTorch,GenerationStep_1,0.566130841121495365086957463063,101.855411052703857421875000000000,2623,284,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
345,345_0,COMPLETED,BoTorch,GenerationStep_1,0.626056074766355163951914164500,95.392297744750976562500000000000,95,100,0.400760615128722019750995286813,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
346,346_0,COMPLETED,BoTorch,GenerationStep_1,0.589657320872274182121941521473,103.822344541549682617187500000000,823,858,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
347,347_0,COMPLETED,BoTorch,GenerationStep_1,0.515389408099688473008370692696,56.096716880798339843750000000000,295,1000,0.466618621771290487121319756625,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
348,348_0,COMPLETED,BoTorch,GenerationStep_1,0.311862928348909662634724782038,50.681238651275634765625000000000,2085,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.596884735202492189287681867427,59.967314958572387695312500000000,103,154,0.436169518129440214604386483188,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
350,350_0,COMPLETED,BoTorch,GenerationStep_1,0.553968847352024895691613437521,58.499745607376098632812500000000,79,152,0.553875507676488409991577555047,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
351,351_0,COMPLETED,BoTorch,GenerationStep_1,0.595165109034267891807701289508,67.236552476882934570312500000000,60,100,0.479268804485999677655883033367,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
352,352_0,COMPLETED,BoTorch,GenerationStep_1,0.608161993769470443638169854239,131.531951904296875000000000000000,1460,836,0.346030879511468714149202696717,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.543638629283489049903721479495,81.855314970016479492187500000000,678,885,0.609201469696308395285200276703,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
354,354_0,COMPLETED,BoTorch,GenerationStep_1,0.503962616822429931850990669773,55.927286863327026367187500000000,2230,766,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
355,355_0,COMPLETED,BoTorch,GenerationStep_1,0.597619937694704006503343407530,102.725807666778564453125000000000,1103,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
356,356_0,COMPLETED,BoTorch,GenerationStep_1,0.586741433021806813030707417056,57.444621801376342773437500000000,121,212,0.401031082246126324708512811412,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
357,357_0,COMPLETED,BoTorch,GenerationStep_1,0.497532710280373824396349391463,43.488682746887207031250000000000,2254,920,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
358,358_0,COMPLETED,BoTorch,GenerationStep_1,0.527214953271028075043602711958,62.910979509353637695312500000000,124,151,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
359,359_0,COMPLETED,BoTorch,GenerationStep_1,0.492498442367601263480736406564,95.965788841247558593750000000000,1526,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
360,360_0,COMPLETED,BoTorch,GenerationStep_1,0.353233644859813067373721651165,30.948175907135009765625000000000,10,762,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
361,361_0,COMPLETED,BoTorch,GenerationStep_1,0.502629283489096523318551135162,52.506440401077270507812500000000,2168,297,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
362,362_0,COMPLETED,BoTorch,GenerationStep_1,0.519925233644859763337819913431,71.073156833648681640625000000000,760,536,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
363,363_0,COMPLETED,BoTorch,GenerationStep_1,0.436722741433021810486536651297,47.719652652740478515625000000000,116,388,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.436523364485981335558761884386,37.684572935104370117187500000000,43,591,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
365,365_0,COMPLETED,BoTorch,GenerationStep_1,0.566479750778816182332775497343,79.248876333236694335937500000000,497,490,0.636984496596823879244197996741,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
366,366_0,COMPLETED,BoTorch,GenerationStep_1,0.545482866043613734419182037527,46.390943527221679687500000000000,44,194,0.396272571190177724798076042134,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
367,367_0,COMPLETED,BoTorch,GenerationStep_1,0.593956386292834870310741735011,96.783663988113403320312500000000,3115,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
368,368_0,COMPLETED,BoTorch,GenerationStep_1,0.369906542056074760793649147672,31.537485837936401367187500000000,10,700,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
369,369_0,COMPLETED,BoTorch,GenerationStep_1,0.609096573208722791292757392512,68.225185871124267578125000000000,78,137,0.334411097043906757608766611156,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
370,370_0,COMPLETED,BoTorch,GenerationStep_1,0.361719626168224284157304282417,31.984399557113647460937500000000,10,869,0.209610417611632099399443518450,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
371,371_0,COMPLETED,BoTorch,GenerationStep_1,0.560822429906542074817821230681,90.035157442092895507812500000000,755,445,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
372,372_0,COMPLETED,BoTorch,GenerationStep_1,0.413133956386292855444963834088,48.686389446258544921875000000000,2123,585,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
373,373_0,COMPLETED,BoTorch,GenerationStep_1,0.340797507788161979647156840656,29.696674108505249023437500000000,10,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
374,374_0,COMPLETED,BoTorch,GenerationStep_1,0.432398753894081000481719456729,54.315518856048583984375000000000,262,663,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
375,375_0,COMPLETED,BoTorch,GenerationStep_1,0.458342679127725860510622624133,51.125177383422851562500000000000,127,476,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
376,376_0,COMPLETED,BoTorch,GenerationStep_1,0.593956386292834870310741735011,114.409366130828857421875000000000,1903,611,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
377,377_0,COMPLETED,BoTorch,GenerationStep_1,0.544336448598130795417660010571,91.031138896942138671875000000000,529,418,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
378,378_0,COMPLETED,BoTorch,GenerationStep_1,0.422218068535825552523021997331,50.554589509963989257812500000000,2141,503,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
379,379_0,COMPLETED,BoTorch,GenerationStep_1,0.601644859813084131872074067360,126.554804086685180664062500000000,1896,674,0.681400054625619477910447585600,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
380,380_0,COMPLETED,BoTorch,GenerationStep_1,0.570704049844236727118129692826,83.258963823318481445312500000000,2766,1000,0.406301261371205812977791538287,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
381,381_0,COMPLETED,BoTorch,GenerationStep_1,0.535090342679127695113550089445,69.897844314575195312500000000000,2528,1000,0.373442429932830055605563757126,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
382,382_0,COMPLETED,BoTorch,GenerationStep_1,0.493831775700934560990873478659,53.456653833389282226562500000000,243,906,0.525910631742059098137076489365,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
383,383_0,COMPLETED,BoTorch,GenerationStep_1,0.606940809968847361233201809227,174.385741710662841796875000000000,1818,297,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
384,384_0,COMPLETED,BoTorch,GenerationStep_1,0.446728971962616849822325093555,58.741354942321777343750000000000,2422,952,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
385,385_0,COMPLETED,BoTorch,GenerationStep_1,0.479003115264797496575255308926,52.332942247390747070312500000000,188,744,0.591131432183224392318265927315,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
386,386_0,COMPLETED,BoTorch,GenerationStep_1,0.505657320872274107514954266662,82.729329824447631835937500000000,1192,740,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
387,387_0,COMPLETED,BoTorch,GenerationStep_1,0.607775700934579443668326348416,149.483939647674560546875000000000,1884,735,0.570560857841720392436002384784,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
388,388_0,COMPLETED,BoTorch,GenerationStep_1,0.420747663551401862580547685866,49.992700099945068359375000000000,205,707,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
389,389_0,COMPLETED,BoTorch,GenerationStep_1,0.602965732087227368474202648940,97.959971427917480468750000000000,479,472,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
390,390_0,COMPLETED,BoTorch,GenerationStep_1,0.432647975077881608019225723183,43.709257602691650390625000000000,142,1000,0.526087198411638312656180005433,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
391,391_0,COMPLETED,BoTorch,GenerationStep_1,0.596236760124610576383474835893,120.574815511703491210937500000000,1283,308,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
392,392_0,COMPLETED,BoTorch,GenerationStep_1,0.409084112149532719282518655746,38.277940273284912109375000000000,47,714,0.501853709395087554945291685726,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
393,393_0,COMPLETED,BoTorch,GenerationStep_1,0.536112149532710247079592136288,57.529412984848022460937500000000,2207,245,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
394,394_0,COMPLETED,BoTorch,GenerationStep_1,0.381433021806853567170492169680,51.168833971023559570312500000000,2217,879,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
395,395_0,COMPLETED,BoTorch,GenerationStep_1,0.458828660436137070188777897783,44.628985881805419921875000000000,123,834,0.428248431247314431669082068765,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
396,396_0,COMPLETED,BoTorch,GenerationStep_1,0.427576323987538919890738497998,39.616089105606079101562500000000,77,783,0.441141672015883890001219924670,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
397,397_0,COMPLETED,BoTorch,GenerationStep_1,0.478940809968847358568666550127,39.838508367538452148437500000000,36,356,0.475627323537911428363855748103,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
398,398_0,COMPLETED,BoTorch,GenerationStep_1,0.446853582554517125835502611153,38.634242773056030273437500000000,44,535,0.477512401725635027283090039418,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
399,399_0,COMPLETED,BoTorch,GenerationStep_1,0.449395638629283500353750469003,40.266155719757080078125000000000,146,1000,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
400,400_0,COMPLETED,BoTorch,GenerationStep_1,0.642641744548286597549235921178,84.430037021636962890625000000000,92,100,0.295491090131031253207538611605,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
401,401_0,COMPLETED,BoTorch,GenerationStep_1,0.438006230529595030898093455107,40.827309608459472656250000000000,114,947,0.447608029698747467151065393409,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
402,402_0,COMPLETED,BoTorch,GenerationStep_1,0.355526479750778834354463242562,51.591929197311401367187500000000,181,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
403,403_0,COMPLETED,BoTorch,GenerationStep_1,0.466878504672897209903936754927,36.845279216766357421875000000000,10,196,0.200000000000000011102230246252,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
404,404_0,COMPLETED,BoTorch,GenerationStep_1,0.595389408099688433040341806191,140.488795757293701171875000000000,1276,266,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
405,405_0,COMPLETED,BoTorch,GenerationStep_1,0.348373831775700915081017683406,55.269015789031982421875000000000,241,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
406,406_0,COMPLETED,BoTorch,GenerationStep_1,0.578666666666666662521834041399,75.379893541336059570312500000000,2232,286,0.536982693082495576852863905515,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
407,407_0,COMPLETED,BoTorch,GenerationStep_1,0.340024922118380035218621060267,55.924652338027954101562500000000,2231,1000,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
408,408_0,COMPLETED,BoTorch,GenerationStep_1,0.342330218068535807596219910920,32.178971767425537109375000000000,10,1000,0.493195532450629359200178214451,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
409,409_0,COMPLETED,BoTorch,GenerationStep_1,0.450305295950155781703472257504,40.399377107620239257812500000000,30,345,0.640204795478207189241004471114,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
410,410_0,COMPLETED,BoTorch,GenerationStep_1,0.554230529595015619648279425746,69.227747440338134765625000000000,2278,466,0.449058483641841088562784989335,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
411,411_0,COMPLETED,BoTorch,GenerationStep_1,0.437046728971962616938640167064,35.715397596359252929687500000000,10,257,0.472340514156904811571280333737,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.378978193146417452474850051658,35.677867650985717773437500000000,19,747,0.503052915506919884336411996628,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.415489096573208704921142953026,46.658551692962646484375000000000,136,559,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
414,414_0,COMPLETED,BoTorch,GenerationStep_1,0.454355140186915862354766204589,59.939025402069091796875000000000,2368,711,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.571202492211838053215444688249,100.731177330017089843750000000000,3233,509,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
416,416_0,COMPLETED,BoTorch,GenerationStep_1,0.339850467289719626595712043127,51.235025882720947265625000000000,94,749,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
417,417_0,COMPLETED,BoTorch,GenerationStep_1,0.563750778816199393794761363097,92.202933788299560546875000000000,2980,527,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
418,418_0,COMPLETED,BoTorch,GenerationStep_1,0.387028037383177592190008908801,33.692842006683349609375000000000,12,576,0.402522644579113908491763140773,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
419,419_0,COMPLETED,BoTorch,GenerationStep_1,0.584348909657320891852805289091,117.311275243759155273437500000000,1027,262,0.800000000000000044408920985006,&quot;\{\&#039;nu\&#039;:0.5,\&#039;kernel\&#039;:\&#039;rbf\&#039;,\&#039;gamma\&#039;:\&#039;auto\&#039;\}&quot;
</pre>
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			<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>OCDD_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACC-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;10&#039;, &#039;4000&#039;, &#039;int&#039;], [&#039;n_samples&#039;, &#039;range&#039;, &#039;100&#039;, &#039;1000&#039;, &#039;int&#039;], [&#039;threshold&#039;, &#039;range&#039;, &#039;0.2&#039;, &#039;0.8&#039;, &#039;float&#039;],     </td></tr><tr><td></td><td>[&#039;outlier_detector_kwargs&#039;, &#039;fixed&#039;, &quot;\\{\\&#039;nu\\&#039;:0.5,\\&#039;kernel\\&#039;:\\&#039;rbf\\&#039;,\\&#039;gamma\\&#039;:\\&#039;auto\\&#039;\\}&quot;]]                                                </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>30                                                                                                                                                       </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                                                    </td></tr><tr><td> time</td><td>1260                                                                                                                                                     </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>
			<div class='invert_in_dark_mode' id='workerUsagePlot'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
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1742455195.5268948,30,1,3
1742455202.6238322,30,1,3
1742455208.000155,30,1,3
1742455208.1669548,30,1,3
1742455210.456461,30,0,0
1742455212.5838594,30,0,0
1742455214.3260722,30,0,0
1742455506.123332,30,0,0
1742455672.1305058,30,0,0
1742455867.892247,30,0,0
1742456058.9382918,30,0,0
1742456340.123961,30,0,0
1742456491.048537,30,0,0
1742456814.7379456,30,0,0
1742457098.1272352,30,0,0
1742457296.7878625,30,0,0
1742457559.8627892,30,0,0
1742457684.6459548,30,0,0
1742457903.5071301,30,0,0
1742458055.718163,30,0,0
1742458373.3850553,30,0,0
1742458535.4794025,30,0,0
1742458697.846765,30,0,0
1742458861.5664573,30,0,0
1742459092.441076,30,0,0
1742459301.4846282,30,0,0
1742459583.0128372,30,0,0
1742459856.9906888,30,0,0
1742460115.2910624,30,0,0
1742460277.6684198,30,0,0
1742460429.2245996,30,0,0
1742460773.7247694,30,0,0
1742461017.2605345,30,0,0
1742461275.9454963,30,0,0
1742461480.7963355,30,0,0
1742461735.2803388,30,0,0
1742462026.6731653,30,0,0
1742462026.946399,30,0,0
1742462029.0860686,30,1,3
1742462029.2036402,30,1,3
1742462029.3629968,30,1,3
1742462031.261816,30,2,7
1742462031.296432,30,2,7
1742462031.6210682,30,2,7
1742462033.6233673,30,3,10
1742462033.7076926,30,3,10
1742462034.0142949,30,3,10
1742462035.9835107,30,4,13
1742462036.0193229,30,4,13
1742462036.20821,30,4,13
1742462038.393142,30,5,17
1742462038.4292395,30,5,17
1742462038.702468,30,5,17
1742462040.4936934,30,6,20
1742462040.5334334,30,6,20
1742462040.712203,30,6,20
1742462042.4610517,30,7,23
1742462042.497587,30,7,23
1742462042.751307,30,7,23
1742462044.507147,30,8,27
1742462044.5430286,30,8,27
1742462044.8240135,30,8,27
1742462046.4117916,30,9,30
1742462046.5057511,30,9,30
1742462046.7293158,30,9,30
1742462048.321385,30,10,33
1742462048.357594,30,10,33
1742462048.638947,30,10,33
1742462050.3411188,30,11,37
1742462050.3900597,30,11,37
1742462050.595298,30,11,37
1742462052.2111547,30,12,40
1742462052.2482133,30,12,40
1742462052.4611742,30,12,40
1742462054.3313649,30,13,43
1742462054.3836365,30,13,43
1742462054.6204083,30,13,43
1742462056.3304942,30,14,47
1742462056.3689005,30,14,47
1742462056.5888033,30,14,47
1742462058.2512343,30,15,50
1742462058.2941952,30,15,50
1742462058.5933263,30,15,50
1742462060.1296139,30,16,53
1742462060.1689243,30,16,53
1742462060.3506598,30,16,53
1742462062.2416408,30,17,57
1742462062.283139,30,17,57
1742462062.4515476,30,17,57
1742462064.2121768,30,18,60
1742462064.2550595,30,18,60
1742462064.4539042,30,18,60
1742462066.3245595,30,19,63
1742462066.364494,30,19,63
1742462066.590085,30,19,63
1742462068.1613107,30,20,67
1742462068.1990902,30,20,67
1742462068.4042108,30,20,67
1742462070.2633495,30,21,70
1742462070.3231063,30,21,70
1742462070.5260024,30,21,70
1742462072.3983514,30,22,73
1742462072.4339893,30,22,73
1742462072.8461618,30,22,73
1742462074.4422653,30,23,77
1742462074.4814687,30,23,77
1742462074.6306171,30,23,77
1742462076.3305135,30,24,80
1742462076.367076,30,24,80
1742462076.5697258,30,24,80
1742462078.229601,30,25,83
1742462078.301445,30,25,83
1742462078.4613473,30,25,83
1742462080.330489,30,26,87
1742462080.4197755,30,26,87
1742462080.6062536,30,26,87
1742462082.2059295,30,27,90
1742462082.244229,30,27,90
1742462082.4490612,30,27,90
1742462084.231954,30,28,93
1742462084.275571,30,28,93
1742462084.4650865,30,28,93
1742462086.382633,30,29,97
1742462086.4156108,30,29,97
1742462086.6048644,30,29,97
1742462088.1775186,30,30,100
1742462088.7690575,30,30,100
1742462088.9624968,30,30,100
1742462092.0171847,30,29,97
1742462092.1411984,30,29,97
1742462097.2740939,30,29,97
1742462104.3208036,30,29,97
1742462110.245898,30,29,97
1742462110.3697145,30,29,97
1742462111.3470528,30,28,93
1742462111.5768733,30,28,93
1742462112.7701018,30,27,90
1742462112.9023929,30,27,90
1742462113.825628,30,26,87
1742462113.9334936,30,26,87
1742462116.3286598,30,25,83
1742462116.451803,30,25,83
1742462121.9294567,30,25,83
1742462122.0651243,30,25,83
1742462123.267109,30,24,80
1742462123.3938692,30,24,80
1742462124.3634226,30,23,77
1742462124.5561075,30,23,77
1742462125.5792415,30,22,73
1742462125.7578213,30,22,73
1742462128.5744042,30,21,70
1742462128.6965227,30,21,70
1742462135.7411532,30,21,70
1742462141.1572943,30,21,70
1742462141.3638844,30,21,70
1742462142.4333189,30,20,67
1742462142.614768,30,20,67
1742462143.6855092,30,19,63
1742462143.7937531,30,19,63
1742462144.7368336,30,18,60
1742462144.830996,30,18,60
1742462147.6486192,30,17,57
1742462147.748864,30,17,57
1742462153.2228007,30,17,57
1742462153.4332697,30,17,57
1742462154.3686275,30,16,53
1742462154.4718435,30,16,53
1742462157.1979625,30,15,50
1742462157.3247542,30,15,50
1742462162.7783,30,15,50
1742462162.911898,30,15,50
1742462163.8025281,30,14,47
1742462163.8977206,30,14,47
1742462164.8569412,30,13,43
1742462165.0828538,30,13,43
1742462167.893175,30,12,40
1742462168.0056784,30,12,40
1742462173.5286555,30,12,40
1742462173.6172223,30,12,40
1742462176.4482415,30,11,37
1742462176.5327485,30,11,37
1742462183.8049076,30,11,37
1742462189.3894951,30,11,37
1742462189.4815822,30,11,37
1742462192.2955043,30,10,33
1742462192.3882542,30,10,33
1742462197.9672027,30,10,33
1742462198.1464071,30,10,33
1742462199.1757784,30,9,30
1742462199.295938,30,9,30
1742462200.1506917,30,8,27
1742462200.269702,30,8,27
1742462203.0898488,30,7,23
1742462203.2134762,30,7,23
1742462208.6708558,30,7,23
1742462208.8188353,30,7,23
1742462209.8893218,30,6,20
1742462210.0417106,30,6,20
1742462213.008802,30,5,17
1742462213.1539881,30,5,17
1742462220.142413,30,5,17
1742462227.4084058,30,5,17
1742462232.9271345,30,5,17
1742462233.1245415,30,5,17
1742462234.2331293,30,4,13
1742462234.3130898,30,4,13
1742462237.1138282,30,3,10
1742462237.267536,30,3,10
1742462242.66968,30,3,10
1742462242.8018446,30,3,10
1742462245.1592305,30,2,7
1742462245.2620804,30,2,7
1742462252.648996,30,2,7
1742462259.6902103,30,2,7
1742462266.9940083,30,2,7
1742462274.3744502,30,2,7
1742462281.7814891,30,2,7
1742462289.087839,30,2,7
1742462294.5048356,30,2,7
1742462294.6096642,30,2,7
1742462297.0578558,30,1,3
1742462297.133758,30,1,3
1742462304.4333854,30,1,3
1742462311.4663475,30,1,3
1742462316.8186896,30,1,3
1742462316.9677207,30,1,3
1742462319.759901,30,0,0
1742462322.1089654,30,0,0
1742462324.045798,30,0,0
1742462633.0008643,30,0,0
1742462968.3834524,30,0,0
1742463151.3562686,30,0,0
1742463341.3480499,30,0,0
1742463528.3752346,30,0,0
1742463844.3926067,30,0,0
1742464051.147174,30,0,0
1742464338.8260946,30,0,0
1742464658.655933,30,0,0
1742464796.2927852,30,0,0
1742465067.89833,30,0,0
1742465324.949944,30,0,0
1742465669.808552,30,0,0
1742465959.7410636,30,0,0
1742466238.504663,30,0,0
1742466639.7565427,30,0,0
1742466981.7665472,30,0,0
1742467244.327648,30,0,0
1742467544.4023514,30,0,0
1742467790.1484368,30,0,0
1742468188.2150486,30,0,0
1742468597.5569992,30,0,0
1742468860.4892492,30,0,0
1742469088.7100494,30,0,0
1742469468.554616,30,0,0
1742469730.4939268,30,0,0
1742469991.8756323,30,0,0
1742470421.6352618,30,0,0
1742470884.508277,30,0,0
1742471186.3600993,30,0,0
1742471186.526048,30,0,0
1742471188.3734565,30,1,3
1742471188.4903305,30,1,3
1742471188.6650746,30,1,3
1742471190.4076087,30,2,7
1742471190.4474666,30,2,7
1742471190.6150923,30,2,7
1742471192.3743467,30,3,10
1742471192.4106376,30,3,10
1742471192.626232,30,3,10
1742471194.2883916,30,4,13
1742471194.3217566,30,4,13
1742471194.489022,30,4,13
1742471196.378736,30,5,17
1742471196.494743,30,5,17
1742471196.6415083,30,5,17
1742471198.3413684,30,6,20
1742471198.3800294,30,6,20
1742471198.5718265,30,6,20
1742471200.198477,30,7,23
1742471200.2357504,30,7,23
1742471200.448203,30,7,23
1742471202.0078712,30,8,27
1742471202.1297858,30,8,27
1742471202.3320615,30,8,27
1742471203.89803,30,9,30
1742471203.9374945,30,9,30
1742471204.109036,30,9,30
1742471205.6100419,30,10,33
1742471205.70169,30,10,33
1742471205.8719375,30,10,33
1742471207.5427885,30,11,37
1742471207.5805535,30,11,37
1742471207.7606921,30,11,37
1742471209.3086836,30,12,40
1742471209.3420773,30,12,40
1742471209.5021288,30,12,40
1742471211.118764,30,13,43
1742471211.158542,30,13,43
1742471211.3419502,30,13,43
1742471212.8472564,30,14,47
1742471212.8828475,30,14,47
1742471213.043266,30,14,47
1742471214.6166058,30,15,50
1742471214.6496851,30,15,50
1742471214.8233976,30,15,50
1742471216.4602153,30,16,53
1742471216.499462,30,16,53
1742471216.6736784,30,16,53
1742471218.413812,30,17,57
1742471218.4514744,30,17,57
1742471218.636124,30,17,57
1742471220.187569,30,18,60
1742471220.2908123,30,18,60
1742471220.4652388,30,18,60
1742471222.1842725,30,19,63
1742471222.2217438,30,19,63
1742471222.3796244,30,19,63
1742471224.0996325,30,20,67
1742471224.139497,30,20,67
1742471224.3843162,30,20,67
1742471225.9920118,30,21,70
1742471226.027225,30,21,70
1742471226.218893,30,21,70
1742471227.8775997,30,22,73
1742471227.912382,30,22,73
1742471228.0776353,30,22,73
1742471229.7196693,30,23,77
1742471229.7643,30,23,77
1742471229.9640067,30,23,77
1742471231.744234,30,24,80
1742471231.784831,30,24,80
1742471232.0147297,30,24,80
1742471233.573927,30,25,83
1742471233.6070235,30,25,83
1742471233.7981687,30,25,83
1742471235.4332075,30,26,87
1742471235.474942,30,26,87
1742471235.6855981,30,26,87
1742471237.3803205,30,27,90
1742471237.4167945,30,27,90
1742471237.5758505,30,27,90
1742471239.1504097,30,28,93
1742471239.1841493,30,28,93
1742471239.3752923,30,28,93
1742471240.9162512,30,29,97
1742471240.9842167,30,29,97
1742471241.136001,30,29,97
1742471242.767457,30,30,100
1742471245.4111915,30,30,100
</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
1742403742,593.5859375,39.2
1742403742,593.62890625,41.6
1742403742,593.72265625,41.3
1742403742,593.72265625,39.6
1742403742,593.72265625,44.9
1742403742,593.72265625,41.0
1742403742,593.72265625,40.9
1742403831,609.59765625,40.9
1742403831,609.59765625,36.4
1742403831,609.59765625,42.7
1742403831,609.59765625,30.8
1742405478,672.6171875,41.0
1742405478,672.6171875,44.6
1742405478,672.6171875,41.8
1742405478,672.6171875,31.6
1742408869,685.765625,41.7
1742408869,685.765625,42.1
1742408869,685.765625,42.2
1742408869,685.765625,38.6
1742410596,697.62890625,42.0
1742410596,697.62890625,45.1
1742410596,697.62890625,42.3
1742410596,697.62890625,45.1
1742412919,720.640625,42.1
1742412919,720.640625,41.7
1742412919,720.640625,41.9
1742412919,720.640625,43.1
1742416559,743.60546875,42.0
1742416559,743.609375,43.8
1742416559,743.609375,41.3
1742416559,743.609375,45.7
1742419662,710.8671875,42.1
1742419662,710.8671875,43.5
1742419662,710.8671875,41.7
1742419662,710.8671875,43.4
1742424343,797.859375,41.4
1742424343,797.859375,22.0
1742424343,797.859375,22.3
1742424343,797.859375,24.4
1742430367,760.9609375,5.1
1742430368,760.9609375,5.7
1742430368,760.9609375,5.9
1742430368,760.9609375,4.3
1742437541,787.9296875,3.6
1742437541,787.9296875,1.1
1742437541,787.9296875,1.0
1742437541,787.9296875,0.0
1742446445,873.94921875,1.3
1742446445,873.94921875,0.1
1742446445,873.94921875,0.5
1742446445,873.94921875,0.0
1742455054,816.94921875,0.8
1742455054,816.94921875,0.0
1742455054,816.94921875,0.2
1742455054,816.94921875,0.0
1742462096,819.64453125,0.8
1742462096,819.64453125,0.2
1742462097,819.64453125,0.1
1742462097,819.64453125,0.0
1742471248,840.65625,1.4
1742471248,840.65625,0.0
1742471248,840.65625,0.5
1742471248,840.65625,0.0
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;Parallel Plot</h1>
			<div class="invert_in_dark_mode" id="parallel-plot"></div>
			<h1>&nbsp;Scatter-2D</h1>
			<div class='invert_in_dark_mode' id='plotScatter2d'></div>
			<h1>&nbsp;Scatter-3D</h1>
			<div class='invert_in_dark_mode' id='plotScatter3d'></div>
			<h1>&nbsp;Job Status Distribution</h1>
			<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
			<h1>&nbsp;Boxplots</h1>
			<div class="invert_in_dark_mode" id="plotBoxplot"></div>
			<h1>&nbsp;Violin</h1>
			<div class="invert_in_dark_mode" id="plotViolin"></div>
			<h1>&nbsp;Histogram</h1>
			<div class="invert_in_dark_mode" id="plotHistogram"></div>
			<h1>&nbsp;Heatmap</h1>
			<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
			    <h1>Correlation Heatmap Explanation</h1>
			
			    <p>
			        This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
			    </p>
			
			    <h2>How It Works</h2>
			    <p>
			        The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
			    </p>
			    <ul>
			        <li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
			        <li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
			        <li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
			    </ul>
			
			    <h2>Color Scale: Yellow to Purple (Viridis)</h2>
			    <p>
			        The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
			    </p>
			    <ul>
			        <li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
			        <li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
			        <li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
			        <li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
			    </ul>
			
			    <h2>What the Heatmap Shows</h2>
			    <p>
			        In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
			    </p>
			
			<h1>&nbsp;Result-Pairs</h1>
			<div class="invert_in_dark_mode" id="plotResultPairs"></div>
			<h1>&nbsp;Evolution</h1>
			<div class="invert_in_dark_mode" id="plotResultEvolution"></div>
	</body>
</html>