| n_samples | const | max_depth | threshold | result |
|---|---|---|---|---|
| 104 | 0.999994 | 4 | 0.613847 | 0.250563 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| const | range | 0.1 | 1 | float | |
| max_depth | range | 2 | 4 | int | |
| threshold | range | 0.2 | 0.8 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 496 | 11 | 507 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,const,max_depth,threshold 0,0_0,COMPLETED,Sobol,0.424606151537884501934172476467,804,0.355191910266876242907585492503,2,0.688604998588562056127670985006 1,1_0,COMPLETED,Sobol,0.389597399349837503201854360668,646,0.494997762795537754598740320944,4,0.709141819179058163769013845013 2,2_0,COMPLETED,Sobol,0.358839709927481820272987533826,260,0.470496729761362142419045540009,2,0.538578139059245675213105641888 3,3_0,COMPLETED,Sobol,0.377594398599649938574884799891,449,0.912662879005074478833137163747,4,0.327099011093378133629983040009 4,4_0,COMPLETED,Sobol,0.354338584646161525171237371978,290,0.807349003944546006472648969066,4,0.386300026066601298602165570628 5,5_0,COMPLETED,Sobol,0.373843460865216359323426331684,423,0.313943139836192153246940961253,3,0.257359432615339778216423383128 6,6_0,COMPLETED,Sobol,0.343835958989747392244851198484,328,0.987501259706914380487319249369,3,0.351141243055462903832619758759 7,7_0,COMPLETED,Sobol,0.324331082770692669114964701294,221,0.490980252064764477459846148122,2,0.427187782898545309606674891256 8,8_0,COMPLETED,Sobol,0.411602900725181242158612349158,709,0.646598942112177610397338867188,2,0.410460004769265696111801844381 9,9_0,COMPLETED,Sobol,0.388347086771692939777267383761,652,0.305540853552520252911506304372,2,0.587577150762081279466997330019 10,10_0,COMPLETED,Sobol,0.363840960240059962949032978941,344,0.935464081354439258575439453125,4,0.294472466409206434789780360006 11,11_0,COMPLETED,Sobol,0.400100025006251525105938071647,718,0.204116850439459096566707785314,2,0.348088733293116125988575504380 12,12_0,COMPLETED,Sobol,0.363840960240059962949032978941,250,0.929595873225480318069458007812,2,0.649391442537307739257812500000 13,13_0,COMPLETED,Sobol,0.393348337084271082453312828875,652,0.589934919681400216084909970959,4,0.481370603851974054876450281881 14,14_0,COMPLETED,Sobol,0.276069017254313542331090047810,116,0.352333464194089174270629882812,2,0.737698853760957895531191752525 15,15_0,COMPLETED,Sobol,0.424856214053513370210168886842,958,0.161001199670135985986263449377,3,0.378761482052505038531364789378 16,16_0,COMPLETED,Sobol,0.426856714178544649485047557391,948,0.614325035829097032546997070312,3,0.466140186786651644634815738755 17,17_0,COMPLETED,Sobol,0.426606651662915781209051147016,919,0.382197991572320483477653851878,4,0.390500460378825686724724164378 18,18_0,COMPLETED,Sobol,0.416354088522130516558661383897,854,0.669267002120614074023308148753,2,0.377037539146840572357177734375 19,19_0,COMPLETED,Sobol,0.388347086771692939777267383761,597,0.947661051340401128229018468119,4,0.222777565941214561462402343750 20,20_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.306444251856041238735173237728,2,0.617700926673672490174737959023 21,21_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.291189111572389136561866962438,2,0.788016810164061443089167369180 22,22_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.419590552525422544327682317089,3,0.681218564753604427508548724290 23,23_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.107219092064853457890727383983,2,0.692766283825185347211572661763 24,24_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.434643338042540405830038707791,2,0.681212125100234544561317306943 25,25_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.469484306122967431917913927464,2,0.496302794864017293718916334910 26,26_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.418763437134718419230239305762,2,0.800000000000000044408920985006 27,27_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.205324796186224789451557626307,3,0.775495919078641238186833106738 28,28_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.218789498844353003104146182523,3,0.572502830901044146294509573636 29,29_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.273266838896060904051665829684,2,0.669960777294955844851642723370 30,30_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.574822450878602064783251535118,2,0.705861387725882361010576460103 31,31_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.550000577284842195879832615901,3,0.494941172955118080523106982582 32,32_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.381080810170699280092776461970,3,0.773677049982346431988844415173 33,33_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.485681394001209021382692299085,2,0.656382055602220293444304388686 34,34_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.217800353368568316847486698862,2,0.496316566166169870211177794772 35,35_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.293274331445568969822801363989,2,0.702896070945567386090147010691 36,36_0,COMPLETED,BoTorch,0.319829957489372374013214539445,150,0.100000000000000005551115123126,2,0.732999933801112835141111645498 37,37_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.303814154247042278456092390115,3,0.490926843364170695238613006950 38,38_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.326023778408373599013714283501,2,0.554441296902848823613396689325 39,39_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.113076100400250345590080769398,2,0.800000000000000044408920985006 40,40_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.693822144385377037600903804559,2,0.200000000000000011102230246252 41,41_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.955358618844174833917293199192,2,0.218991660182812436508115183642 42,42_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.360693524082099425953629179276,2,0.200000000000000011102230246252 43,43_0,COMPLETED,BoTorch,0.317579394848712226462339458521,157,0.254917576997851336173539493757,2,0.800000000000000044408920985006 44,44_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.365026935099056593081456867367,3,0.455997916680213721818404337682 45,45_0,COMPLETED,BoTorch,0.320830207551887958139502643462,171,0.133370385680159492247653929553,3,0.800000000000000044408920985006 46,46_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.100000000000000005551115123126,3,0.430494390014774463981694907488 47,47_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.685784010468876048527420152823,2,0.344804364832543397412223384890 48,48_0,COMPLETED,BoTorch,0.272818204551137810653926862869,136,0.507792075119600117005802530912,3,0.200000000000000011102230246252 49,49_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.368690403436225500044542968681,2,0.651581193878452147316693299217 50,50_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.250546365281839822358733727015,3,0.242676365222636497565034119361 51,51_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.887863512601483884090214360185,3,0.541316418402806309728703126893 52,52_0,COMPLETED,BoTorch,0.299324831207801955734737475723,143,0.300122371534258247649518125399,2,0.200000000000000011102230246252 53,53_0,COMPLETED,BoTorch,0.288322080520130086256358481478,109,0.272554887870771089808386022924,3,0.302214598380782795139509744331 54,54_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.152989410441737666568329245820,4,0.547622286486337039868033116363 55,55_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.705955529139108750591447005718,3,0.200000000000000011102230246252 56,56_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 57,57_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.696918279252083383568106000894,4,0.743144919153433614056325495767 58,58_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.240951007171628206471325484017,4,0.338599669988341678283916280634 59,59_0,COMPLETED,BoTorch,0.290572643160790233807233562402,145,0.314486290097687526401415425426,4,0.472096338115887137476534007874 60,60_0,COMPLETED,BoTorch,0.292073018254563665507816949685,127,0.807583384608269616578013483377,3,0.390832008136810071796674037614 61,61_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.841761008546026423537966820732,2,0.250154467449882789154003148724 62,62_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.455084401112905378994355487521,3,0.296057782760091647844546969282 63,63_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,3,0.639653593977541512494155995228 64,64_0,COMPLETED,BoTorch,0.272568142035508831355627989979,131,1.000000000000000000000000000000,3,0.200000000000000011102230246252 65,65_0,COMPLETED,BoTorch,0.268817204301075252104169521772,131,0.803433343727120385935336344119,4,0.537728500266900333315334137296 66,66_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.551433642703019533115593731054,2,0.200000000000000011102230246252 67,67_0,COMPLETED,BoTorch,0.268817204301075252104169521772,131,0.815720686100123382189508447482,4,0.239004039956049629811474233065 68,68_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.574429573542817095699319906998,3,0.622326501938141207759258577425 69,69_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,2,0.523369565286156968042519110895 70,70_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.100000000000000005551115123126,4,0.200000000000000011102230246252 71,71_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.100000000000000005551115123126,3,0.200000000000000011102230246252 72,72_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.540706476994079365816503468523,4,0.200000000000000011102230246252 73,73_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.100000000000000005551115123126,4,0.200000000000000011102230246252 74,74_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,0.100000000000000005551115123126,2,0.200000000000000011102230246252 75,75_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,0.494589746404622832010034017003,3,0.200000000000000011102230246252 76,76_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.100000000000000005551115123126,4,0.590562607776977666063089600357 77,77_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.438517813872483785964107028121,4,0.200000000000000011102230246252 78,78_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,3,0.200000000000000011102230246252 79,79_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.739082785376435125179739316081,3,0.200000000000000011102230246252 80,80_0,COMPLETED,BoTorch,0.264066016504125977704120487033,121,1.000000000000000000000000000000,4,0.200000000000000011102230246252 81,81_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,4,0.634366332350308370635616483924 82,82_0,COMPLETED,BoTorch,0.273818454613653394780214966886,116,0.874124302662239505146146711922,4,0.200000000000000011102230246252 83,83_0,COMPLETED,BoTorch,0.292823205801450381358108643326,140,1.000000000000000000000000000000,3,0.200000000000000011102230246252 84,84_0,COMPLETED,BoTorch,0.269817454363590947252760088304,116,0.863004108513081735765126722981,3,0.200000000000000011102230246252 85,85_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,1.000000000000000000000000000000,2,0.200000000000000011102230246252 86,86_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,1.000000000000000000000000000000,4,0.361172035751472220166391480234 87,87_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,4,0.394609154010147789026774489685 88,88_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.975852408397702486553271228331,4,0.200000000000000011102230246252 89,89_0,COMPLETED,BoTorch,0.268567141785446383828173111397,115,1.000000000000000000000000000000,2,0.200000000000000011102230246252 90,90_0,COMPLETED,BoTorch,0.293573393348337097208400336967,115,0.611223177940916073680455156136,3,0.200000000000000011102230246252 91,91_0,COMPLETED,BoTorch,0.301575393848462103285612556647,140,1.000000000000000000000000000000,4,0.200000000000000011102230246252 92,92_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.351881429180319726945924685424,3,0.302701587448269804347944500478 93,93_0,COMPLETED,BoTorch,0.281570392598149532581430776190,129,1.000000000000000000000000000000,4,0.440810689282914225373133376706 94,94_0,COMPLETED,BoTorch,0.281570392598149532581430776190,129,1.000000000000000000000000000000,4,0.432497661304634517520639747090 95,95_0,COMPLETED,BoTorch,0.304076019004751230134786510462,125,1.000000000000000000000000000000,4,0.200000000000000011102230246252 96,96_0,COMPLETED,BoTorch,0.286071517879469827683180938038,133,0.100000000000000005551115123126,3,0.200000000000000011102230246252 97,97_0,COMPLETED,BoTorch,0.278319579894973689881965128734,124,0.932849181146925054974872182356,4,0.200000000000000011102230246252 98,98_0,COMPLETED,BoTorch,0.384346086521630381227510042663,521,0.209998931150957912628030044289,2,0.716665702831517981152842367010 99,99_0,COMPLETED,BoTorch,0.294323580895223813058692030609,149,1.000000000000000000000000000000,2,0.200000000000000011102230246252 100,100_0,COMPLETED,BoTorch,0.316829207301825510612047764880,177,1.000000000000000000000000000000,2,0.200000000000000011102230246252 101,101_0,COMPLETED,BoTorch,0.390597649412353087328142464685,524,0.300624071976989759580334293787,3,0.514060622048500448499908088706 102,102_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.994564123595890969831145866920,2,0.200877583763874462130516462821 103,103_0,COMPLETED,BoTorch,0.312328082020505104487995140516,172,0.891076978112465889481086378510,2,0.239148733964359483383788074207 104,104_0,COMPLETED,BoTorch,0.284321080270067527706601140380,135,1.000000000000000000000000000000,2,0.200000000000000011102230246252 105,105_0,COMPLETED,BoTorch,0.267066766691672952127589724114,107,0.794924273575133844005335959082,4,0.200000000000000011102230246252 106,106_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,4,0.435398190927561934415734867798 107,107_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.815006137091721538645572309179,4,0.479645736219914020637133944547 108,108_0,COMPLETED,BoTorch,0.288822205551387822808351302228,126,1.000000000000000000000000000000,2,0.200000000000000011102230246252 109,109_0,COMPLETED,BoTorch,0.305576394098524661835369897744,125,0.742023673686266205251627070538,4,0.200000000000000011102230246252 110,110_0,RUNNING,BoTorch,,174,0.482933293032632882102461735485,4,0.200000000000000011102230246252 111,111_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.810569395106448764565243436664,4,0.499766488547969012223859408550 112,112_0,COMPLETED,BoTorch,0.309077269317329372810831955576,184,0.742123642083571621874682477937,4,0.396004149328738341839795111810 113,113_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,1.000000000000000000000000000000,3,0.200000000000000011102230246252 114,114_0,COMPLETED,BoTorch,0.327831957989497380090426759125,197,0.100000000000000005551115123126,4,0.200000000000000011102230246252 115,115_0,COMPLETED,BoTorch,0.305826456614153530111366308120,125,0.758720311414586401355109046563,3,0.200000000000000011102230246252 116,116_0,COMPLETED,BoTorch,0.319829957489372374013214539445,150,0.470679202442742194989477866329,4,0.200000000000000011102230246252 117,117_0,COMPLETED,BoTorch,0.292073018254563665507816949685,127,0.690125469209516007040861040878,4,0.200000000000000011102230246252 118,118_0,COMPLETED,BoTorch,0.322830707676919237414381314011,180,0.329235935289270420511797965446,3,0.200000000000000011102230246252 119,119_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.187316804149583071570361880731,2,0.435589549928915964471798361046 120,120_0,COMPLETED,BoTorch,0.281070267566891685007135492924,109,1.000000000000000000000000000000,4,0.344082379059187259962016014470 121,121_0,COMPLETED,BoTorch,0.284321080270067527706601140380,135,0.817826042300227151748970300105,4,0.800000000000000044408920985006 122,122_0,RUNNING,BoTorch,,109,1.000000000000000000000000000000,4,0.312452915817014120758443596060 123,123_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,1.000000000000000000000000000000,4,0.375796397073783650100153863605 124,124_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.771861456166849091431458873558,4,0.800000000000000044408920985006 125,125_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.849031030601169578453379926941,4,0.444389355230059179824309012474 126,126_0,COMPLETED,BoTorch,0.312328082020505104487995140516,172,0.386146898570099650349618514156,4,0.200000000000000011102230246252 127,127_0,COMPLETED,BoTorch,0.260065016254063530176665608451,120,0.833691857195334562469213324221,4,0.313856728827658137959133455297 128,128_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.183931821772425674321738142680,3,0.311638779116048592054966093201 129,129_0,COMPLETED,BoTorch,0.330582645661415375215597123315,198,0.430196361213256439626206883986,4,0.200000000000000011102230246252 130,130_0,COMPLETED,BoTorch,0.309077269317329372810831955576,179,0.164687766307813510113788879607,4,0.212450260103791394028505123970 131,131_0,COMPLETED,BoTorch,0.305076269067266814261074614478,181,0.551495677668769168633389199385,3,0.201627516843725901329875682677 132,132_0,COMPLETED,BoTorch,0.259314828707176814326373914810,112,1.000000000000000000000000000000,4,0.410902053365496566783576781745 133,133_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,4,0.317875825487976160221847976572 134,134_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.566399598631598721887314695778,2,0.403521829421623401401575392811 135,135_0,COMPLETED,BoTorch,0.283320830207551832558010573848,133,0.789334273103131645044072683959,2,0.364387340321895958350495448030 136,136_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,3,0.317129241655597304827551852213 137,137_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.509742213052080339608096437587,2,0.443394331004298258847029501339 138,138_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,0.993123264978424957760694269382,4,0.380351317060630966793155494088 139,139_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,2,0.361328690539701402606453939370 140,140_0,COMPLETED,BoTorch,0.286071517879469827683180938038,133,0.499676527138228188107405003393,2,0.473267592536426029425911110593 141,141_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,2,0.375555143706168381712018344842 142,142_0,COMPLETED,BoTorch,0.268317079269817404529874238506,119,0.805543472401036519947581382439,4,0.369518638296195245374065052602 143,143_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.615425905535865958029262401396,3,0.395597999065405825369623471488 144,144_0,COMPLETED,BoTorch,0.321330332583145805713797926728,219,0.100000000000000005551115123126,4,0.800000000000000044408920985006 145,145_0,COMPLETED,BoTorch,0.289322330582645670382646585495,127,1.000000000000000000000000000000,3,0.326273843161262067091854532919 146,146_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,2,0.745180433082964777113943455333 147,147_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.869881937528376281143493997661,2,0.200000000000000011102230246252 148,148_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.200000000000000011102230246252 149,149_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.365170970667487715388688229723,2,0.314281816466359331663227294484 150,150_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.707053424378947026340824777435,2,0.779454584530959948551753768697 151,151_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.578695879337874363734783855762,2,0.385064316364432057682165577717 152,152_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,1.000000000000000000000000000000,2,0.800000000000000044408920985006 153,153_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.168133216273176455679561058787,2,0.200000000000000011102230246252 154,154_0,COMPLETED,BoTorch,0.340585146286571660567688013543,231,0.363095715366456461836719427083,4,0.800000000000000044408920985006 155,155_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,0.823343898349617964171898165660,4,0.311474978668095481282307446236 156,156_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.548515495006930753341123363498,3,0.461741433845498039367782894260 157,157_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.508177623583070392498939327197,2,0.360975277400786775938712480638 158,158_0,COMPLETED,BoTorch,0.289572393098274538658642995870,145,0.763497378353212186041787390423,4,0.656120774559123653979497703403 159,159_0,COMPLETED,BoTorch,0.303575893973493382560491227196,161,1.000000000000000000000000000000,4,0.760259955963611844254046445712 160,160_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,0.937764596806095585002083225845,4,0.394124146019359966608419654222 161,161_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.886316538091796557452539673250,4,0.350218293815040215832823378150 162,162_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.778764703440061434314145571989,3,0.539387403467770920606483286974 163,163_0,COMPLETED,BoTorch,0.298074518629657392310150498815,153,1.000000000000000000000000000000,3,0.628493505113686179441856438643 164,164_0,COMPLETED,BoTorch,0.318579644911227810588627562538,168,0.738572607067920161583174376574,4,0.800000000000000044408920985006 165,165_0,COMPLETED,BoTorch,0.293073268317079249634105053701,148,0.751566712122554658748185829609,3,0.544611584131581061285487521673 166,166_0,COMPLETED,BoTorch,0.288072018004501106958059608587,149,0.797970724371749429160161071195,4,0.683057012718588651978279813193 167,167_0,COMPLETED,BoTorch,0.258564641160290098476082221168,112,0.915763377289078084331208629010,4,0.378650620479047517186188542837 168,168_0,COMPLETED,BoTorch,0.328832208052012964216714863142,241,0.100000000000000005551115123126,3,0.434494296114224742844101001538 169,169_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.629764129201447975248129296233,4,0.588088343398097013192682425142 170,170_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.962964698548357089791238649923,3,0.427658823549549427234239828977 171,171_0,COMPLETED,BoTorch,0.306576644161040245961658001761,153,1.000000000000000000000000000000,2,0.587153476524125084168304056220 172,172_0,COMPLETED,BoTorch,0.299824956239059803309032758989,135,0.745822116784916944176586639514,2,0.544121941815105203410496415017 173,173_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.827611575533053089870350049750,3,0.268758229960108396827678234331 174,174_0,COMPLETED,BoTorch,0.279819954988747232604850978532,141,1.000000000000000000000000000000,2,0.496055723357998634703847073979 175,175_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.690252747052303772257175751292,4,0.379316123232398272335785804898 176,176_0,COMPLETED,BoTorch,0.284571142785696395982597550756,141,0.829531273845949268519461838878,2,0.604076118093288871868651312980 177,177_0,COMPLETED,BoTorch,0.279819954988747232604850978532,138,0.892858250315531565277638037514,2,0.508344152498240275939167531760 178,178_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.802276616479083437560859692894,3,0.375773198110284201156616745720 179,179_0,COMPLETED,BoTorch,0.279819954988747232604850978532,138,0.703059145765578263898021305067,2,0.502117528977239047094371926505 180,180_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.801826514657622446691220829962,4,0.382489158967147635515004822082 181,181_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,1.000000000000000000000000000000,2,0.469273848852979458268208645677 182,182_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.341926562172202797018627506986 183,183_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.361218419901333298582812858513 184,184_0,COMPLETED,BoTorch,0.335833958489622386167638978804,277,0.652037357889697410939788824180,2,0.758349342422176331268701687804 185,185_0,COMPLETED,BoTorch,0.267816954238559667977881417755,119,1.000000000000000000000000000000,4,0.383305619574218492395800694794 186,186_0,COMPLETED,BoTorch,0.260065016254063530176665608451,120,0.866555181411025832183270267706,4,0.354446227755446707785580429118 187,187_0,COMPLETED,BoTorch,0.268317079269817404529874238506,119,0.891011085697958282736408364144,4,0.369120428275470313650430398411 188,188_0,COMPLETED,BoTorch,0.278819704926231537456260412000,131,0.615240076151345571808803924796,4,0.460038000781976041952958667025 189,189_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,4,0.298110710880169160752473089815 190,190_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.204561782784976775584340202840 191,191_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.391419670086716298129658753169,3,0.651583099925149400455381965003 192,192_0,COMPLETED,BoTorch,0.336584146036509102017930672446,272,0.856497506172069100749411063589,2,0.396854329683163031816661714402 193,193_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,0.912871013282556376111642748583,4,0.396425274701186380887918403459 194,194_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,1.000000000000000000000000000000,2,0.322438055455256833425892182277 195,195_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,0.936261693752430290693666847801,4,0.441620754726279707291780596279 196,196_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.634370897759171437080283340038,4,0.202680907845545016376931357627 197,197_0,COMPLETED,BoTorch,0.279819954988747232604850978532,108,1.000000000000000000000000000000,4,0.306861455817827921688234482644 198,198_0,COMPLETED,BoTorch,0.267066766691672952127589724114,107,1.000000000000000000000000000000,4,0.296635269217448538370263122488 199,199_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.267560930379156514113958564849 200,200_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.558457506855587926253292607726,3,0.495368226815706858001675527703 201,201_0,RUNNING,BoTorch,,105,1.000000000000000000000000000000,4,0.276707349586912232375368603243 202,202_0,COMPLETED,BoTorch,0.259314828707176814326373914810,112,1.000000000000000000000000000000,4,0.200000000000000011102230246252 203,203_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,0.868611823450960440773371828982,4,0.329269159059516924870081311383 204,204_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.765459828832301347745215025498,4,0.399337708864830442934135135147 205,205_0,COMPLETED,BoTorch,0.250562640660165092398870001489,104,1.000000000000000000000000000000,4,0.251125074422064775703233863169 206,206_0,COMPLETED,BoTorch,0.267066766691672952127589724114,115,1.000000000000000000000000000000,4,0.299735146995161960692399816253 207,207_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.278950448218898860996972643989 208,208_0,COMPLETED,BoTorch,0.289822455613903517956941868761,147,0.545341513176659908879173599416,2,0.414259569087121692909647663328 209,209_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.305519568595645829578444363506 210,210_0,COMPLETED,BoTorch,0.359089772443110799571286406717,342,0.700004732291977016522821486433,3,0.482526382098135198095434361676 211,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 212,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 213,213_0,COMPLETED,BoTorch,0.306326581645411377685661591386,115,0.445382703240380317666335940885,2,0.362941993874897783634025927313 214,214_0,COMPLETED,BoTorch,0.289072268067016802106650175119,132,0.694642970553881688999808829976,3,0.448362179732559817946224711704 215,215_0,COMPLETED,BoTorch,0.279819954988747232604850978532,108,1.000000000000000000000000000000,4,0.200000000000000011102230246252 216,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 217,217_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,1.000000000000000000000000000000,4,0.245942470023802983725147441874 218,218_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.778954710278577655557796788344,3,0.539542309207637682533231782145 219,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 220,220_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,0.217786124134503461524658973758,4,0.365303712186263340733205495781 221,221_0,COMPLETED,BoTorch,0.358839709927481820272987533826,343,0.100000000000000005551115123126,2,0.800000000000000044408920985006 222,222_0,COMPLETED,BoTorch,0.346586646661665387370021562674,288,0.100000000000000005551115123126,2,0.800000000000000044408920985006 223,223_0,COMPLETED,BoTorch,0.288322080520130086256358481478,109,1.000000000000000000000000000000,2,0.720568206042152104018327918311 224,224_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.563146078035753339108282489178,2,0.749625672745976689981262097717 225,225_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.896068730123848466995184480766,3,0.534059884369033177620167407440 226,226_0,COMPLETED,BoTorch,0.274568642160540110630506660527,116,1.000000000000000000000000000000,4,0.320651682662672188328656375234 227,227_0,COMPLETED,BoTorch,0.259314828707176814326373914810,112,1.000000000000000000000000000000,4,0.343685005329892934167190787775 228,228_0,COMPLETED,BoTorch,0.321330332583145805713797926728,125,0.782538794082730304602080195764,3,0.495888682473096475966656271339 229,229_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.574333647445877115700341164484,3,0.622477701345453882098013309587 230,230_0,COMPLETED,BoTorch,0.274568642160540110630506660527,132,0.410872344080077112060678246053,4,0.553965792765449993595439082128 231,231_0,COMPLETED,BoTorch,0.286571642910727675257476221304,140,0.837072930304496365394584245223,2,0.200000000000000011102230246252 232,232_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.368634423676738265385210979730,2,0.651489027673128551221282123151 233,233_0,COMPLETED,BoTorch,0.318079519879969963014332279272,135,0.696379538661256347609196382109,2,0.800000000000000044408920985006 234,234_0,COMPLETED,BoTorch,0.267066766691672952127589724114,115,1.000000000000000000000000000000,4,0.313397998116239806520866295614 235,235_0,COMPLETED,BoTorch,0.336584146036509102017930672446,140,0.500627054817103345207840447983,4,0.653493728278130570075177274703 236,236_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,1.000000000000000000000000000000,3,0.200000000000000011102230246252 237,237_0,COMPLETED,BoTorch,0.274068517129282374078513839777,110,1.000000000000000000000000000000,4,0.211178212019044297953485056496 238,238_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,1.000000000000000000000000000000,3,0.332016367996864458778105699821 239,239_0,COMPLETED,BoTorch,0.274068517129282374078513839777,110,1.000000000000000000000000000000,4,0.218012146608940637904083814647 240,240_0,COMPLETED,BoTorch,0.269817454363590947252760088304,116,0.862934271159563670572367755085,3,0.200000000000000011102230246252 241,241_0,COMPLETED,BoTorch,0.291322830707676949657525256043,139,0.100000000000000005551115123126,4,0.800000000000000044408920985006 242,242_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,0.808899042933486134288045832363,4,0.200000000000000011102230246252 243,243_0,COMPLETED,BoTorch,0.274318579644911242354510250152,110,1.000000000000000000000000000000,4,0.411639440914393639481261288893 244,244_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,1.000000000000000000000000000000,3,0.307448454178484764653944694146 245,245_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.100000000000000005551115123126,3,0.800000000000000044408920985006 246,246_0,COMPLETED,BoTorch,0.296074018504626113035271828267,140,0.909754852166291816395471414580,4,0.800000000000000044408920985006 247,247_0,COMPLETED,BoTorch,0.274068517129282374078513839777,110,1.000000000000000000000000000000,4,0.200000000000000011102230246252 248,248_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.309761848662823191524751109682,3,0.800000000000000044408920985006 249,249_0,COMPLETED,BoTorch,0.336584146036509102017930672446,140,0.466843497959019804177671630896,4,0.800000000000000044408920985006 250,250_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.394125602210631864608103569481 251,251_0,COMPLETED,BoTorch,0.304576144036008966686779331212,125,0.812953784026579362453901467234,4,0.272988858239856690968139218967 252,252_0,COMPLETED,BoTorch,0.294323580895223813058692030609,149,0.261274780514945959009054377020,3,0.414663585542853874166269179113 253,253_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.100000000000000005551115123126,3,0.496999849952154959531469557987 254,254_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.400123605305035656698464663350 255,255_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.391734976195479700500357012061,3,0.651784479376789516180679129320 256,256_0,RUNNING,BoTorch,,139,0.735207535889332408629570636549,3,0.274237824375596628279083688540 257,257_0,COMPLETED,BoTorch,0.327581895473868511814430348750,199,0.646985955364709819370716559206,4,0.200000000000000011102230246252 258,56_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 259,259_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.260655654332802189099282941243,3,0.276161757871757340687679516122 260,260_0,COMPLETED,BoTorch,0.279569892473118253306552105641,136,0.913242927622038735968601486093,2,0.384574173698379428998350704205 261,261_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.100000000000000005551115123126,3,0.475117384893876670837187248253 262,262_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,1.000000000000000000000000000000,2,0.439296380580260714676654743016 263,263_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,2,0.636080261639037636278715126537 264,264_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.321924225653422324544550292558,3,0.618518030849716904384649751591 265,265_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,3,0.589696417472513667590305885824 266,266_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.179724787686078690818192171719,2,0.800000000000000044408920985006 267,267_0,COMPLETED,BoTorch,0.294323580895223813058692030609,149,0.292112099987361706343591549739,3,0.497794681718722209495808783686 268,268_0,COMPLETED,BoTorch,0.267816954238559667977881417755,119,1.000000000000000000000000000000,4,0.305944267056477481325771350384 269,269_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.462033126174450581302721730026,2,0.800000000000000044408920985006 270,270_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.507901938175407630104984946229,2,0.627577790751056463491863723902 271,271_0,COMPLETED,BoTorch,0.288072018004501106958059608587,133,1.000000000000000000000000000000,2,0.436594589517995901317704010580 272,272_0,COMPLETED,BoTorch,0.292073018254563665507816949685,142,0.186797929666732365205916721607,3,0.778620227078443694068710101419 273,273_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.100000000000000005551115123126,2,0.605340982541530570060217542050 274,274_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.368745357567170861656791203131,2,0.651487258862999518704839374550 275,275_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.367328151624619514414860077522,2,0.599872681460932533070717909141 276,276_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.173398003657111388076117464152,2,0.658298009012616591739686100482 277,277_0,COMPLETED,BoTorch,0.318079519879969963014332279272,135,0.656172595607408370987911894190,3,0.200000000000000011102230246252 278,278_0,COMPLETED,BoTorch,0.279569892473118253306552105641,136,1.000000000000000000000000000000,2,0.266053842348531743855488684858 279,279_0,COMPLETED,BoTorch,0.295073768442110528908983724250,147,0.755133970020621880792077718070,3,0.369219927858086049976549247731 280,280_0,COMPLETED,BoTorch,0.294573643410852681334688440984,134,1.000000000000000000000000000000,2,0.334715010251796118989631168006 281,281_0,COMPLETED,BoTorch,0.279569892473118253306552105641,136,0.888971972008698707590212961804,3,0.684980645020563772007449188095 282,282_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.241020146552075431589656773212,3,0.800000000000000044408920985006 283,283_0,COMPLETED,BoTorch,0.283320830207551832558010573848,133,0.782474569148861820444551540277,2,0.302350153974637214648879535162 284,284_0,COMPLETED,BoTorch,0.295823955988997244759275417891,143,1.000000000000000000000000000000,3,0.436908041124190571480312428321 285,285_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.100000000000000005551115123126,3,0.800000000000000044408920985006 286,286_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.505889829568764048950413325656,3,0.617280330653734887746963977406 287,287_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,1.000000000000000000000000000000,2,0.517911792475650001321696436207 288,288_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.805012052894248597567639080808,2,0.800000000000000044408920985006 289,289_0,COMPLETED,BoTorch,0.284071017754438659430604730005,135,1.000000000000000000000000000000,3,0.373994731156762960644357463025 290,290_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,2,0.631296959339576635450441699504 291,291_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,3,0.800000000000000044408920985006 292,292_0,RUNNING,BoTorch,,119,1.000000000000000000000000000000,4,0.405362795471797543456204948598 293,293_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.686937584294991743227853930875,3,0.696768265601847236467847324093 294,294_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.379140968943526113221764717309 295,295_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.383046815159447273657633559196,4,0.456288884020715213019059319777 296,296_0,COMPLETED,BoTorch,0.283070767691922964282014163473,134,1.000000000000000000000000000000,2,0.200000000000000011102230246252 297,297_0,COMPLETED,BoTorch,0.306576644161040245961658001761,153,1.000000000000000000000000000000,2,0.620471477037471741411422954116 298,298_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,1.000000000000000000000000000000,2,0.454921349971635469167097198806 299,299_0,COMPLETED,BoTorch,0.294573643410852681334688440984,141,0.529306429997960514732824321982,4,0.460062570952162686044317752021 300,300_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.295711213702062214458976541209,4,0.461894378669080785115852449962 301,301_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.378266192146028523701772883214 302,302_0,COMPLETED,BoTorch,0.274818704676169089928805533418,131,1.000000000000000000000000000000,3,0.616622157130604753305647136585 303,303_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.377171915208691310716915268131 304,304_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.371761937848294587993791537883 305,305_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.370534762480242862991985930421 306,306_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.762625246910083487428266835195,4,0.573666527043149576670089118124 307,307_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.647695718996099678577138547553,4,0.619933025815884497511376594048 308,308_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.366748736894267068908703777197 309,309_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,2,0.476200186878467157658434416589 310,310_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.920911740206398654606800846523,3,0.589396897793263541132091631880 311,311_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.491113369284582046425668977463,2,0.266359857455391035863101478753 312,312_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.372839774533017476176866011883 313,313_0,COMPLETED,BoTorch,0.284821205301325375280896423646,140,0.648487957130945424921719677513,3,0.376006982045066884268180729123 314,314_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,1.000000000000000000000000000000,2,0.800000000000000044408920985006 315,315_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.372097758162890257516153269535 316,316_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,3,0.597073646865899809732525227446 317,317_0,COMPLETED,BoTorch,0.299824956239059803309032758989,135,0.827026791976526398642022286367,2,0.494435537430720661866700993414 318,318_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,0.745391413098200228404266454163,4,0.329220049971043371428436330461 319,319_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,1.000000000000000000000000000000,2,0.200000000000000011102230246252 320,320_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.355810006149157198596100215582 321,321_0,COMPLETED,BoTorch,0.277319329832458105755677024717,132,1.000000000000000000000000000000,3,0.544117701807304010586108233838 322,322_0,COMPLETED,BoTorch,0.299824956239059803309032758989,144,0.598928960994094095049433690292,2,0.580430274457044315106202247989 323,323_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.776550343627608552488084114884,2,0.394422867762336260000211041188 324,324_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.351377457791350267246599514692 325,325_0,COMPLETED,BoTorch,0.286071517879469827683180938038,133,0.640570597045141632008835586021,3,0.630731672418594402351743610780 326,326_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.677483119244375764367305237101,2,0.200000000000000011102230246252 327,327_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,4,0.525092678581893457234741617867 328,328_0,COMPLETED,BoTorch,0.343335833958489655692858377734,248,0.665717795880264917585122930177,3,0.352743170199508460083137606489 329,329_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.601600510989812486961625381809,2,0.439965251976133120415113353374 330,330_0,COMPLETED,BoTorch,0.267066766691672952127589724114,107,0.904910323058552235053753065586,4,0.338583103791986195219010369328 331,331_0,COMPLETED,BoTorch,0.320830207551887958139502643462,157,0.566076796214043165278440028487,4,0.392483898884218695979342328428 332,332_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 333,333_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.346938986638215596247647454220 334,334_0,COMPLETED,BoTorch,0.276319079769942521629388920701,102,1.000000000000000000000000000000,4,0.797650014636681170543397456640 335,335_0,COMPLETED,BoTorch,0.292073018254563665507816949685,127,0.772521224032820863492077023693,3,0.526138142727633151274346801074 336,336_0,COMPLETED,BoTorch,0.286071517879469827683180938038,140,0.765452887083384991839807298675,2,0.200000000000000011102230246252 337,337_0,COMPLETED,BoTorch,0.262815703925981525301835972641,120,1.000000000000000000000000000000,4,0.485506943096034204732092121048 338,338_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.352272778737810432492238987834 339,339_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.100000000000000005551115123126,4,0.800000000000000044408920985006 340,340_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.478862953149110204265070933616,3,0.502821308996568738791665964527 341,341_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.346535899222528864793702041425 342,342_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,4,0.430498586441823039017151586449 343,343_0,COMPLETED,BoTorch,0.339584896224055965419097447011,331,0.209402445890009392126529519373,4,0.560447153076529591686494313763 344,344_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.716158284940703993015631567687,2,0.606057643034333848319761273160 345,345_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.371618402578465345520442042471 346,346_0,COMPLETED,BoTorch,0.291322830707676949657525256043,139,0.315122530730716887692466343651,4,0.635071163932410254204796729027 347,347_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,2,0.800000000000000044408920985006 348,348_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.374034753958239818416586786043 349,349_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.369309990871746651741602818220 350,350_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.370351275521700162851601589864 351,351_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.554966917646870117053481408220,2,0.800000000000000044408920985006 352,352_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,4,0.449437308160192561246049081092 353,353_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.376847657684939441047333730239 354,354_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.344644762870198628768036996917 355,355_0,COMPLETED,BoTorch,0.304076019004751230134786510462,162,0.507606027169142048904859620961,4,0.404042353785717178737968424684 356,356_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.360501790394709198395162275119 357,357_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.363228489774211071861031996377 358,358_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.361225729561333919193089059263 359,359_0,COMPLETED,BoTorch,0.279319829957489385030555695266,115,0.844782890543848585807040763029,3,0.581375512015207762672730495979 360,360_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.234476123489465365645756378399,4,0.600745109895956863610422260535 361,361_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.357369846007610902915985207073 362,362_0,COMPLETED,BoTorch,0.332333083270817675192176920973,223,1.000000000000000000000000000000,3,0.200000000000000011102230246252 363,363_0,COMPLETED,BoTorch,0.268317079269817404529874238506,119,1.000000000000000000000000000000,4,0.785992122381078406334609098849 364,364_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.368663830136781611734875241382 365,365_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.358143044386928122158053611201 366,366_0,COMPLETED,BoTorch,0.276319079769942521629388920701,102,0.999412330351672428818687876628,4,0.796259607817824344166979244619 367,367_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.692187289041971243186424089799,4,0.745294659097976985862032961450 368,368_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.385393438797286824737398092111 369,369_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.401676546353689767343553285173,2,0.800000000000000044408920985006 370,370_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,4,0.595840412660513463194433825265 371,371_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.363536754074063106489944630084 372,372_0,COMPLETED,BoTorch,0.332333083270817675192176920973,223,0.817007565231207655287448687886,2,0.546458496057858011951680055063 373,373_0,COMPLETED,BoTorch,0.376094023505876506874301412608,486,0.565207275332108682874832084053,2,0.348849819379121517393116391759 374,374_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.681544464593317522727033974661,3,0.800000000000000044408920985006 375,375_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.339979950372799821778357909352,3,0.377962472556345130403343546277 376,376_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.900688072709770803925266591250,3,0.800000000000000044408920985006 377,377_0,COMPLETED,BoTorch,0.258314578644661119177783348277,106,0.100000000000000005551115123126,4,0.800000000000000044408920985006 378,378_0,COMPLETED,BoTorch,0.279319829957489385030555695266,115,0.847671920698207514988098409958,3,0.578555377007153648349913055426 379,379_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.602471887758031732218455545080,4,0.800000000000000044408920985006 380,380_0,COMPLETED,BoTorch,0.294573643410852681334688440984,134,0.100000000000000005551115123126,2,0.407307649295201579242586831242 381,381_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.741205869379818471642806798627,3,0.723191571566681234983775539149 382,382_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.745361885044307892478343546827,2,0.276873424877870089044762380581 383,383_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.367332739031138222340899801566 384,384_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.309881409297940391134318360855,3,0.582957850126455179307072285155 385,385_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.788195185885804838754609136231,2,0.792782077678210850280038357596 386,386_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.233516769036349575161537472923,4,0.433904767223168397194399403816 387,387_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.273446907489193702378571515510,4,0.447216803531953033257195784245 388,388_0,COMPLETED,BoTorch,0.278819704926231537456260412000,119,0.881905539666244964180918941565,3,0.231444937465992428560213056699 389,389_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.322995936548953088696123359114,3,0.617731555312260538492807881994 390,390_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,2,0.800000000000000044408920985006 391,391_0,COMPLETED,BoTorch,0.301575393848462103285612556647,212,0.474508074072854113545361087745,3,0.200000000000000011102230246252 392,392_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.473775389618769748878435166262,4,0.497592641856966555469199420259 393,393_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.100000000000000005551115123126,4,0.498294822576290263871356955860 394,394_0,COMPLETED,BoTorch,0.333083270817704391042468614614,246,0.763990211995244417053640972881,3,0.464790229862514447933818928504 395,395_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.100000000000000005551115123126,4,0.642735395803077036447348291404 396,396_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,2,0.732032297896095807132610389090 397,397_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.249511525336271744457405930007,3,0.515769946450997607279020940041 398,398_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.345175355326192812022156886087,3,0.511452762064493748894733471388 399,399_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.309940546678504569300116600061,3,0.583056621668500030253312615969 400,400_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.900647955736050165320705218619,2,0.533016918716322951610209202045 401,401_0,COMPLETED,BoTorch,0.350587646911727945919778903772,252,1.000000000000000000000000000000,2,0.350062635330719129633791908418 402,402_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.114222794506172398154575375884,4,0.800000000000000044408920985006 403,403_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.376420100982620020602098520612 404,404_0,COMPLETED,BoTorch,0.265066266566641672852711053565,121,1.000000000000000000000000000000,3,0.393866325500411451621118885669 405,405_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.269330672302867668577164295129,3,0.479119717206920814334125680034 406,406_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.360775336142017399865267179848,3,0.494284164839690343118405735368 407,407_0,COMPLETED,BoTorch,0.262815703925981525301835972641,121,0.618871115183078357446788686502,4,0.433219056023665616272921852214 408,408_0,COMPLETED,BoTorch,0.299324831207801955734737475723,137,0.797342654691236107922236442391,3,0.289102204846215293798650236567 409,409_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.460933739731902436531640887551,3,0.419087163023928566119025163061 410,410_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.765105918252003469604005658766 411,411_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.829661718866093855773158338707,2,0.521759694545806729237824583834 412,412_0,COMPLETED,BoTorch,0.317329332333083247164040585631,157,1.000000000000000000000000000000,4,0.470093843759098573009680421819 413,413_0,COMPLETED,BoTorch,0.306326581645411377685661591386,115,0.100000000000000005551115123126,3,0.653224943427757720471049651678 414,414_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.100000000000000005551115123126,4,0.514317761719366828288002579939 415,415_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.597682227022087619872081631911,3,0.443253394888896146142087673070 416,416_0,COMPLETED,BoTorch,0.280320080020004969156843799283,133,1.000000000000000000000000000000,3,0.800000000000000044408920985006 417,417_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.781656202165017077732045436278 418,418_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.267363183558668393580148858746,3,0.200000000000000011102230246252 419,419_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.200000000000000011102230246252 420,420_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,0.100000000000000005551115123126,4,0.684037583846488272953934028919 421,421_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.368874973109499126877608432551,4,0.514890663017425698200213446398 422,422_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,0.100000000000000005551115123126,4,0.793283852038364267755810033123 423,423_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,4,0.416834078572020094721750638200 424,424_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.412713681606371785015596742596,4,0.645409703743409113307905045076 425,425_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,1.000000000000000000000000000000,2,0.800000000000000044408920985006 426,426_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,0.596828359893246229717078676913,3,0.325006645601174148918488526760 427,427_0,COMPLETED,BoTorch,0.279819954988747232604850978532,108,1.000000000000000000000000000000,2,0.800000000000000044408920985006 428,428_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.100000000000000005551115123126,4,0.598348067664245308883153029456 429,429_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.362630840739596504995745362976 430,430_0,COMPLETED,BoTorch,0.286571642910727675257476221304,126,1.000000000000000000000000000000,4,0.535386705373522064910218887235 431,431_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.674980728529784967939519901847,3,0.760242206616716931222299535875 432,432_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.682069310646121307328826333105 433,433_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.799708994208384593704863618768 434,434_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,3,0.566121064041541721678640897153 435,435_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.401258545223425744374878831877,3,0.463507586543266458800616192093 436,436_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.344612226901362062037037503615 437,437_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.548063956739078705915346745314,2,0.607525843639822982211740054481 438,438_0,COMPLETED,BoTorch,0.294573643410852681334688440984,134,0.435162907096169004894647969195,2,0.691331958695589499086509022163 439,439_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.103066223643821175404156065269,3,0.576769925107146197440499690856 440,440_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.347814359324130850659173574968 441,441_0,COMPLETED,BoTorch,0.330582645661415375215597123315,198,0.100000000000000005551115123126,2,0.200000000000000011102230246252 442,442_0,COMPLETED,BoTorch,0.302075518879719950859907839913,143,0.518158665508963167667388916016,4,0.353360534457829111865123650205 443,443_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.835648919229832753963194136304,4,0.439628729300919895983668084227 444,444_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.348840737596048111601731989140 445,445_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,2,0.421097117361204809071750787552 446,446_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.337650229893375564138580102735 447,447_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.800000000000000044408920985006 448,448_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,2,0.369217183929255732266483391868 449,449_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.800000000000000044408920985006 450,450_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.800000000000000044408920985006 451,451_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.351298373850245537752812197141 452,452_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.346770191542974948184507866245 453,453_0,COMPLETED,BoTorch,0.344836209052263087393441765016,175,0.195865026358155280838957423839,2,0.294328435073661909271436343261 454,454_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,3,0.627203350421499772338052025589 455,447_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.800000000000000044408920985006 456,456_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.363792966486353452904012328872 457,457_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,1.000000000000000000000000000000,2,0.800000000000000044408920985006 458,458_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,2,0.454075564295686406879326568742 459,459_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.105112557331285758066563573720,2,0.200000000000000011102230246252 460,450_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.800000000000000044408920985006 461,461_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.328192171021130629782192045241 462,462_0,COMPLETED,BoTorch,0.299324831207801955734737475723,143,1.000000000000000000000000000000,2,0.360026572536664701829067780636 463,463_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.310133536304270984729214433173,3,0.800000000000000044408920985006 464,464_0,COMPLETED,BoTorch,0.306326581645411377685661591386,115,0.245113547523504698988361383272,4,0.800000000000000044408920985006 465,465_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.350583923934616237261252535973 466,466_0,COMPLETED,BoTorch,0.387596899224806223926975690119,593,0.811309632570055172529066567222,2,0.502288805221354328622851426189 467,467_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.195019309195396811640321743653,3,0.694549642324797988912621349300 468,450_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.800000000000000044408920985006 469,469_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,4,0.800000000000000044408920985006 470,470_0,COMPLETED,BoTorch,0.262815703925981525301835972641,120,1.000000000000000000000000000000,4,0.320793779495005870749935183994 471,471_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,0.100000000000000005551115123126,4,0.800000000000000044408920985006 472,472_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,1.000000000000000000000000000000,4,0.800000000000000044408920985006 473,473_0,COMPLETED,BoTorch,0.299324831207801955734737475723,143,0.582452914700959611948860583652,2,0.207011162429791506500720288386 474,474_0,COMPLETED,BoTorch,0.275818954738684674055093637435,137,0.895267730174122866593222624942,3,0.717456889319511015301600309613 475,475_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.683826575239120493243660803273,4,0.523360732246596183081521758140 476,476_0,COMPLETED,BoTorch,0.324081020255063800838968290918,145,0.426354446175510437555544740462,2,0.575735287858712307951236653025 477,477_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,1.000000000000000000000000000000,4,0.352019570094081246658390682569 478,478_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.549356629389723960521507706289,4,0.428206400903140937952429112556 479,479_0,COMPLETED,BoTorch,0.356089022255563936170119632152,306,0.626011414669169652391644831368,3,0.370375719492277322153483964939 480,480_0,COMPLETED,BoTorch,0.267066766691672952127589724114,116,1.000000000000000000000000000000,4,0.800000000000000044408920985006 481,481_0,COMPLETED,BoTorch,0.260065016254063530176665608451,120,0.853789541980178645630417122447,4,0.500896547605649833379004576273 482,482_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.783875327672402200107626413228 483,483_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.362977612960554529841772364307 484,484_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.351800858328863597090929715705 485,485_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,4,0.534979366467124295425605851051 486,486_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,0.100000000000000005551115123126,4,0.668676281584206666508407579386 487,487_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.711045909478306592532703689358,3,0.440006468383705884317436130004 488,488_0,COMPLETED,BoTorch,0.309327331832958241086828365951,186,0.591177975322017990045253554854,4,0.400213050707425299634678594884 489,489_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,3,0.713697892252195265072600705025 490,490_0,COMPLETED,BoTorch,0.269817454363590947252760088304,116,0.895266105785531007832389605028,4,0.623191229394803203334163299587 491,491_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.650002808351968885958171995298,3,0.303300221835209216081352678884 492,492_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.340330137707803959123964432365 493,493_0,COMPLETED,BoTorch,0.337084271067766949592225955712,247,0.439780210046635033904749434441,2,0.304351866098853451880756892933 494,494_0,COMPLETED,BoTorch,0.320080020005001242289210949821,105,0.541925894851806977392527642223,4,0.467258981891562008570417674491 495,495_0,COMPLETED,BoTorch,0.255313828457114255776616573712,106,0.498880026271479537491870814847,3,0.508476001062399429741844869568 496,496_0,COMPLETED,BoTorch,0.250562640660165092398870001489,104,0.999993602368809408886818346218,4,0.613847061085561973570179361559 497,497_0,COMPLETED,BoTorch,0.296824206051512828885563521908,105,0.976699980754624719203604854556,2,0.200000000000000011102230246252 498,498_0,COMPLETED,BoTorch,0.252313078269567392375449799147,103,0.989350662877010900153607053653,4,0.351727088174433655254347286245 499,499_0,COMPLETED,BoTorch,0.297074268567141808183862394799,105,0.654461119449933015346232423326,4,0.200000000000000011102230246252 500,500_0,COMPLETED,BoTorch,0.256814203550887687477199960995,106,0.646246910857836809327636728995,4,0.798045727530231507884650454798 501,501_0,RUNNING,BoTorch,,105,0.662420271798276005803529642435,3,0.681309910502036042423412709468 502,502_0,RUNNING,BoTorch,,107,0.270257330926630179313008284225,2,0.678248232407570594837409316824 503,503_0,RUNNING,BoTorch,,104,0.548590630558805858463244931045,4,0.394919371316218792422603200976 504,504_0,RUNNING,BoTorch,,103,1.000000000000000000000000000000,2,0.249385079050372993059170312335 505,505_0,RUNNING,BoTorch,,106,0.105416195382791275103606665198,4,0.381102809968718814204180489469 506,506_0,RUNNING,BoTorch,,104,1.000000000000000000000000000000,2,0.486254401124945612178152032357
start_time,end_time,run_time,program_string,n_samples,const,max_depth,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727455213,1727455250,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 260 const 0.47049672976136214 max_depth 2 threshold 0.5385781390592457,260,0.47049672976136214,2,0.5385781390592457,0.3588397099274818,0,None,i7186,33,0.040438681098846144 1727455213,1727455257,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 646 const 0.49499776279553775 max_depth 4 threshold 0.7091418191790582,646,0.49499776279553775,4,0.7091418191790582,0.3895973993498375,0,None,i7186,40,0.08410435942318911 1727455213,1727455258,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 804 const 0.35519191026687624 max_depth 2 threshold 0.6886049985885621,804,0.35519191026687624,2,0.6886049985885621,0.4246061515378845,0,None,i7186,41,0.10865216304076017 1727455249,1727455289,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 0.3523334641940892 max_depth 2 threshold 0.7376988537609579,116,0.3523334641940892,2,0.7376988537609579,0.27606901725431354,0,None,i7170,20,0.0215200859038289 1727455252,1727455296,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 597 const 0.9476610513404011 max_depth 4 threshold 0.22277756594121456,597,0.9476610513404011,4,0.22277756594121456,0.38834708677169294,0,None,i7170,27,0.08452113028257063 1727455252,1727455298,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 854 const 0.6692670021206141 max_depth 2 threshold 0.3770375391468406,854,0.6692670021206141,2,0.3770375391468406,0.4163540885221305,0,None,i7170,30,0.11277819454863716 1727455240,1727455298,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 250 const 0.9295958732254803 max_depth 2 threshold 0.6493914425373077,250,0.9295958732254803,2,0.6493914425373077,0.36384096024005996,0,None,i7173,30,0.03475868967241811 1727455249,1727455299,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 919 const 0.3821979915723205 max_depth 4 threshold 0.3905004603788257,919,0.3821979915723205,4,0.3905004603788257,0.4266066516629158,0,None,i7170,30,0.10765191297824453 1727455249,1727455300,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 958 const 0.16100119967013599 max_depth 3 threshold 0.37876148205250504,958,0.16100119967013599,3,0.37876148205250504,0.42485621405351337,0,None,i7170,32,0.10852713178294573 1727455249,1727455300,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 948 const 0.614325035829097 max_depth 3 threshold 0.46614018678665164,948,0.614325035829097,3,0.46614018678665164,0.42685671417854465,0,None,i7170,32,0.1075268817204301 1727455231,1727455302,71,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 328 const 0.9875012597069144 max_depth 3 threshold 0.3511412430554629,328,0.9875012597069144,3,0.3511412430554629,0.3438359589897474,0,None,i7181,34,0.04967908643827624 1727455231,1727455303,72,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 423 const 0.31394313983619215 max_depth 3 threshold 0.2573594326153398,423,0.31394313983619215,3,0.2573594326153398,0.37384346086521636,0,None,i7181,35,0.06701675418854712 1727455231,1727455304,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 221 const 0.4909802520647645 max_depth 2 threshold 0.4271877828985453,221,0.4909802520647645,2,0.4271877828985453,0.32433108277069267,0,None,i7181,35,0.035286599427634686 1727455231,1727455304,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 449 const 0.9126628790050745 max_depth 4 threshold 0.32709901109337813,449,0.9126628790050745,4,0.32709901109337813,0.37759439859964994,0,None,i7181,36,0.06607901975493873 1727455240,1727455305,65,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 718 const 0.2041168504394591 max_depth 2 threshold 0.3480887332931161,718,0.2041168504394591,2,0.3480887332931161,0.4001000250062515,0,None,i7173,37,0.12090522630657666 1727455231,1727455306,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 709 const 0.6465989421121776 max_depth 2 threshold 0.4104600047692657,709,0.6465989421121776,2,0.4104600047692657,0.41160290072518124,0,None,i7181,38,0.1151537884471118 1727455231,1727455307,76,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 652 const 0.30554085355252025 max_depth 2 threshold 0.5875771507620813,652,0.30554085355252025,2,0.5875771507620813,0.38834708677169294,0,None,i7181,38,0.08452113028257063 1727455249,1727455308,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 652 const 0.5899349196814002 max_depth 4 threshold 0.48137060385197405,652,0.5899349196814002,4,0.48137060385197405,0.3933483370842711,0,None,i7170,39,0.08285404684504459 1727455231,1727455310,79,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 344 const 0.9354640813544393 max_depth 4 threshold 0.29447246640920643,344,0.9354640813544393,4,0.29447246640920643,0.36384096024005996,0,None,i7181,42,0.055613903475868975 1727455231,1727455327,96,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 290 const 0.807349003944546 max_depth 4 threshold 0.3863000260666013,290,0.807349003944546,4,0.3863000260666013,0.3543385846461615,0,None,i7181,58,0.047928648828873884 1727455433,1727455464,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.30644425185604124 max_depth 2 threshold 0.6177009266736725,100,0.30644425185604124,2,0.6177009266736725,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727455433,1727455465,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.29118911157238914 max_depth 2 threshold 0.7880168101640614,100,0.29118911157238914,2,0.7880168101640614,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455453,1727455482,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4187634371347184 max_depth 2 threshold 0.8,100,0.4187634371347184,2,0.8,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455453,1727455482,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4346433380425404 max_depth 2 threshold 0.6812121251002345,100,0.4346433380425404,2,0.6812121251002345,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455453,1727455482,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.10721909206485346 max_depth 2 threshold 0.6927662838251853,100,0.10721909206485346,2,0.6927662838251853,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455453,1727455483,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.46948430612296743 max_depth 2 threshold 0.4963027948640173,100,0.46948430612296743,2,0.4963027948640173,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455453,1727455484,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.41959055252542254 max_depth 3 threshold 0.6812185647536044,100,0.41959055252542254,3,0.6812185647536044,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455453,1727455486,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2053247961862248 max_depth 3 threshold 0.7754959190786412,100,0.2053247961862248,3,0.7754959190786412,0.30732683170792696,0,None,i7181,30,0.016729182295573894 1727455463,1727455492,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5500005772848422 max_depth 3 threshold 0.4949411729551181,100,0.5500005772848422,3,0.4949411729551181,0.30732683170792696,0,None,i7173,25,0.016729182295573894 1727455464,1727455493,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.218789498844353 max_depth 3 threshold 0.5725028309010441,100,0.218789498844353,3,0.5725028309010441,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455464,1727455493,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2732668388960609 max_depth 2 threshold 0.6699607772949558,100,0.2732668388960609,2,0.6699607772949558,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455464,1727455498,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5748224508786021 max_depth 2 threshold 0.7058613877258824,100,0.5748224508786021,2,0.7058613877258824,0.30732683170792696,0,None,i7181,29,0.016729182295573894 1727455473,1727455505,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.485681394001209 max_depth 2 threshold 0.6563820556022203,100,0.485681394001209,2,0.6563820556022203,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455473,1727455505,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3810808101706993 max_depth 3 threshold 0.7736770499823464,100,0.3810808101706993,3,0.7736770499823464,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455493,1727455521,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.29327433144556897 max_depth 2 threshold 0.7028960709455674,100,0.29327433144556897,2,0.7028960709455674,0.30732683170792696,0,None,i7181,25,0.016729182295573894 1727455493,1727455522,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3260237784083736 max_depth 2 threshold 0.5544412969028488,100,0.3260237784083736,2,0.5544412969028488,0.30732683170792696,0,None,i7181,25,0.016729182295573894 1727455493,1727455522,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3038141542470423 max_depth 3 threshold 0.4909268433641707,100,0.3038141542470423,3,0.4909268433641707,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455493,1727455523,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 150 const 0.1 max_depth 2 threshold 0.7329999338011128,150,0.1,2,0.7329999338011128,0.3198299574893724,0,None,i7181,27,0.024775424625387114 1727455493,1727455524,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.21780035336856832 max_depth 2 threshold 0.49631656616616987,100,0.21780035336856832,2,0.49631656616616987,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727455493,1727455525,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.11307610040025035 max_depth 2 threshold 0.8,100,0.11307610040025035,2,0.8,0.30732683170792696,0,None,i7181,28,0.016729182295573894 1727455553,1727455585,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9553586188441748 max_depth 2 threshold 0.21899166018281244,100,0.9553586188441748,2,0.21899166018281244,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455553,1727455585,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.693822144385377 max_depth 2 threshold 0.2,100,0.693822144385377,2,0.2,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455573,1727455602,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.43049439001477446,100,0.1,3,0.43049439001477446,0.30732683170792696,0,None,i7181,25,0.016729182295573894 1727455573,1727455602,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3650269350990566 max_depth 3 threshold 0.4559979166802137,100,0.3650269350990566,3,0.4559979166802137,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455573,1727455604,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3606935240820994 max_depth 2 threshold 0.2,100,0.3606935240820994,2,0.2,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727455573,1727455604,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 157 const 0.25491757699785134 max_depth 2 threshold 0.8,157,0.25491757699785134,2,0.8,0.3175793948487122,0,None,i7181,27,0.02702759023089105 1727455573,1727455604,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 171 const 0.1333703856801595 max_depth 3 threshold 0.8,171,0.1333703856801595,3,0.8,0.32083020755188796,0,None,i7181,28,0.029189115460683354 1727455573,1727455606,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.685784010468876 max_depth 2 threshold 0.3448043648325434,100,0.685784010468876,2,0.3448043648325434,0.30732683170792696,0,None,i7181,29,0.016729182295573894 1727455693,1727455727,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.5077920751196001 max_depth 3 threshold 0.2,136,0.5077920751196001,3,0.2,0.2728182045511378,0,None,i7186,31,0.026363733790590503 1727455705,1727455734,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8878635126014839 max_depth 3 threshold 0.5413164184028063,100,0.8878635126014839,3,0.5413164184028063,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455705,1727455738,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.2505463652818398 max_depth 3 threshold 0.2426763652226365,112,0.2505463652818398,3,0.2426763652226365,0.26956739184796197,0,None,i7186,28,0.0219025344571437 1727455706,1727455738,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.3686904034362255 max_depth 2 threshold 0.6515811938784521,124,0.3686904034362255,2,0.6515811938784521,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727455713,1727455742,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 109 const 0.2725548878707711 max_depth 3 threshold 0.3022145983807828,109,0.2725548878707711,3,0.3022145983807828,0.2883220805201301,0,None,i7181,26,0.019643799838848598 1727455713,1727455743,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.30012237153425825 max_depth 2 threshold 0.2,143,0.30012237153425825,2,0.2,0.29932483120780196,0,None,i7181,27,0.026352742031661762 1727455733,1727455762,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7059555291391088 max_depth 3 threshold 0.2,100,0.7059555291391088,3,0.2,0.30732683170792696,0,None,i7181,25,0.016729182295573894 1727455733,1727455763,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2409510071716282 max_depth 4 threshold 0.3385996699883417,100,0.2409510071716282,4,0.3385996699883417,0.30732683170792696,0,None,i7181,26,0.016729182295573894 1727455734,1727455764,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6969182792520834 max_depth 4 threshold 0.7431449191534336,100,0.6969182792520834,4,0.7431449191534336,0.30732683170792696,0,None,i7181,27,0.016729182295573894 1727455733,1727455765,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.15298941044173767 max_depth 4 threshold 0.547622286486337,100,0.15298941044173767,4,0.547622286486337,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727455733,1727455766,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.30732683170792696,0,None,i7181,29,0.016729182295573894 1727455733,1727455770,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 145 const 0.3144862900976875 max_depth 4 threshold 0.47209633811588714,145,0.3144862900976875,4,0.47209633811588714,0.29057264316079023,0,None,i7181,33,0.027025987266047276 1727455853,1727455886,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.8417610085460264 max_depth 2 threshold 0.2501544674498828,128,0.8417610085460264,2,0.2501544674498828,0.2825706426606651,0,None,i7186,29,0.023955988997249315 1727455853,1727455887,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.8075833846082696 max_depth 3 threshold 0.39083200813681007,127,0.8075833846082696,3,0.39083200813681007,0.29207301825456367,0,None,i7186,30,0.02332249729098941 1727455873,1727455903,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 1 max_depth 3 threshold 0.6396535939775415,130,1,3,0.6396535939775415,0.30932733183295824,0,None,i7181,27,0.02217220971909644 1727455873,1727455904,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.5514336427030195 max_depth 2 threshold 0.2,126,0.5514336427030195,2,0.2,0.2943235808952238,0,None,i7181,27,0.02317245978161207 1727455873,1727455906,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 1 max_depth 3 threshold 0.2,131,1,3,0.2,0.27256814203550883,0,None,i7181,29,0.024622822372259733 1727455873,1727455906,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.4550844011129054 max_depth 3 threshold 0.29605778276009165,124,0.4550844011129054,3,0.29605778276009165,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727455873,1727455908,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.8034333437271204 max_depth 4 threshold 0.5377285002669003,131,0.8034333437271204,4,0.5377285002669003,0.26881720430107525,0,None,i7181,31,0.02487288488788864 1727455887,1727455917,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 1 max_depth 2 threshold 0.523369565286157,128,1,2,0.523369565286157,0.2825706426606651,0,None,i7181,26,0.023955988997249315 1727455887,1727455917,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.5744295735428171 max_depth 3 threshold 0.6223265019381412,126,0.5744295735428171,3,0.6223265019381412,0.2943235808952238,0,None,i7181,27,0.02317245978161207 1727455887,1727455926,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.8157206861001234 max_depth 4 threshold 0.23900403995604963,131,0.8157206861001234,4,0.23900403995604963,0.26881720430107525,0,None,i7181,35,0.02487288488788864 1727455977,1727456010,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.1 max_depth 4 threshold 0.2,120,0.1,4,0.2,0.30482620655163795,0,None,i7186,29,0.02106776694173543 1727455993,1727456026,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.1 max_depth 3 threshold 0.2,119,0.1,3,0.2,0.28307076769192296,0,None,i7186,29,0.022427481870467617 1727455993,1727456026,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.5407064769940794 max_depth 4 threshold 0.2,121,0.5407064769940794,4,0.2,0.2658164541135284,0,None,i7186,29,0.023505876469117278 1727456007,1727456036,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 0.1 max_depth 2 threshold 0.2,118,0.1,2,0.2,0.27606901725431354,0,None,i7181,26,0.022865091272818206 1727456007,1727456037,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.1 max_depth 4 threshold 0.2,124,0.1,4,0.2,0.28157039259814953,0,None,i7181,27,0.022521255313828457 1727456007,1727456037,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 0.49458974640462283 max_depth 3 threshold 0.2,118,0.49458974640462283,3,0.2,0.27606901725431354,0,None,i7181,27,0.022865091272818206 1727456013,1727456043,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.1 max_depth 4 threshold 0.5905626077769777,120,0.1,4,0.5905626077769777,0.30482620655163795,0,None,i7181,26,0.02106776694173543 1727456013,1727456047,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.4385178138724838 max_depth 4 threshold 0.2,120,0.4385178138724838,4,0.2,0.30482620655163795,0,None,i7181,29,0.02106776694173543 1727456034,1727456066,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 1 max_depth 3 threshold 0.2,117,1,3,0.2,0.2755688922230558,0,None,i7186,29,0.02154950502331465 1727456034,1727456066,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.7390827853764351 max_depth 3 threshold 0.2,117,0.7390827853764351,3,0.2,0.2755688922230558,0,None,i7186,29,0.02154950502331465 1727456034,1727456069,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 4 threshold 0.2,121,1,4,0.2,0.264066016504126,0,None,i7186,31,0.02361527881970493 1727456173,1727456200,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 1 max_depth 2 threshold 0.2,137,1,2,0.2,0.27156789197299325,0,None,i7181,23,0.026453041831886542 1727456173,1727456200,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 0.8630041085130817 max_depth 3 threshold 0.2,116,0.8630041085130817,3,0.2,0.26981745436359095,0,None,i7181,24,0.021887824897400817 1727456173,1727456200,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 1 max_depth 4 threshold 0.3611720357514722,123,1,4,0.3611720357514722,0.2720680170042511,0,None,i7181,24,0.02311515378844711 1727456167,1727456202,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 4 threshold 0.6343663323503084,121,1,4,0.6343663323503084,0.2685671417854464,0,None,i7186,31,0.023333958489622404 1727456167,1727456203,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 0.8741243026622395 max_depth 4 threshold 0.2,116,0.8741243026622395,4,0.2,0.2738184546136534,0,None,i7186,32,0.02165247194151479 1727456167,1727456206,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 1 max_depth 3 threshold 0.2,140,1,3,0.2,0.2928232058014504,0,None,i7186,35,0.02493480512985389 1727456188,1727456215,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.9758524083977025 max_depth 4 threshold 0.2,117,0.9758524083977025,4,0.2,0.2755688922230558,0,None,i7181,23,0.02154950502331465 1727456188,1727456215,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 1 max_depth 4 threshold 0.3946091540101478,122,1,4,0.3946091540101478,0.2763190797699425,0,None,i7181,23,0.022849462365591395 1727456188,1727456216,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 1 max_depth 2 threshold 0.2,115,1,2,0.2,0.2685671417854464,0,None,i7181,24,0.021961372696115204 1727456193,1727456220,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 0.6112231779409161 max_depth 3 threshold 0.2,115,0.6112231779409161,3,0.2,0.2935733933483371,0,None,i7181,24,0.020490416721827515 1727456214,1727456247,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.3518814291803197 max_depth 3 threshold 0.3027015874482698,132,0.3518814291803197,3,0.3027015874482698,0.28307076769192296,0,None,i7186,29,0.023922647328498792 1727456215,1727456265,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 1 max_depth 4 threshold 0.2,140,1,4,0.2,0.3015753938484621,0,None,i7186,46,0.024309648840781625 1727456314,1727456348,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 1 max_depth 4 threshold 0.4408106892829142,129,1,4,0.4408106892829142,0.28157039259814953,0,None,i7186,31,0.024022672334750354 1727456334,1727456369,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 1 max_depth 4 threshold 0.4324976613046345,129,1,4,0.4324976613046345,0.28157039259814953,0,None,i7186,31,0.024022672334750354 1727456339,1727456369,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 0.1 max_depth 3 threshold 0.2,133,0.1,3,0.2,0.2860715178794698,0,None,i7181,27,0.02372259731599567 1727456334,1727456376,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 1 max_depth 4 threshold 0.2,125,1,4,0.2,0.30407601900475123,0,None,i7186,38,0.02252229724097691 1727456353,1727456384,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 149 const 1 max_depth 2 threshold 0.2,149,1,2,0.2,0.2943235808952238,0,None,i7181,28,0.026737453594167772 1727456354,1727456388,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.932849181146925 max_depth 4 threshold 0.2,124,0.932849181146925,4,0.2,0.2783195798949737,0,None,i7186,30,0.022724431107776947 1727456353,1727456392,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 521 const 0.2099989311509579 max_depth 2 threshold 0.716665702831518,521,0.2099989311509579,2,0.716665702831518,0.3843460865216304,0,None,i7181,36,0.08585479703259148 1727456370,1727456400,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.994564123595891 max_depth 2 threshold 0.20087758376387446,137,0.994564123595891,2,0.20087758376387446,0.27156789197299325,0,None,i7181,26,0.026453041831886542 1727456369,1727456401,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 177 const 1 max_depth 2 threshold 0.2,177,1,2,0.2,0.3168292073018255,0,None,i7181,28,0.029552842756143574 1727456374,1727456408,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 172 const 0.8910769781124659 max_depth 2 threshold 0.23914873396435948,172,0.8910769781124659,2,0.23914873396435948,0.3123280820205051,0,None,i7186,30,0.02996203596353634 1727456369,1727456412,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 524 const 0.30062407197698976 max_depth 3 threshold 0.5140606220485004,524,0.30062407197698976,3,0.5140606220485004,0.3905976494123531,0,None,i7181,39,0.08377094273568392 1727456394,1727456427,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.7949242735751338 max_depth 4 threshold 0.2,107,0.7949242735751338,4,0.2,0.26706676669167295,0,None,i7186,29,0.020824650607096217 1727456394,1727456428,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 1 max_depth 2 threshold 0.2,135,1,2,0.2,0.2843210802700675,0,None,i7186,31,0.025542099810666952 1727456399,1727456429,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 1 max_depth 4 threshold 0.43539819092756193,130,1,4,0.43539819092756193,0.30932733183295824,0,None,i7181,26,0.02217220971909644 1727456574,1727456607,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.8150061370917215 max_depth 4 threshold 0.479645736219914,122,0.8150061370917215,4,0.479645736219914,0.2763190797699425,0,None,i7186,30,0.022849462365591395 1727456574,1727456608,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 1 max_depth 2 threshold 0.2,126,1,2,0.2,0.2888222055513878,0,None,i7186,30,0.0235392181378678 1727456581,1727456620,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.7420236736862662 max_depth 4 threshold 0.2,125,0.7420236736862662,4,0.2,0.30557639409852466,0,None,i7186,35,0.022422272234725347 1727456593,1727456624,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.8105693951064488 max_depth 4 threshold 0.499766488547969,123,0.8105693951064488,4,0.499766488547969,0.2720680170042511,0,None,i7181,27,0.02311515378844711 1727456593,1727456625,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 174 const 0.4829332930326329 max_depth 4 threshold 0.2,174,0.4829332930326329,4,0.2,0.31832958239559894,0,None,i7181,28,0.02941644502034599 1727456593,1727456626,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 184 const 0.7421236420835716 max_depth 4 threshold 0.39600414932873834,184,0.7421236420835716,4,0.39600414932873834,0.3090772693173294,0,None,i7181,29,0.03328332083020755 1727456611,1727456640,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 1 max_depth 3 threshold 0.2,123,1,3,0.2,0.2720680170042511,0,None,i7181,26,0.02311515378844711 1727456611,1727456646,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 197 const 0.1 max_depth 4 threshold 0.2,197,0.1,4,0.2,0.3278319579894974,0,None,i7181,32,0.03140785196299074 1727456614,1727456648,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.7587203114145864 max_depth 3 threshold 0.2,125,0.7587203114145864,3,0.2,0.30582645661415353,0,None,i7186,31,0.022405601400350087 1727456634,1727456668,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.690125469209516 max_depth 4 threshold 0.2,127,0.690125469209516,4,0.2,0.29207301825456367,0,None,i7186,31,0.02332249729098941 1727456634,1727456669,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 150 const 0.4706792024427422 max_depth 4 threshold 0.2,150,0.4706792024427422,4,0.2,0.3198299574893724,0,None,i7186,31,0.024775424625387114 1727456641,1727456671,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 180 const 0.3292359352892704 max_depth 3 threshold 0.2,180,0.3292359352892704,3,0.2,0.32283070767691924,0,None,i7181,27,0.029007251812953237 1727456655,1727456687,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.18731680414958307 max_depth 2 threshold 0.43558954992891596,123,0.18731680414958307,2,0.43558954992891596,0.2720680170042511,0,None,i7186,28,0.02311515378844711 1727456822,1727456855,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 109 const 1 max_depth 4 threshold 0.34408237905918726,109,1,4,0.34408237905918726,0.2810702675668917,0,None,i7186,30,0.02004667833625073 1727456834,1727456872,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.8178260423002272 max_depth 4 threshold 0.8,135,0.8178260423002272,4,0.8,0.2843210802700675,0,None,i7186,35,0.025542099810666952 1727456852,1727456882,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.7718614561668491 max_depth 4 threshold 0.8,130,0.7718614561668491,4,0.8,0.30932733183295824,0,None,i7181,27,0.02217220971909644 1727456852,1727456882,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 1 max_depth 4 threshold 0.37579639707378365,111,1,4,0.37579639707378365,0.27331832958239555,0,None,i7181,27,0.020477341557611627 1727456854,1727456884,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.8490310306011696 max_depth 4 threshold 0.4443893552300592,117,0.8490310306011696,4,0.4443893552300592,0.2755688922230558,0,None,i7181,27,0.02154950502331465 1727456852,1727456885,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 109 const 1 max_depth 4 threshold 0.3124529158170141,109,1,4,0.3124529158170141,0.2810702675668917,0,None,i7186,30,0.02004667833625073 1727456874,1727456904,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.8336918571953346 max_depth 4 threshold 0.31385672882765814,120,0.8336918571953346,4,0.31385672882765814,0.26006501625406353,0,None,i7181,27,0.023865341335333832 1727456874,1727456905,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.18393182177242567 max_depth 3 threshold 0.3116387791160486,125,0.18393182177242567,3,0.3116387791160486,0.3215803950987747,0,None,i7181,27,0.020020630157539385 1727456874,1727456909,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 172 const 0.38614689857009965 max_depth 4 threshold 0.2,172,0.38614689857009965,4,0.2,0.3123280820205051,0,None,i7186,31,0.02996203596353634 1727456883,1727456919,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 198 const 0.43019636121325644 max_depth 4 threshold 0.2,198,0.43019636121325644,4,0.2,0.3305826456614154,0,None,i7186,32,0.031132783195798947 1727456894,1727456929,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 179 const 0.1646877663078135 max_depth 4 threshold 0.2124502601037914,179,0.1646877663078135,4,0.2124502601037914,0.3090772693173294,0,None,i7186,31,0.03025756439109777 1727456912,1727456942,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 1 max_depth 4 threshold 0.41090205336549657,112,1,4,0.41090205336549657,0.2593148287071768,0,None,i7181,27,0.02250562640660165 1727456912,1727456943,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 181 const 0.5514956776687692 max_depth 3 threshold 0.2016275168437259,181,0.5514956776687692,3,0.2016275168437259,0.3050762690672668,0,None,i7181,27,0.030621291686558003 1727457094,1727457129,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 1 max_depth 4 threshold 0.31787582548797616,118,1,4,0.31787582548797616,0.28507126781695424,0,None,i7186,32,0.022302450612653162 1727457114,1727457145,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 1 max_depth 3 threshold 0.3171292416555973,118,1,3,0.3171292416555973,0.28507126781695424,0,None,i7181,27,0.022302450612653162 1727457114,1727457147,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.5663995986315987 max_depth 2 threshold 0.4035218294216234,131,0.5663995986315987,2,0.4035218294216234,0.27706926731682924,0,None,i7186,29,0.02432274735350504 1727457114,1727457147,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 0.7893342731031316 max_depth 2 threshold 0.36438734032189596,133,0.7893342731031316,2,0.36438734032189596,0.28332083020755183,0,None,i7186,29,0.023905976494123533 1727457123,1727457152,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.5097422130520803 max_depth 2 threshold 0.44339433100429826,129,0.5097422130520803,2,0.44339433100429826,0.2820705176294074,0,None,i7181,26,0.02398933066599983 1727457123,1727457156,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.993123264978425 max_depth 4 threshold 0.38035131706063097,114,0.993123264978425,4,0.38035131706063097,0.2848212053013254,0,None,i7181,30,0.021005251312828203 1727457134,1727457167,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 1 max_depth 2 threshold 0.3613286905397014,128,1,2,0.3613286905397014,0.2825706426606651,0,None,i7186,29,0.023955988997249315 1727457154,1727457184,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.8055434724010365 max_depth 4 threshold 0.36951863829619525,119,0.8055434724010365,4,0.36951863829619525,0.2683170792698174,0,None,i7181,27,0.023349587396849215 1727457154,1727457186,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.1 max_depth 2 threshold 0.3755551437061684,126,0.1,2,0.3755551437061684,0.2943235808952238,0,None,i7186,29,0.02317245978161207 1727457154,1727457187,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 0.4996765271382282 max_depth 2 threshold 0.47326759253642603,133,0.4996765271382282,2,0.47326759253642603,0.2860715178794698,0,None,i7186,29,0.02372259731599567 1727457174,1727457206,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.615425905535866 max_depth 3 threshold 0.3955979990654058,124,0.615425905535866,3,0.3955979990654058,0.28157039259814953,0,None,i7186,28,0.022521255313828457 1727457174,1727457206,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 219 const 0.1 max_depth 4 threshold 0.8,219,0.1,4,0.8,0.3213303325831458,0,None,i7181,29,0.035620016115139895 1727457184,1727457215,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 1 max_depth 3 threshold 0.32627384316126207,127,1,3,0.32627384316126207,0.28932233058264567,0,None,i7181,27,0.023505876469117278 1727457334,1727457367,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 1 max_depth 2 threshold 0.7451804330829648,130,1,2,0.7451804330829648,0.30932733183295824,0,None,i7186,29,0.02217220971909644 1727457354,1727457383,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.3651709706674877 max_depth 2 threshold 0.31428181646635933,130,0.3651709706674877,2,0.31428181646635933,0.30932733183295824,0,None,i7181,26,0.02217220971909644 1727457355,1727457387,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.8698819375283763 max_depth 2 threshold 0.2,125,0.8698819375283763,2,0.2,0.3215803950987747,0,None,i7186,29,0.020020630157539385 1727457355,1727457389,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.2,113,1,4,0.2,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727457365,1727457394,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.707053424378947 max_depth 2 threshold 0.77945458453096,128,0.707053424378947,2,0.77945458453096,0.2825706426606651,0,None,i7181,25,0.023955988997249315 1727457374,1727457407,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.5786958793378744 max_depth 2 threshold 0.38506431636443206,128,0.5786958793378744,2,0.38506431636443206,0.2825706426606651,0,None,i7186,28,0.023955988997249315 1727457395,1727457427,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.16813321627317646 max_depth 2 threshold 0.2,130,0.16813321627317646,2,0.2,0.30932733183295824,0,None,i7186,29,0.02217220971909644 1727457394,1727457427,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 231 const 0.36309571536645646 max_depth 4 threshold 0.8,231,0.36309571536645646,4,0.8,0.34058514628657166,0,None,i7181,30,0.03766566641660415 1727457395,1727457428,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 1 max_depth 2 threshold 0.8,137,1,2,0.8,0.27156789197299325,0,None,i7186,30,0.026453041831886542 1727457414,1727457444,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.5485154950069308 max_depth 3 threshold 0.46174143384549804,129,0.5485154950069308,3,0.46174143384549804,0.2820705176294074,0,None,i7181,26,0.02398933066599983 1727457415,1727457451,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.823343898349618 max_depth 4 threshold 0.3114749786680955,114,0.823343898349618,4,0.3114749786680955,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727457425,1727457455,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.5081776235830704 max_depth 2 threshold 0.3609752774007868,128,0.5081776235830704,2,0.3609752774007868,0.2825706426606651,0,None,i7181,26,0.023955988997249315 1727457595,1727457632,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 145 const 0.7634973783532122 max_depth 4 threshold 0.6561207745591237,145,0.7634973783532122,4,0.6561207745591237,0.28957239309827454,0,None,i7186,33,0.027102929578548485 1727457615,1727457650,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 161 const 1 max_depth 4 threshold 0.7602599559636118,161,1,4,0.7602599559636118,0.3035758939734934,0,None,i7186,31,0.028194548637159287 1727457635,1727457664,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.8863165380917966 max_depth 4 threshold 0.3502182938150402,122,0.8863165380917966,4,0.3502182938150402,0.2763190797699425,0,None,i7181,26,0.022849462365591395 1727457635,1727457664,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.7787647034400614 max_depth 3 threshold 0.5393874034677709,137,0.7787647034400614,3,0.5393874034677709,0.27156789197299325,0,None,i7181,27,0.026453041831886542 1727457635,1727457668,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.9377645968060956 max_depth 4 threshold 0.39412414601935997,113,0.9377645968060956,4,0.39412414601935997,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727457655,1727457690,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 168 const 0.7385726070679202 max_depth 4 threshold 0.8,168,0.7385726070679202,4,0.8,0.3185796449112278,0,None,i7186,31,0.02939371206437973 1727457655,1727457691,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 153 const 1 max_depth 3 threshold 0.6284935051136862,153,1,3,0.6284935051136862,0.2980745186296574,0,None,i7186,33,0.028652996582478954 1727457667,1727457697,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 148 const 0.7515667121225547 max_depth 3 threshold 0.5446115841315811,148,0.7515667121225547,3,0.5446115841315811,0.29307326831707925,0,None,i7181,27,0.026833631484794278 1727457667,1727457704,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.7979707243717494 max_depth 4 threshold 0.6830570127185887,149,0.7979707243717494,4,0.6830570127185887,0.2880720180045011,0,None,i7181,34,0.027218343047300288 1727457694,1727457724,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.629764129201448 max_depth 4 threshold 0.588088343398097,123,0.629764129201448,4,0.588088343398097,0.2720680170042511,0,None,i7181,26,0.02311515378844711 1727457695,1727457729,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.9157633772890781 max_depth 4 threshold 0.3786506204790475,112,0.9157633772890781,4,0.3786506204790475,0.2585646411602901,0,None,i7186,30,0.022549755085830278 1727457695,1727457731,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 241 const 0.1 max_depth 3 threshold 0.43449429611422474,241,0.1,3,0.43449429611422474,0.32883220805201296,0,None,i7186,33,0.039134783695923984 1727457915,1727457948,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.9629646985483571 max_depth 3 threshold 0.4276588235495494,129,0.9629646985483571,3,0.4276588235495494,0.2820705176294074,0,None,i7186,29,0.02398933066599983 1727457935,1727457968,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 153 const 1 max_depth 2 threshold 0.5871534765241251,153,1,2,0.5871534765241251,0.30657664416104025,0,None,i7186,29,0.025794910266028044 1727457935,1727457969,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.7458221167849169 max_depth 2 threshold 0.5441219418151052,135,0.7458221167849169,2,0.5441219418151052,0.2998249562390598,0,None,i7186,30,0.024434680098596073 1727457955,1727457988,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 141 const 1 max_depth 2 threshold 0.49605572335799863,141,1,2,0.49605572335799863,0.27981995498874723,0,None,i7186,29,0.025863608759332687 1727457955,1727457988,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.8276115755330531 max_depth 3 threshold 0.2687582299601084,128,0.8276115755330531,3,0.2687582299601084,0.2825706426606651,0,None,i7186,29,0.023955988997249315 1727457969,1727457998,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.6902527470523038 max_depth 4 threshold 0.3793161232323983,124,0.6902527470523038,4,0.3793161232323983,0.28157039259814953,0,None,i7181,26,0.022521255313828457 1727457975,1727458008,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.8295312738459493 max_depth 2 threshold 0.6040761180932889,141,0.8295312738459493,2,0.6040761180932889,0.2845711427856964,0,None,i7186,29,0.025524238202407745 1727457995,1727458028,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 138 const 0.8928582503155316 max_depth 2 threshold 0.5083441524982403,138,0.8928582503155316,2,0.5083441524982403,0.27981995498874723,0,None,i7186,29,0.025863608759332687 1727457995,1727458029,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.8022766164790834 max_depth 3 threshold 0.3757731981102842,137,0.8022766164790834,3,0.3757731981102842,0.27156789197299325,0,None,i7186,30,0.026453041831886542 1727458015,1727458048,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 138 const 0.7030591457655783 max_depth 2 threshold 0.502117528977239,138,0.7030591457655783,2,0.502117528977239,0.27981995498874723,0,None,i7186,29,0.025863608759332687 1727458015,1727458049,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.8018265146576224 max_depth 4 threshold 0.38248915896714764,122,0.8018265146576224,4,0.38248915896714764,0.2763190797699425,0,None,i7186,30,0.022849462365591395 1727458029,1727458058,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 1 max_depth 2 threshold 0.46927384885297946,137,1,2,0.46927384885297946,0.27156789197299325,0,None,i7181,26,0.026453041831886542 1727458035,1727458068,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3419265621722028,113,1,4,0.3419265621722028,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727458055,1727458091,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3612184199013333,114,1,4,0.3612184199013333,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727458236,1727458273,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 277 const 0.6520373578896974 max_depth 2 threshold 0.7583493424221763,277,0.6520373578896974,2,0.7583493424221763,0.3358339584896224,0,None,i7186,33,0.043725217018540354 1727458256,1727458290,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.8665551814110258 max_depth 4 threshold 0.3544462277554467,120,0.8665551814110258,4,0.3544462277554467,0.26006501625406353,0,None,i7186,30,0.023865341335333832 1727458256,1727458290,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 1 max_depth 4 threshold 0.3833056195742185,119,1,4,0.3833056195742185,0.26781695423855967,0,None,i7186,31,0.023380845211302823 1727458270,1727458303,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.6152400761513456 max_depth 4 threshold 0.46003800078197604,131,0.6152400761513456,4,0.46003800078197604,0.27881970492623154,0,None,i7181,29,0.024206051512878222 1727458271,1727458305,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.8910110856979583 max_depth 4 threshold 0.3691204282754703,119,0.8910110856979583,4,0.3691204282754703,0.2683170792698174,0,None,i7186,31,0.023349587396849215 1727458276,1727458306,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 1 max_depth 4 threshold 0.29811071088016916,117,1,4,0.29811071088016916,0.2755688922230558,0,None,i7181,27,0.02154950502331465 1727458296,1727458329,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.3914196700867163 max_depth 3 threshold 0.6515830999251494,132,0.3914196700867163,3,0.6515830999251494,0.28307076769192296,0,None,i7186,30,0.023922647328498792 1727458296,1727458330,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 1 max_depth 4 threshold 0.20456178278497678,106,1,4,0.20456178278497678,0.2530632658164541,0,None,i7186,30,0.021602622877941707 1727458316,1727458353,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 272 const 0.8564975061720691 max_depth 2 threshold 0.39685432968316303,272,0.8564975061720691,2,0.39685432968316303,0.3365841460365091,0,None,i7186,34,0.043618047368985106 1727458330,1727458359,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 1 max_depth 2 threshold 0.32243805545525683,131,1,2,0.32243805545525683,0.27706926731682924,0,None,i7181,25,0.02432274735350504 1727458331,1727458367,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.9128710132825564 max_depth 4 threshold 0.3964252747011864,114,0.9128710132825564,4,0.3964252747011864,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727458356,1727458389,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.6343708977591714 max_depth 4 threshold 0.20268090784554502,117,0.6343708977591714,4,0.20268090784554502,0.2755688922230558,0,None,i7186,29,0.02154950502331465 1727458356,1727458391,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.9362616937524303 max_depth 4 threshold 0.4416207547262797,121,0.9362616937524303,4,0.4416207547262797,0.2685671417854464,0,None,i7186,31,0.023333958489622404 1727458542,1727458575,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 4 threshold 0.3068614558178279,108,1,4,0.3068614558178279,0.27981995498874723,0,None,i7186,29,0.020116140146147644 1727458572,1727458605,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 107 const 1 max_depth 4 threshold 0.29663526921744854,107,1,4,0.29663526921744854,0.26706676669167295,0,None,i7186,29,0.020824650607096217 1727458572,1727458606,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 1 max_depth 4 threshold 0.2675609303791565,106,1,4,0.2675609303791565,0.2530632658164541,0,None,i7186,30,0.021602622877941707 1727458576,1727458610,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.5584575068555879 max_depth 3 threshold 0.49536822681570686,131,0.5584575068555879,3,0.49536822681570686,0.27706926731682924,0,None,i7186,31,0.02432274735350504 1727458596,1727458630,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 1 max_depth 4 threshold 0.2,112,1,4,0.2,0.2593148287071768,0,None,i7186,30,0.02250562640660165 1727458596,1727458636,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 105 const 1 max_depth 4 threshold 0.27670734958691223,105,1,4,0.27670734958691223,0.3140785196299075,0,None,i7186,36,0.018212886554972073 1727458616,1727458650,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.7654598288323013 max_depth 4 threshold 0.39933770886483044,126,0.7654598288323013,4,0.39933770886483044,0.2943235808952238,0,None,i7186,30,0.02317245978161207 1727458616,1727458653,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 0.8686118234509604 max_depth 4 threshold 0.3292691590595169,118,0.8686118234509604,4,0.3292691590595169,0.28507126781695424,0,None,i7186,34,0.022302450612653162 1727458632,1727458666,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 104 const 1 max_depth 4 threshold 0.2511250744220648,104,1,4,0.2511250744220648,0.2505626406601651,0,None,i7186,30,0.02059725457680209 1727458656,1727458690,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 1 max_depth 4 threshold 0.27895044821889886,106,1,4,0.27895044821889886,0.2530632658164541,0,None,i7186,30,0.021602622877941707 1727458656,1727458695,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 1 max_depth 4 threshold 0.29973514699516196,115,1,4,0.29973514699516196,0.26706676669167295,0,None,i7186,36,0.022049630054572465 1727458663,1727458701,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.5453415131766599 max_depth 2 threshold 0.4142595690871217,147,0.5453415131766599,2,0.4142595690871217,0.2898224556139035,0,None,i7186,30,0.027083694000423177 1727458676,1727458710,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 1 max_depth 4 threshold 0.30551956859564583,106,1,4,0.30551956859564583,0.2530632658164541,0,None,i7186,30,0.021602622877941707 1727458693,1727458731,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 342 const 0.700004732291977 max_depth 3 threshold 0.4825263820981352,342,0.700004732291977,3,0.4825263820981352,0.3590897724431108,0,None,i7186,35,0.05656414103525881 1727458836,1727458868,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.30732683170792696,0,None,i7186,29,0.016729182295573894 1727458856,1727458889,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.30732683170792696,0,None,i7186,29,0.016729182295573894 1727458873,1727458907,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 0.4453827032403803 max_depth 2 threshold 0.3629419938748978,115,0.4453827032403803,2,0.3629419938748978,0.3063265816454114,0,None,i7186,29,0.019740229174940793 1727458873,1727458907,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.6946429705538817 max_depth 3 threshold 0.4483621797325598,132,0.6946429705538817,3,0.4483621797325598,0.2890722680670168,0,None,i7186,30,0.023522547303492534 1727458876,1727458909,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 4 threshold 0.2,108,1,4,0.2,0.27981995498874723,0,None,i7186,29,0.020116140146147644 1727458896,1727458929,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.30732683170792696,0,None,i7186,29,0.016729182295573894 1727458903,1727458937,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 1 max_depth 4 threshold 0.24594247002380298,111,1,4,0.24594247002380298,0.27331832958239555,0,None,i7186,29,0.020477341557611627 1727458916,1727458951,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.7789547102785777 max_depth 3 threshold 0.5395423092076377,137,0.7789547102785777,3,0.5395423092076377,0.27156789197299325,0,None,i7186,31,0.026453041831886542 1727458934,1727458966,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.30732683170792696,0,None,i7186,29,0.016729182295573894 1727458934,1727458967,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 0.21778612413450346 max_depth 4 threshold 0.36530371218626334,118,0.21778612413450346,4,0.36530371218626334,0.27606901725431354,0,None,i7186,29,0.022865091272818206 1727458956,1727458996,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 343 const 0.1 max_depth 2 threshold 0.8,343,0.1,2,0.8,0.3588397099274818,0,None,i7186,36,0.0566141535383846 1727458964,1727459001,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 288 const 0.1 max_depth 2 threshold 0.8,288,0.1,2,0.8,0.3465866466616654,0,None,i7186,33,0.049220638492956575 1727459256,1727459289,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 109 const 1 max_depth 2 threshold 0.7205682060421521,109,1,2,0.7205682060421521,0.2883220805201301,0,None,i7186,28,0.019643799838848598 1727459276,1727459308,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.5631460780357533 max_depth 2 threshold 0.7496256727459767,136,0.5631460780357533,2,0.7496256727459767,0.2755688922230558,0,None,i7186,28,0.026167256099739217 1727459296,1727459330,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.8960687301238485 max_depth 3 threshold 0.5340598843690332,129,0.8960687301238485,3,0.5340598843690332,0.2820705176294074,0,None,i7186,30,0.02398933066599983 1727459296,1727459330,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 1 max_depth 4 threshold 0.3206516826626722,116,1,4,0.3206516826626722,0.2745686421605401,0,None,i7186,31,0.02160834326228616 1727459317,1727459351,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 1 max_depth 4 threshold 0.34368500532989293,112,1,4,0.34368500532989293,0.2593148287071768,0,None,i7186,30,0.02250562640660165 1727459317,1727459351,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.7825387940827303 max_depth 3 threshold 0.4958886824730965,125,0.7825387940827303,3,0.4958886824730965,0.3213303325831458,0,None,i7186,31,0.021372009669083935 1727459326,1727459359,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.5743336474458771 max_depth 3 threshold 0.6224777013454539,126,0.5743336474458771,3,0.6224777013454539,0.2943235808952238,0,None,i7186,29,0.02317245978161207 1727459337,1727459373,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.4108723440800771 max_depth 4 threshold 0.55396579276545,132,0.4108723440800771,4,0.55396579276545,0.2745686421605401,0,None,i7186,32,0.024489455697257648 1727459356,1727459388,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.36863442367673827 max_depth 2 threshold 0.6514890276731286,124,0.36863442367673827,2,0.6514890276731286,0.28157039259814953,0,None,i7186,28,0.022521255313828457 1727459356,1727459390,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.8370729303044964 max_depth 2 threshold 0.2,140,0.8370729303044964,2,0.2,0.2865716429107277,0,None,i7186,30,0.02538134533633408 1727459377,1727459410,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.6963795386612563 max_depth 2 threshold 0.8,135,0.6963795386612563,2,0.8,0.31807951987996996,0,None,i7186,30,0.02313078269567392 1727459386,1727459425,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 1 max_depth 4 threshold 0.3133979981162398,115,1,4,0.3133979981162398,0.26706676669167295,0,None,i7186,35,0.022049630054572465 1727459397,1727459430,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.5006270548171033 max_depth 4 threshold 0.6534937282781306,140,0.5006270548171033,4,0.6534937282781306,0.3365841460365091,0,None,i7186,30,0.021809023684492553 1727459717,1727459750,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 3 threshold 0.2,110,1,3,0.2,0.3178294573643411,0,None,i7186,30,0.01800450112528132 1727459737,1727459771,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 4 threshold 0.2111782120190443,110,1,4,0.2111782120190443,0.2740685171292824,0,None,i7186,31,0.02043566447167347 1727459748,1727459780,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 3 threshold 0.33201636799686446,110,1,3,0.33201636799686446,0.3178294573643411,0,None,i7186,29,0.01800450112528132 1727459757,1727459792,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 4 threshold 0.21801214660894064,110,1,4,0.21801214660894064,0.2740685171292824,0,None,i7186,32,0.02043566447167347 1727459777,1727459810,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 0.8629342711595637 max_depth 3 threshold 0.2,116,0.8629342711595637,3,0.2,0.26981745436359095,0,None,i7186,29,0.021887824897400817 1727459777,1727459810,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 139 const 0.1 max_depth 4 threshold 0.8,139,0.1,4,0.8,0.29132283070767695,0,None,i7186,30,0.025041974779409136 1727459797,1727459831,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 0.8088990429334861 max_depth 4 threshold 0.2,110,0.8088990429334861,4,0.2,0.3178294573643411,0,None,i7186,30,0.01800450112528132 1727459797,1727459831,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 4 threshold 0.41163944091439364,110,1,4,0.41163944091439364,0.27431857964491124,0,None,i7186,30,0.02042177210969409 1727459817,1727459849,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 3 threshold 0.30744845417848476,110,1,3,0.30744845417848476,0.3178294573643411,0,None,i7186,29,0.01800450112528132 1727459837,1727459871,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.1 max_depth 3 threshold 0.8,136,0.1,3,0.8,0.2755688922230558,0,None,i7186,30,0.026167256099739217 1727459837,1727459878,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.9097548521662918 max_depth 4 threshold 0.8,140,0.9097548521662918,4,0.8,0.2960740185046261,0,None,i7186,37,0.024702604222484194 1727459857,1727459892,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 1 max_depth 4 threshold 0.2,110,1,4,0.2,0.2740685171292824,0,None,i7186,31,0.02043566447167347 1727459869,1727459901,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.3097618486628232 max_depth 3 threshold 0.8,112,0.3097618486628232,3,0.8,0.26956739184796197,0,None,i7186,29,0.0219025344571437 1727459877,1727459911,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.4668434979590198 max_depth 4 threshold 0.8,140,0.4668434979590198,4,0.8,0.3365841460365091,0,None,i7186,30,0.021809023684492553 1727460170,1727460206,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.39412560221063186,113,1,4,0.39412560221063186,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727460197,1727460231,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.26127478051494596 max_depth 3 threshold 0.4146635855428539,149,0.26127478051494596,3,0.4146635855428539,0.2943235808952238,0,None,i7186,30,0.026737453594167772 1727460197,1727460238,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.8129537840265794 max_depth 4 threshold 0.2729888582398567,125,0.8129537840265794,4,0.2729888582398567,0.30457614403600897,0,None,i7186,37,0.022488955572226393 1727460218,1727460251,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.40012360530503566,113,1,4,0.40012360530503566,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727460218,1727460252,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.1 max_depth 3 threshold 0.49699984995215496,136,0.1,3,0.49699984995215496,0.2755688922230558,0,None,i7186,30,0.026167256099739217 1727460231,1727460263,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.3917349761954797 max_depth 3 threshold 0.6517844793767895,132,0.3917349761954797,3,0.6517844793767895,0.28307076769192296,0,None,i7186,29,0.023922647328498792 1727460258,1727460293,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 199 const 0.6469859553647098 max_depth 4 threshold 0.2,199,0.6469859553647098,4,0.2,0.3275818954738685,0,None,i7186,32,0.03143285821455363 1727460258,1727460296,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 139 const 0.7352075358893324 max_depth 3 threshold 0.27423782437559663,139,0.7352075358893324,3,0.27423782437559663,0.28782195548887224,0,None,i7186,34,0.025292037295038042 1727460278,1727460308,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727460291,1727460323,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.2606556543328022 max_depth 3 threshold 0.27616175787175734,125,0.2606556543328022,3,0.27616175787175734,0.3215803950987747,0,None,i7186,29,0.020020630157539385 1727460298,1727460331,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.9132429276220387 max_depth 2 threshold 0.38457417369837943,136,0.9132429276220387,2,0.38457417369837943,0.27956989247311825,0,None,i7186,30,0.025881470367591898 1727460322,1727460355,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.1 max_depth 3 threshold 0.47511738489387667,129,0.1,3,0.47511738489387667,0.2820705176294074,0,None,i7186,30,0.02398933066599983 1727460338,1727460370,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 1 max_depth 2 threshold 0.4392963805802607,131,1,2,0.4392963805802607,0.27706926731682924,0,None,i7186,29,0.02432274735350504 1727460351,1727460383,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.1 max_depth 2 threshold 0.6360802616390376,126,0.1,2,0.6360802616390376,0.2943235808952238,0,None,i7186,28,0.02317245978161207 1727460714,1727460748,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.3219242256534223 max_depth 3 threshold 0.6185180308497169,128,0.3219242256534223,3,0.6185180308497169,0.2825706426606651,0,None,i7186,30,0.023955988997249315 1727460738,1727460771,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.1797247876860787 max_depth 2 threshold 0.8,126,0.1797247876860787,2,0.8,0.2943235808952238,0,None,i7186,29,0.02317245978161207 1727460738,1727460771,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.1 max_depth 3 threshold 0.5896964174725137,126,0.1,3,0.5896964174725137,0.2943235808952238,0,None,i7186,29,0.02317245978161207 1727460744,1727460778,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.2921120999873617 max_depth 3 threshold 0.4977946817187222,149,0.2921120999873617,3,0.4977946817187222,0.2943235808952238,0,None,i7186,30,0.026737453594167772 1727460758,1727460794,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 1 max_depth 4 threshold 0.3059442670564775,119,1,4,0.3059442670564775,0.26781695423855967,0,None,i7186,32,0.023380845211302823 1727460774,1727460806,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.4620331261744506 max_depth 2 threshold 0.8,123,0.4620331261744506,2,0.8,0.2720680170042511,0,None,i7186,28,0.02311515378844711 1727460778,1727460810,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.5079019381754076 max_depth 2 threshold 0.6275777907510565,130,0.5079019381754076,2,0.6275777907510565,0.30932733183295824,0,None,i7186,29,0.02217220971909644 1727460798,1727460831,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 1 max_depth 2 threshold 0.4365945895179959,133,1,2,0.4365945895179959,0.2880720180045011,0,None,i7186,30,0.023589230640993584 1727460818,1727460850,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 142 const 0.18679792966673237 max_depth 3 threshold 0.7786202270784437,142,0.18679792966673237,3,0.7786202270784437,0.29207301825456367,0,None,i7186,29,0.024988389954631512 1727460834,1727460866,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.1 max_depth 2 threshold 0.6053409825415306,130,0.1,2,0.6053409825415306,0.30932733183295824,0,None,i7186,28,0.02217220971909644 1727460838,1727460870,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.36874535756717086 max_depth 2 threshold 0.6514872588629995,124,0.36874535756717086,2,0.6514872588629995,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727460858,1727460890,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.3673281516246195 max_depth 2 threshold 0.5998726814609325,131,0.3673281516246195,2,0.5998726814609325,0.27706926731682924,0,None,i7186,28,0.02432274735350504 1727460864,1727460896,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.1733980036571114 max_depth 2 threshold 0.6582980090126166,128,0.1733980036571114,2,0.6582980090126166,0.2825706426606651,0,None,i7186,28,0.023955988997249315 1727461256,1727461293,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.6561725956074084 max_depth 3 threshold 0.2,135,0.6561725956074084,3,0.2,0.31807951987996996,0,None,i7186,30,0.02313078269567392 1727461279,1727461312,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 1 max_depth 2 threshold 0.26605384234853174,136,1,2,0.26605384234853174,0.27956989247311825,0,None,i7186,30,0.025881470367591898 1727461279,1727461313,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.7551339700206219 max_depth 3 threshold 0.36921992785808605,147,0.7551339700206219,3,0.36921992785808605,0.29507376844211053,0,None,i7186,30,0.02667974685979187 1727461299,1727461332,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 134 const 1 max_depth 2 threshold 0.3347150102517961,134,1,2,0.3347150102517961,0.2945736434108527,0,None,i7186,30,0.02480977387203944 1727461316,1727461349,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.24102014655207543 max_depth 3 threshold 0.8,124,0.24102014655207543,3,0.8,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727461316,1727461351,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.8889719720086987 max_depth 3 threshold 0.6849806450205638,136,0.8889719720086987,3,0.6849806450205638,0.27956989247311825,0,None,i7186,31,0.025881470367591898 1727461339,1727461373,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 0.7824745691488618 max_depth 2 threshold 0.3023501539746372,133,0.7824745691488618,2,0.3023501539746372,0.28332083020755183,0,None,i7186,30,0.023905976494123533 1727461347,1727461382,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 143 const 1 max_depth 3 threshold 0.43690804112419057,143,1,3,0.43690804112419057,0.29582395598899724,0,None,i7186,32,0.02662204012541597 1727461360,1727461392,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.1 max_depth 3 threshold 0.8,125,0.1,3,0.8,0.3215803950987747,0,None,i7186,28,0.020020630157539385 1727461377,1727461410,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.505889829568764 max_depth 3 threshold 0.6172803306537349,136,0.505889829568764,3,0.6172803306537349,0.2755688922230558,0,None,i7186,30,0.026167256099739217 1727461399,1727461431,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 1 max_depth 2 threshold 0.51791179247565,123,1,2,0.51791179247565,0.2720680170042511,0,None,i7186,29,0.02311515378844711 1727461407,1727461439,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.8050120528942486 max_depth 2 threshold 0.8,131,0.8050120528942486,2,0.8,0.27706926731682924,0,None,i7186,29,0.02432274735350504 1727461419,1727461452,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 1 max_depth 3 threshold 0.37399473115676296,135,1,3,0.37399473115676296,0.28407101775443866,0,None,i7186,30,0.025559961418926157 1727461437,1727461469,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 1 max_depth 2 threshold 0.6312969593395766,124,1,2,0.6312969593395766,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727461800,1727461833,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 1 max_depth 3 threshold 0.8,122,1,3,0.8,0.2763190797699425,0,None,i7186,29,0.022849462365591395 1727461830,1727461862,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.6869375842949917 max_depth 3 threshold 0.6967682656018472,129,0.6869375842949917,3,0.6967682656018472,0.2820705176294074,0,None,i7186,28,0.02398933066599983 1727461831,1727461865,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 1 max_depth 4 threshold 0.40536279547179754,119,1,4,0.40536279547179754,0.2683170792698174,0,None,i7186,31,0.023349587396849215 1727461852,1727461886,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3791409689435261,113,1,4,0.3791409689435261,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727461860,1727461894,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.3830468151594473 max_depth 4 threshold 0.4562888840207152,123,0.3830468151594473,4,0.4562888840207152,0.2720680170042511,0,None,i7186,29,0.02311515378844711 1727461872,1727461907,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 134 const 1 max_depth 2 threshold 0.2,134,1,2,0.2,0.28307076769192296,0,None,i7186,31,0.02563140785196299 1727461891,1727461925,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 153 const 1 max_depth 2 threshold 0.6204714770374717,153,1,2,0.6204714770374717,0.30657664416104025,0,None,i7186,30,0.025794910266028044 1727461912,1727461945,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 1 max_depth 2 threshold 0.45492134997163547,131,1,2,0.45492134997163547,0.27706926731682924,0,None,i7186,29,0.02432274735350504 1727461912,1727461951,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.5293064299979605 max_depth 4 threshold 0.4600625709521627,141,0.5293064299979605,4,0.4600625709521627,0.2945736434108527,0,None,i7186,35,0.02480977387203944 1727461932,1727461966,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 0.2957112137020622 max_depth 4 threshold 0.4618943786690808,111,0.2957112137020622,4,0.4618943786690808,0.27331832958239555,0,None,i7186,30,0.020477341557611627 1727461951,1727461984,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3782661921460285,113,1,4,0.3782661921460285,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727461972,1727462006,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3771719152086913,113,1,4,0.3771719152086913,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727461973,1727462007,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 1 max_depth 3 threshold 0.6166221571306048,131,1,3,0.6166221571306048,0.2748187046761691,0,None,i7186,31,0.024472784862882384 1727461992,1727462027,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3717619378482946,113,1,4,0.3717619378482946,0.2820705176294074,0,None,i7186,31,0.02116705646999985 1727462493,1727462527,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.37053476248024286,113,1,4,0.37053476248024286,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727462513,1727462546,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.6476957189960997 max_depth 4 threshold 0.6199330258158845,127,0.6476957189960997,4,0.6199330258158845,0.2918229557389347,0,None,i7186,30,0.023339168125364677 1727462513,1727462546,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.7626252469100835 max_depth 4 threshold 0.5736665270431496,119,0.7626252469100835,4,0.5736665270431496,0.28307076769192296,0,None,i7186,30,0.022427481870467617 1727462524,1727462557,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.36674873689426707,113,1,4,0.36674873689426707,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727462553,1727462585,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.9209117402063987 max_depth 3 threshold 0.5893968977932635,124,0.9209117402063987,3,0.5893968977932635,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727462553,1727462586,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 1 max_depth 2 threshold 0.47620018687846716,130,1,2,0.47620018687846716,0.30932733183295824,0,None,i7186,30,0.02217220971909644 1727462573,1727462605,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.49111336928458205 max_depth 2 threshold 0.26635985745539104,121,0.49111336928458205,2,0.26635985745539104,0.2658164541135284,0,None,i7186,28,0.023505876469117278 1727462585,1727462618,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3728397745330175,113,1,4,0.3728397745330175,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727462613,1727462645,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 1 max_depth 2 threshold 0.8,119,1,2,0.8,0.28307076769192296,0,None,i7186,28,0.022427481870467617 1727462613,1727462649,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.6484879571309454 max_depth 3 threshold 0.3760069820450669,140,0.6484879571309454,3,0.3760069820450669,0.2848212053013254,0,None,i7186,32,0.025506376594148533 1727462633,1727462669,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.37209775816289026,114,1,4,0.37209775816289026,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727462673,1727462708,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 1 max_depth 3 threshold 0.5970736468658998,128,1,3,0.5970736468658998,0.2825706426606651,0,None,i7186,31,0.023955988997249315 1727463054,1727463087,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.8270267919765264 max_depth 2 threshold 0.49443553743072066,135,0.8270267919765264,2,0.49443553743072066,0.2998249562390598,0,None,i7186,29,0.024434680098596073 1727463068,1727463102,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.7453914130982002 max_depth 4 threshold 0.32922004997104337,121,0.7453914130982002,4,0.32922004997104337,0.2685671417854464,0,None,i7186,31,0.023333958489622404 1727463094,1727463126,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 1 max_depth 2 threshold 0.2,125,1,2,0.2,0.3215803950987747,0,None,i7186,29,0.020020630157539385 1727463094,1727463127,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3558100061491572,113,1,4,0.3558100061491572,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727463114,1727463147,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 1 max_depth 3 threshold 0.544117701807304,132,1,3,0.544117701807304,0.2773193298324581,0,None,i7186,29,0.024306076519129784 1727463128,1727463160,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.5989289609940941 max_depth 2 threshold 0.5804302744570443,144,0.5989289609940941,2,0.5804302744570443,0.2998249562390598,0,None,i7186,29,0.026314270875411157 1727463154,1727463186,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.7765503436276086 max_depth 2 threshold 0.39442286776233626,126,0.7765503436276086,2,0.39442286776233626,0.2943235808952238,0,None,i7186,29,0.02317245978161207 1727463158,1727463191,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.35137745779135027,113,1,4,0.35137745779135027,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727463174,1727463206,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 0.6405705970451416 max_depth 3 threshold 0.6307316724185944,133,0.6405705970451416,3,0.6307316724185944,0.2860715178794698,0,None,i7186,29,0.02372259731599567 1727463189,1727463221,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.6774831192443758 max_depth 2 threshold 0.2,131,0.6774831192443758,2,0.2,0.27706926731682924,0,None,i7186,29,0.02432274735350504 1727463214,1727463249,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 4 threshold 0.5250926785818935,121,1,4,0.5250926785818935,0.2685671417854464,0,None,i7186,32,0.023333958489622404 1727463219,1727463255,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 248 const 0.6657177958802649 max_depth 3 threshold 0.35274317019950846,248,0.6657177958802649,3,0.35274317019950846,0.34333583395848966,0,None,i7186,33,0.0373218304576144 1727463234,1727463265,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.6016005109898125 max_depth 2 threshold 0.4399652519761331,127,0.6016005109898125,2,0.4399652519761331,0.2918229557389347,0,None,i7186,28,0.023339168125364677 1727463611,1727463644,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.9049103230585522 max_depth 4 threshold 0.3385831037919862,107,0.9049103230585522,4,0.3385831037919862,0.26706676669167295,0,None,i7186,29,0.020824650607096217 1727463634,1727463666,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.30732683170792696,0,None,i7186,28,0.016729182295573894 1727463634,1727463670,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 157 const 0.5660767962140432 max_depth 4 threshold 0.3924838988842187,157,0.5660767962140432,4,0.3924838988842187,0.32083020755188796,0,None,i7186,32,0.026756689172293072 1727463654,1727463693,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3469389866382156,114,1,4,0.3469389866382156,0.2848212053013254,0,None,i7186,35,0.021005251312828203 1727463671,1727463704,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 102 const 1 max_depth 4 threshold 0.7976500146366812,102,1,4,0.7976500146366812,0.2763190797699425,0,None,i7186,29,0.019241652518392754 1727463674,1727463707,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.7725212240328209 max_depth 3 threshold 0.5261381427276332,127,0.7725212240328209,3,0.5261381427276332,0.29207301825456367,0,None,i7186,29,0.02332249729098941 1727463694,1727463727,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.765452887083385 max_depth 2 threshold 0.2,140,0.765452887083385,2,0.2,0.2860715178794698,0,None,i7186,29,0.0254170685528525 1727463714,1727463749,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 1 max_depth 4 threshold 0.4855069430960342,120,1,4,0.4855069430960342,0.2628157039259815,0,None,i7186,32,0.023693423355838957 1727463732,1727463768,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.35227277873781043,114,1,4,0.35227277873781043,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727463754,1727463788,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.1 max_depth 4 threshold 0.8,117,0.1,4,0.8,0.2755688922230558,0,None,i7186,30,0.02154950502331465 1727463762,1727463794,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.4788629531491102 max_depth 3 threshold 0.5028213089965687,129,0.4788629531491102,3,0.5028213089965687,0.2820705176294074,0,None,i7186,28,0.02398933066599983 1727463774,1727463807,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.34653589922252886,113,1,4,0.34653589922252886,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727464214,1727464250,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 1 max_depth 4 threshold 0.43049858644182304,118,1,4,0.43049858644182304,0.28507126781695424,0,None,i7186,32,0.022302450612653162 1727464235,1727464267,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.716158284940704 max_depth 2 threshold 0.6060576430343338,125,0.716158284940704,2,0.6060576430343338,0.3215803950987747,0,None,i7186,29,0.020020630157539385 1727464235,1727464274,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 331 const 0.2094024458900094 max_depth 4 threshold 0.5604471530765296,331,0.2094024458900094,4,0.5604471530765296,0.33958489622405597,0,None,i7186,35,0.05038759689922481 1727464255,1727464288,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.37161840257846535,113,1,4,0.37161840257846535,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727464275,1727464308,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 139 const 0.3151225307307169 max_depth 4 threshold 0.6350711639324103,139,0.3151225307307169,4,0.6350711639324103,0.29132283070767695,0,None,i7186,30,0.025041974779409136 1727464295,1727464327,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 1 max_depth 2 threshold 0.8,117,1,2,0.8,0.2755688922230558,0,None,i7186,29,0.02154950502331465 1727464305,1727464338,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3740347539582398,113,1,4,0.3740347539582398,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727464315,1727464348,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.36930999087174665,113,1,4,0.36930999087174665,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727464335,1727464369,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.37035127552170016,113,1,4,0.37035127552170016,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727464355,1727464386,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.5549669176468701 max_depth 2 threshold 0.8,120,0.5549669176468701,2,0.8,0.30482620655163795,0,None,i7186,28,0.02106776694173543 1727464375,1727464411,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 1 max_depth 4 threshold 0.44943730816019256,118,1,4,0.44943730816019256,0.28507126781695424,0,None,i7186,32,0.022302450612653162 1727464395,1727464428,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.37684765768493944,113,1,4,0.37684765768493944,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727464936,1727464973,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.34464476287019863,114,1,4,0.34464476287019863,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727464956,1727464994,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 162 const 0.507606027169142 max_depth 4 threshold 0.4040423537857172,162,0.507606027169142,4,0.4040423537857172,0.30407601900475123,0,None,i7186,35,0.028152871551221134 1727464971,1727465004,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3605017903947092,113,1,4,0.3605017903947092,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727464996,1727465029,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.36322848977421107,113,1,4,0.36322848977421107,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727465001,1727465037,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3612257295613339,114,1,4,0.3612257295613339,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727465031,1727465064,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 0.8447828905438486 max_depth 3 threshold 0.5813755120152078,115,0.8447828905438486,3,0.5813755120152078,0.2793198299574894,0,None,i7186,29,0.021328861627171496 1727465036,1727465069,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.23447612348946537 max_depth 4 threshold 0.6007451098959569,117,0.23447612348946537,4,0.6007451098959569,0.2755688922230558,0,None,i7186,30,0.02154950502331465 1727465057,1727465093,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3573698460076109,114,1,4,0.3573698460076109,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727465076,1727465112,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 223 const 1 max_depth 3 threshold 0.2,223,1,3,0.2,0.3323330832708177,0,None,i7186,33,0.038697174293573396 1727465092,1727465126,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 1 max_depth 4 threshold 0.7859921223810784,119,1,4,0.7859921223810784,0.2683170792698174,0,None,i7186,30,0.023349587396849215 1727465116,1727465152,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3686638301367816,114,1,4,0.3686638301367816,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727465122,1727465159,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3581430443869281,114,1,4,0.3581430443869281,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727465152,1727465184,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.9994123303516724 max_depth 4 threshold 0.7962596078178243,102,0.9994123303516724,4,0.7962596078178243,0.2763190797699425,0,None,i7186,28,0.019241652518392754 1727465575,1727465608,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6921872890419712 max_depth 4 threshold 0.745294659097977,100,0.6921872890419712,4,0.745294659097977,0.30732683170792696,0,None,i7186,29,0.016729182295573894 1727465598,1727465628,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.40167654635368977 max_depth 2 threshold 0.8,100,0.40167654635368977,2,0.8,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727465598,1727465634,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3853934387972868,114,1,4,0.3853934387972868,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727465618,1727465651,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 1 max_depth 4 threshold 0.5958404126605135,117,1,4,0.5958404126605135,0.2755688922230558,0,None,i7186,30,0.02154950502331465 1727465636,1727465672,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3635367540740631,114,1,4,0.3635367540740631,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727465658,1727465694,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 223 const 0.8170075652312077 max_depth 2 threshold 0.546458496057858,223,0.8170075652312077,2,0.546458496057858,0.3323330832708177,0,None,i7186,33,0.038697174293573396 1727465666,1727465707,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 486 const 0.5652072753321087 max_depth 2 threshold 0.3488498193791215,486,0.5652072753321087,2,0.3488498193791215,0.3760940235058765,0,None,i7186,38,0.06645411352838208 1727465696,1727465728,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.6815444645933175 max_depth 3 threshold 0.8,119,0.6815444645933175,3,0.8,0.28307076769192296,0,None,i7186,28,0.022427481870467617 1727465718,1727465751,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.3399799503727998 max_depth 3 threshold 0.37796247255634513,131,0.3399799503727998,3,0.37796247255634513,0.27706926731682924,0,None,i7186,30,0.02432274735350504 1727465726,1727465759,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9006880727097708 max_depth 3 threshold 0.8,100,0.9006880727097708,3,0.8,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727465738,1727465770,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 0.1 max_depth 4 threshold 0.8,106,0.1,4,0.8,0.2583145786446611,0,None,i7186,29,0.02131088327637465 1727465758,1727465791,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 0.8476719206982075 max_depth 3 threshold 0.5785553770071536,115,0.8476719206982075,3,0.5785553770071536,0.2793198299574894,0,None,i7186,30,0.021328861627171496 1727466539,1727466574,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.6024718877580317 max_depth 4 threshold 0.8,120,0.6024718877580317,4,0.8,0.30482620655163795,0,None,i7186,30,0.02106776694173543 1727466559,1727466591,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 134 const 0.1 max_depth 2 threshold 0.4073076492952016,134,0.1,2,0.4073076492952016,0.2945736434108527,0,None,i7186,29,0.02480977387203944 1727466572,1727466603,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.7412058693798185 max_depth 3 threshold 0.7231915715666812,120,0.7412058693798185,3,0.7231915715666812,0.30482620655163795,0,None,i7186,28,0.02106776694173543 1727466599,1727466632,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.7453618850443079 max_depth 2 threshold 0.2768734248778701,132,0.7453618850443079,2,0.2768734248778701,0.28307076769192296,0,None,i7186,29,0.023922647328498792 1727466619,1727466655,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3673327390311382,114,1,4,0.3673327390311382,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727466632,1727466664,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.3098814092979404 max_depth 3 threshold 0.5829578501264552,122,0.3098814092979404,3,0.5829578501264552,0.2763190797699425,0,None,i7186,28,0.022849462365591395 1727466662,1727466695,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.23351676903634958 max_depth 4 threshold 0.4339047672231684,124,0.23351676903634958,4,0.4339047672231684,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727466679,1727466711,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.2734469074891937 max_depth 4 threshold 0.44721680353195303,123,0.2734469074891937,4,0.44721680353195303,0.2720680170042511,0,None,i7186,29,0.02311515378844711 1727466699,1727466731,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.881905539666245 max_depth 3 threshold 0.23144493746599243,119,0.881905539666245,3,0.23144493746599243,0.27881970492623154,0,None,i7186,29,0.02269317329332333 1727466719,1727466751,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.3229959365489531 max_depth 3 threshold 0.6177315553122605,128,0.3229959365489531,3,0.6177315553122605,0.2825706426606651,0,None,i7186,29,0.023955988997249315 1727466677,1727466769,92,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.7881951858858048 max_depth 2 threshold 0.7927820776782109,119,0.7881951858858048,2,0.7927820776782109,0.28307076769192296,0,None,i7181,25,0.022427481870467617 1727466739,1727466770,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 1 max_depth 2 threshold 0.8,124,1,2,0.8,0.28157039259814953,0,None,i7186,28,0.022521255313828457 1727466759,1727466794,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 212 const 0.4745080740728541 max_depth 3 threshold 0.2,212,0.4745080740728541,3,0.2,0.3015753938484621,0,None,i7186,31,0.03781500930788253 1727467280,1727467313,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.47377538961876975 max_depth 4 threshold 0.49759264185696656,120,0.47377538961876975,4,0.49759264185696656,0.30482620655163795,0,None,i7186,29,0.02106776694173543 1727467320,1727467356,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.1 max_depth 4 threshold 0.49829482257629026,117,0.1,4,0.49829482257629026,0.2755688922230558,0,None,i7186,32,0.02154950502331465 1727467320,1727467356,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 246 const 0.7639902119952444 max_depth 3 threshold 0.46479022986251445,246,0.7639902119952444,3,0.46479022986251445,0.3330832708177044,0,None,i7186,33,0.038603400850212556 1727467340,1727467372,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.1 max_depth 4 threshold 0.642735395803077,119,0.1,4,0.642735395803077,0.28307076769192296,0,None,i7186,29,0.022427481870467617 1727467357,1727467387,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.7320322978960958,100,1,2,0.7320322978960958,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727467380,1727467413,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.24951152533627174 max_depth 3 threshold 0.5157699464509976,122,0.24951152533627174,3,0.5157699464509976,0.2763190797699425,0,None,i7186,29,0.022849462365591395 1727467400,1727467432,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.3451753553261928 max_depth 3 threshold 0.5114527620644937,121,0.3451753553261928,3,0.5114527620644937,0.2658164541135284,0,None,i7186,28,0.023505876469117278 1727467417,1727467447,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.30994054667850457 max_depth 3 threshold 0.5830566216685,122,0.30994054667850457,3,0.5830566216685,0.2763190797699425,0,None,i7181,27,0.022849462365591395 1727467440,1727467473,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.9006479557360502 max_depth 2 threshold 0.533016918716323,131,0.9006479557360502,2,0.533016918716323,0.27706926731682924,0,None,i7186,29,0.02432274735350504 1727467447,1727467483,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 252 const 1 max_depth 2 threshold 0.35006263533071913,252,1,2,0.35006263533071913,0.35058764691172795,0,None,i7186,32,0.04161754724395384 1727467477,1727467510,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 0.1142227945061724 max_depth 4 threshold 0.8,111,0.1142227945061724,4,0.8,0.27331832958239555,0,None,i7186,29,0.020477341557611627 1727467500,1727467536,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.37642010098262,114,1,4,0.37642010098262,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727468216,1727468251,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 3 threshold 0.39386632550041145,121,1,3,0.39386632550041145,0.2650662665666417,0,None,i7186,30,0.023552763190797698 1727468221,1727468253,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.26933067230286767 max_depth 3 threshold 0.4791197172069208,121,0.26933067230286767,3,0.4791197172069208,0.2658164541135284,0,None,i7186,28,0.023505876469117278 1727468232,1727468264,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.3607753361420174 max_depth 3 threshold 0.49428416483969034,122,0.3607753361420174,3,0.49428416483969034,0.2763190797699425,0,None,i7186,28,0.022849462365591395 1727468261,1727468295,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.6188711151830784 max_depth 4 threshold 0.4332190560236656,121,0.6188711151830784,4,0.4332190560236656,0.2628157039259815,0,None,i7186,31,0.023693423355838957 1727468261,1727468298,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.7973426546912361 max_depth 3 threshold 0.2891022048462153,137,0.7973426546912361,3,0.2891022048462153,0.29932483120780196,0,None,i7186,33,0.024470403315114492 1727468292,1727468325,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.46093373973190244 max_depth 3 threshold 0.41908716302392857,136,0.46093373973190244,3,0.41908716302392857,0.2755688922230558,0,None,i7186,29,0.026167256099739217 1727468321,1727468354,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.1 max_depth 4 threshold 0.7651059182520035,112,0.1,4,0.7651059182520035,0.26956739184796197,0,None,i7186,29,0.0219025344571437 1727468341,1727468373,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.8296617188660939 max_depth 2 threshold 0.5217596945458067,127,0.8296617188660939,2,0.5217596945458067,0.2918229557389347,0,None,i7186,28,0.023339168125364677 1727468341,1727468378,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 157 const 1 max_depth 4 threshold 0.4700938437590986,157,1,4,0.4700938437590986,0.31732933233308325,0,None,i7186,33,0.02704842877386013 1727468361,1727468393,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 0.1 max_depth 3 threshold 0.6532249434277577,115,0.1,3,0.6532249434277577,0.3063265816454114,0,None,i7186,28,0.019740229174940793 1727468381,1727468413,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.1 max_depth 4 threshold 0.5143177617193668,124,0.1,4,0.5143177617193668,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727468413,1727468446,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.5976822270220876 max_depth 3 threshold 0.44325339488889615,122,0.5976822270220876,3,0.44325339488889615,0.2763190797699425,0,None,i7186,29,0.022849462365591395 1727468441,1727468474,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 133 const 1 max_depth 3 threshold 0.8,133,1,3,0.8,0.28032008002000497,0,None,i7186,29,0.024106026506626656 1727469138,1727469171,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.1 max_depth 4 threshold 0.7816562021650171,113,0.1,4,0.7816562021650171,0.28332083020755183,0,None,i7186,29,0.021093508671285472 1727469165,1727469197,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.2673631835586684 max_depth 3 threshold 0.2,120,0.2673631835586684,3,0.2,0.30482620655163795,0,None,i7186,28,0.02106776694173543 1727469185,1727469218,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.1 max_depth 4 threshold 0.2,113,0.1,4,0.2,0.28332083020755183,0,None,i7186,29,0.021093508671285472 1727469198,1727469230,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.1 max_depth 4 threshold 0.6840375838464883,114,0.1,4,0.6840375838464883,0.27706926731682924,0,None,i7186,28,0.021461247664857387 1727469225,1727469259,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.3688749731094991 max_depth 4 threshold 0.5148906630174257,119,0.3688749731094991,4,0.5148906630174257,0.28307076769192296,0,None,i7186,30,0.022427481870467617 1727469245,1727469277,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.1 max_depth 4 threshold 0.7932838520383643,114,0.1,4,0.7932838520383643,0.27706926731682924,0,None,i7186,28,0.021461247664857387 1727469259,1727469292,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.1 max_depth 4 threshold 0.4168340785720201,126,0.1,4,0.4168340785720201,0.2943235808952238,0,None,i7186,29,0.02317245978161207 1727469285,1727469318,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.4127136816063718 max_depth 4 threshold 0.6454097037434091,119,0.4127136816063718,4,0.6454097037434091,0.28307076769192296,0,None,i7186,29,0.022427481870467617 1727469305,1727469336,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 2 threshold 0.8,114,1,2,0.8,0.27706926731682924,0,None,i7186,27,0.021461247664857387 1727469319,1727469351,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 0.5968283598932462 max_depth 3 threshold 0.32500664560117415,110,0.5968283598932462,3,0.32500664560117415,0.3178294573643411,0,None,i7186,28,0.01800450112528132 1727469345,1727469377,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 2 threshold 0.8,108,1,2,0.8,0.27981995498874723,0,None,i7186,28,0.020116140146147644 1727469365,1727469397,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 117 const 0.1 max_depth 4 threshold 0.5983480676642453,117,0.1,4,0.5983480676642453,0.2755688922230558,0,None,i7186,28,0.02154950502331465 1727469379,1727469416,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3626308407395965,114,1,4,0.3626308407395965,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727470395,1727470432,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 126 const 1 max_depth 4 threshold 0.5353867053735221,126,1,4,0.5353867053735221,0.2865716429107277,0,None,i7186,33,0.023689255647245146 1727470415,1727470446,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.674980728529785 max_depth 3 threshold 0.7602422066167169,124,0.674980728529785,3,0.7602422066167169,0.28157039259814953,0,None,i7186,28,0.022521255313828457 1727470435,1727470467,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.1 max_depth 4 threshold 0.6820693106461213,113,0.1,4,0.6820693106461213,0.28332083020755183,0,None,i7186,28,0.021093508671285472 1727470455,1727470487,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.1 max_depth 4 threshold 0.7997089942083846,112,0.1,4,0.7997089942083846,0.26956739184796197,0,None,i7186,29,0.0219025344571437 1727470475,1727470507,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 1 max_depth 3 threshold 0.5661210640415417,122,1,3,0.5661210640415417,0.2763190797699425,0,None,i7186,29,0.022849462365591395 1727470495,1727470527,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.40125854522342574 max_depth 3 threshold 0.46350758654326646,127,0.40125854522342574,3,0.46350758654326646,0.2918229557389347,0,None,i7186,29,0.023339168125364677 1727470515,1727470551,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.34461222690136206,114,1,4,0.34461222690136206,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727470535,1727470567,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.5480639567390787 max_depth 2 threshold 0.607525843639823,131,0.5480639567390787,2,0.607525843639823,0.27706926731682924,0,None,i7186,28,0.02432274735350504 1727470554,1727470584,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 134 const 0.435162907096169 max_depth 2 threshold 0.6913319586955895,134,0.435162907096169,2,0.6913319586955895,0.2945736434108527,0,None,i7181,26,0.02480977387203944 1727470575,1727470607,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.10306622364382118 max_depth 3 threshold 0.5767699251071462,125,0.10306622364382118,3,0.5767699251071462,0.3215803950987747,0,None,i7186,28,0.020020630157539385 1727470618,1727470652,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 198 const 0.1 max_depth 2 threshold 0.2,198,0.1,2,0.2,0.3305826456614154,0,None,i7186,31,0.031132783195798947 1727470615,1727470652,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.34781435932413085,114,1,4,0.34781435932413085,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727470648,1727470685,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.5181586655089632 max_depth 4 threshold 0.3533605344578291,143,0.5181586655089632,4,0.3533605344578291,0.30207551887971995,0,None,i7186,33,0.026141150672283453 1727471406,1727471445,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.8356489192298328 max_depth 4 threshold 0.4396287293009199,122,0.8356489192298328,4,0.4396287293009199,0.2763190797699425,0,None,i7186,30,0.022849462365591395 1727471434,1727471466,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.3488407375960481,113,1,4,0.3488407375960481,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727471464,1727471497,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 130 const 1 max_depth 2 threshold 0.4210971173612048,130,1,2,0.4210971173612048,0.30932733183295824,0,None,i7186,29,0.02217220971909644 1727471486,1727471519,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.33765022989337556,113,1,4,0.33765022989337556,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727471494,1727471527,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.1 max_depth 4 threshold 0.8,112,0.1,4,0.8,0.26956739184796197,0,None,i7186,30,0.0219025344571437 1727471525,1727471556,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 1 max_depth 2 threshold 0.36921718392925573,124,1,2,0.36921718392925573,0.28157039259814953,0,None,i7186,28,0.022521255313828457 1727471546,1727471578,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 0.1 max_depth 4 threshold 0.8,113,0.1,4,0.8,0.28332083020755183,0,None,i7186,28,0.021093508671285472 1727471566,1727471599,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 0.1 max_depth 4 threshold 0.8,111,0.1,4,0.8,0.27331832958239555,0,None,i7186,29,0.020477341557611627 1727471585,1727471617,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.35129837385024554,113,1,4,0.35129837385024554,0.2820705176294074,0,None,i7186,29,0.02116705646999985 1727471606,1727471640,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.34677019154297495,113,1,4,0.34677019154297495,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727471627,1727471660,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 175 const 0.19586502635815528 max_depth 2 threshold 0.2943284350736619,175,0.19586502635815528,2,0.2943284350736619,0.3448362090522631,0,None,i7186,30,0.02700675168792198 1727471645,1727471678,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 1 max_depth 3 threshold 0.6272033504214998,122,1,3,0.6272033504214998,0.2763190797699425,0,None,i7186,28,0.022849462365591395 1727472581,1727472614,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 112 const 0.1 max_depth 4 threshold 0.8,112,0.1,4,0.8,0.26956739184796197,0,None,i7186,29,0.0219025344571437 1727472601,1727472637,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.36379296648635345,114,1,4,0.36379296648635345,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727472621,1727472654,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 125 const 1 max_depth 2 threshold 0.8,125,1,2,0.8,0.3215803950987747,0,None,i7186,29,0.020020630157539385 1727472641,1727472671,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 1 max_depth 2 threshold 0.4540755642956864,128,1,2,0.4540755642956864,0.2825706426606651,0,None,i7181,25,0.023955988997249315 1727472672,1727472703,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.10511255733128576 max_depth 2 threshold 0.2,100,0.10511255733128576,2,0.2,0.30732683170792696,0,None,i7186,27,0.016729182295573894 1727472701,1727472733,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 0.1 max_depth 4 threshold 0.8,111,0.1,4,0.8,0.27331832958239555,0,None,i7186,28,0.020477341557611627 1727472721,1727472755,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.32819217102113063,113,1,4,0.32819217102113063,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727472733,1727472765,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 143 const 1 max_depth 2 threshold 0.3600265725366647,143,1,2,0.3600265725366647,0.29932483120780196,0,None,i7186,29,0.026352742031661762 1727472761,1727472795,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.310133536304271 max_depth 3 threshold 0.8,122,0.310133536304271,3,0.8,0.2763190797699425,0,None,i7186,29,0.022849462365591395 1727472781,1727472813,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 115 const 0.2451135475235047 max_depth 4 threshold 0.8,115,0.2451135475235047,4,0.8,0.3063265816454114,0,None,i7186,28,0.019740229174940793 1727472802,1727472835,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 113 const 1 max_depth 4 threshold 0.35058392393461624,113,1,4,0.35058392393461624,0.2820705176294074,0,None,i7186,30,0.02116705646999985 1727472822,1727472864,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 593 const 0.8113096325700552 max_depth 2 threshold 0.5022888052213543,593,0.8113096325700552,2,0.5022888052213543,0.3875968992248062,0,None,i7186,38,0.08477119279819954 1727472842,1727472874,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.1950193091953968 max_depth 3 threshold 0.694549642324798,119,0.1950193091953968,3,0.694549642324798,0.28307076769192296,0,None,i7186,29,0.022427481870467617 1727473971,1727474004,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 0.1 max_depth 4 threshold 0.8,111,0.1,4,0.8,0.27331832958239555,0,None,i7186,29,0.020477341557611627 1727474001,1727474034,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 124 const 1 max_depth 4 threshold 0.8,124,1,4,0.8,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727474028,1727474063,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 1 max_depth 4 threshold 0.32079377949500587,120,1,4,0.32079377949500587,0.2628157039259815,0,None,i7186,31,0.023693423355838957 1727474048,1727474080,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 110 const 0.1 max_depth 4 threshold 0.8,110,0.1,4,0.8,0.3178294573643411,0,None,i7186,28,0.01800450112528132 1727474062,1727474095,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 118 const 1 max_depth 4 threshold 0.8,118,1,4,0.8,0.27606901725431354,0,None,i7186,30,0.022865091272818206 1727474088,1727474122,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.5824529147009596 max_depth 2 threshold 0.2070111624297915,143,0.5824529147009596,2,0.2070111624297915,0.29932483120780196,0,None,i7186,29,0.026352742031661762 1727474108,1727474141,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.8952677301741229 max_depth 3 threshold 0.717456889319511,137,0.8952677301741229,3,0.717456889319511,0.2758189547386847,0,None,i7186,29,0.026149394491480012 1727474128,1727474162,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.6838265752391205 max_depth 4 threshold 0.5233607322465962,123,0.6838265752391205,4,0.5233607322465962,0.2720680170042511,0,None,i7186,30,0.02311515378844711 1727474148,1727474181,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 145 const 0.42635444617551044 max_depth 2 threshold 0.5757352878587123,145,0.42635444617551044,2,0.5757352878587123,0.3240810202550638,0,None,i7186,29,0.024448419797257002 1727474183,1727474218,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 4 threshold 0.35201957009408125,121,1,4,0.35201957009408125,0.2658164541135284,0,None,i7186,32,0.023505876469117278 1727474208,1727474241,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.549356629389724 max_depth 4 threshold 0.42820640090314094,119,0.549356629389724,4,0.42820640090314094,0.28307076769192296,0,None,i7186,29,0.022427481870467617 1727474229,1727474269,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 306 const 0.6260114146691697 max_depth 3 threshold 0.3703757194922773,306,0.6260114146691697,3,0.3703757194922773,0.35608902225556394,0,None,i7186,36,0.04763690922730682 1727474243,1727474277,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 1 max_depth 4 threshold 0.8,116,1,4,0.8,0.26706676669167295,0,None,i7186,30,0.022049630054572465 1727475131,1727475168,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.8537895419801786 max_depth 4 threshold 0.5008965476056498,120,0.8537895419801786,4,0.5008965476056498,0.26006501625406353,0,None,i7186,30,0.023865341335333832 1727475171,1727475203,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 111 const 0.1 max_depth 4 threshold 0.7838753276724022,111,0.1,4,0.7838753276724022,0.27331832958239555,0,None,i7186,28,0.020477341557611627 1727475180,1727475217,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.36297761296055453,114,1,4,0.36297761296055453,0.2848212053013254,0,None,i7186,33,0.021005251312828203 1727475210,1727475246,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.3518008583288636,114,1,4,0.3518008583288636,0.2848212053013254,0,None,i7186,32,0.021005251312828203 1727475232,1727475267,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 4 threshold 0.5349793664671243,121,1,4,0.5349793664671243,0.2685671417854464,0,None,i7186,31,0.023333958489622404 1727475253,1727475284,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 0.1 max_depth 4 threshold 0.6686762815842067,114,0.1,4,0.6686762815842067,0.27706926731682924,0,None,i7186,28,0.021461247664857387 1727475271,1727475302,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.7110459094783066 max_depth 3 threshold 0.4400064683837059,121,0.7110459094783066,3,0.4400064683837059,0.2658164541135284,0,None,i7186,28,0.023505876469117278 1727475292,1727475327,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 186 const 0.591177975322018 max_depth 4 threshold 0.4002130507074253,186,0.591177975322018,4,0.4002130507074253,0.30932733183295824,0,None,i7186,32,0.03325831457864466 1727475312,1727475344,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 121 const 1 max_depth 3 threshold 0.7136978922521953,121,1,3,0.7136978922521953,0.2685671417854464,0,None,i7186,28,0.023333958489622404 1727475352,1727475387,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 116 const 0.895266105785531 max_depth 4 threshold 0.6231912293948032,116,0.895266105785531,4,0.6231912293948032,0.26981745436359095,0,None,i7186,31,0.021887824897400817 1727475361,1727475393,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.6500028083519689 max_depth 3 threshold 0.3033002218352092,128,0.6500028083519689,3,0.3033002218352092,0.2825706426606651,0,None,i7186,28,0.023955988997249315 1727475391,1727475425,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 114 const 1 max_depth 4 threshold 0.34033013770780396,114,1,4,0.34033013770780396,0.2848212053013254,0,None,i7181,29,0.021005251312828203 1727475412,1727475449,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 247 const 0.43978021004663503 max_depth 2 threshold 0.30435186609885345,247,0.43978021004663503,2,0.30435186609885345,0.33708427106776695,0,None,i7186,32,0.038103275818954736 1727476204,1727476238,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 105 const 0.541925894851807 max_depth 4 threshold 0.467258981891562,105,0.541925894851807,4,0.467258981891562,0.32008002000500124,0,None,i7186,30,0.016938445137600188 1727476224,1727476255,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 0.49888002627147954 max_depth 3 threshold 0.5084760010623994,106,0.49888002627147954,3,0.5084760010623994,0.25531382845711426,0,None,i7186,27,0.021477591620127256 1727476244,1727476277,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 104 const 0.9999936023688094 max_depth 4 threshold 0.613847061085562,104,0.9999936023688094,4,0.613847061085562,0.2505626406601651,0,None,i7186,29,0.02059725457680209 1727476267,1727476299,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 105 const 0.9766999807546247 max_depth 2 threshold 0.2,105,0.9766999807546247,2,0.2,0.29682420605151283,0,None,i7186,28,0.019171459531549556 1727476298,1727476331,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 103 const 0.9893506628770109 max_depth 4 threshold 0.35172708817443366,103,0.9893506628770109,4,0.35172708817443366,0.2523130782695674,0,None,i7186,30,0.02050512628157039 1727476324,1727476356,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 105 const 0.654461119449933 max_depth 4 threshold 0.2,105,0.654461119449933,4,0.2,0.2970742685671418,0,None,i7186,29,0.018149274160645424 1727476344,1727476377,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 106 const 0.6462469108578368 max_depth 4 threshold 0.7980457275302315,106,0.6462469108578368,4,0.7980457275302315,0.2568142035508877,0,None,i7186,30,0.021394237448250954
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1727472845,478.22265625,54.3 1727472845,478.22265625,49.4 1727472845,478.22265625,58.7 1727474239,497.078125,50.3 1727474239,497.078125,55.6 1727474239,497.078125,49.0 1727474239,497.078125,58.1 1727475410,526.44921875,50.2 1727475410,526.44921875,56.5 1727475410,526.44921875,50.4 1727475410,526.44921875,40.6 1727476352,533.0078125,50.2 1727476352,533.0078125,51.0 1727476384,533.03125,49.8 1727476384,533.03125,38.2
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.
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:
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
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.
submitit INFO (2024-09-27 18:40:11,087) - Starting with JobEnvironment(job_id=2257950, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-27 18:40:11,089) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/BayesianNonparametricDetectionMethod_OutdoorObjects/0/single_runs/2257950/2257950_submitted.pkl
Parameters: {"n_samples": 804, "const": 0.35519191026687624, "max_depth": 2, "threshold": 0.6886049985885621}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 804 const 0.35519191026687624 max_depth 2 threshold 0.6886049985885621
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate
UID: 2063769
GID: 200270
Status-Change-Time: 1724762786.0
Size: 2165 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727455054.0
Last modification: 1724759185.0
Hostname: i7186
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: main_omniopt.py
UID: 2063769
GID: 200270
Status-Change-Time: 1727277242.0
Size: 1729 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727455054.0
Last modification: 1727277242.0
Hostname: i7186
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 BayesianNonparametricDetectionMethod n_samples 804 const 0.35519191026687624 max_depth 2 threshold 0.6886049985885621
stdout:
RESULT: 0.4246061515378845
OO-Info: runtime: 41
OO-Info: lpd: 0.10865216304076017
Result: 0.4246061515378845
EXIT_CODE: 0
submitit INFO (2024-09-27 18:40:58,651) - Job completed successfully
submitit INFO (2024-09-27 18:40:58,653) - Exiting after successful completion
Job-Infos
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}
body.dark-mode {
background-color: #1e1e1e; color: #fff;
}
.plot-container {
margin-bottom: 2rem;
}
.spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #3498db;
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 2s linear infinite;
margin: auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
.tabs {
margin-bottom: 20px;
}
.tab-content {
display: none;
}
.tab-content.active {
display: block;
}
pre {
color: #00CC00 !important;
background-color: black !important;
font-family: monospace !important;
line-break: anywhere;
}
menu[role="tablist"] {
display: flex;
flex-wrap: wrap;
gap: 4px;
max-width: 100%;
max-height: 100px;
overflow: scroll;
}
menu[role="tablist"] button {
white-space: nowrap;
min-width: 100px;
}
.container {
max-width: 100% !important;
}
.gridjs-sort {
min-width: 1px !important;
}
td.gridjs-td {
overflow: clip;
}
.title-bar-text {
font-size: 22px;
display: block ruby;
}
.title-bar {
height: fit-content;
}
.window {
width: fit-content;
min-width: 100%;
}
.top_link {
display: inline-block;
padding: 5px 5px;
background-color: #007bff; /* Blau, kannst du anpassen */
color: white;
text-decoration: none;
font-size: 16px;
font-weight: bold;
border-radius: 6px;
border: 2px solid #0056b3;
text-align: center;
transition: all 0.3s ease-in-out;
}
.top_link:hover {
background-color: #0056b3;
border-color: #004494;
}
.top_link:active {
background-color: #003366;
border-color: #002244;
}
button {
color: black;
}
.share_folder_buttons {
width: fit-content;
}
button {
background: #fcfcfe;
border-color: #919b9c;
border-top-color: rgb(145, 155, 156);
border-bottom-color: rgb(145, 155, 156);
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c;
}
button {
padding-bottom: 2px;
margin-top: -2px;
background-color: #ece9d8;
position: relative;
z-index: 8;
margin-left: -3px;
margin-bottom: 1px;
}
.window {
min-width: 1100px;
}
.error_text {
color: red;
}
[role="tab"] {
padding: 10px !important;
}
[role="tabpanel"] {
min-width: fit-content;
}
select {
border: 1px solid #7f9db9;
background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 -0.5 15 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e6eefc' d='M0 0h1'/%3E%3Cpath stroke='%23d1e0fd' d='M1 0h1M0 1h1m3 0h2M2 3h1M2 4h1'/%3E%3Cpath stroke='%23cad8f9' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23c4d3f7' d='M3 0h1M0 3h1M0 4h1'/%3E%3Cpath stroke='%23bfd0f8' d='M4 0h2M0 5h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 0h1M0 6h1'/%3E%3Cpath stroke='%23baccf4' d='M7 0h1m6 2h1m-1 5h1m-1 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M8 0h1M0 7h1M0 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M9 0h2M0 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 0h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 0h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 0h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 0h1'/%3E%3Cpath stroke='%23e1eafe' d='M1 1h1'/%3E%3Cpath stroke='%23dae6fe' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23d4e1fc' d='M3 1h1M1 3h1M1 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M6 1h1M1 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M7 1h1M4 2h2'/%3E%3Cpath stroke='%23cad9fd' d='M8 1h1M6 2h1M3 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M9 1h2'/%3E%3Cpath stroke='%23c5d6fc' d='M11 1h1M2 11h4'/%3E%3Cpath stroke='%23c2d3fc' d='M12 1h1m-2 1h1M1 11h1m0 1h2m-2 1h2'/%3E%3Cpath stroke='%23bccefa' d='M13 1h1m-1 1h1m-1 1h1m-1 1h1M3 15h4'/%3E%3Cpath stroke='%23b9c9f3' d='M14 1h1M3 16h4'/%3E%3Cpath stroke='%23d8e3fc' d='M2 2h1'/%3E%3Cpath stroke='%23d1defd' d='M3 2h1'/%3E%3Cpath stroke='%23c9d8fc' d='M7 2h1M4 3h3M4 4h3M3 6h1m1 0h2M1 7h1M1 8h1'/%3E%3Cpath stroke='%23c5d5fc' d='M8 2h1m-8 8h5'/%3E%3Cpath stroke='%23c5d3fc' d='M9 2h2'/%3E%3Cpath stroke='%23bed0fc' d='M12 2h1M8 3h1M8 4h1m-8 8h1m-1 1h1m0 1h1m1 0h3'/%3E%3Cpath stroke='%23cddbfc' d='M3 3h1M3 4h1M1 6h2'/%3E%3Cpath stroke='%23c8d5fb' d='M7 3h1M7 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M9 3h4M9 4h4M8 5h1M7 6h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 3h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23ceddfd' d='M2 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M4 5h4M1 9h3'/%3E%3Cpath stroke='%23bacdfc' d='M9 5h2m1 0h2M1 14h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1M8 6h2m2 0h2m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%234d6185' d='M4 6h1m5 0h1M3 7h3m3 0h3M4 8h3m1 0h3M5 9h5m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1m-1 1h1'/%3E%3Cpath stroke='%23cad8fd' d='M2 7h1M2 8h2'/%3E%3Cpath stroke='%23c1d3fb' d='M6 7h2M7 8h1M4 9h1'/%3E%3Cpath stroke='%23b6cefb' d='M8 7h1m2 1h1m-2 1h3m-2 1h2'/%3E%3Cpath stroke='%23b6cdfb' d='M13 9h1m-6 6h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 9h1'/%3E%3Cpath stroke='%23b4c8f6' d='M0 10h1'/%3E%3Cpath stroke='%23bdd3fb' d='M9 10h2m-4 4h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 10h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 10h1'/%3E%3Cpath stroke='%23b1c7f6' d='M0 11h1'/%3E%3Cpath stroke='%23c3d5fd' d='M6 11h1'/%3E%3Cpath stroke='%23bad4fc' d='M8 11h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M9 11h4m-2 3h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 11h1m-3 4h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 11h1m-7 5h3'/%3E%3Cpath stroke='%23adc3f6' d='M0 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c2d5fc' d='M4 12h4m-4 1h4'/%3E%3Cpath stroke='%23b7d3fc' d='M9 12h2m-2 1h2m-3 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 12h1m-1 1h1'/%3E%3Cpath stroke='%23afcdfb' d='M12 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afcbfa' d='M13 12h1m-1 1h1'/%3E%3Cpath stroke='%23b2c8f4' d='M14 12h1m-1 1h1m-4 3h1'/%3E%3Cpath stroke='%23c1d2fb' d='M3 14h1'/%3E%3Cpath stroke='%23b6d1fb' d='M9 14h2'/%3E%3Cpath stroke='%23adc9f9' d='M13 14h1m-2 1h1'/%3E%3Cpath stroke='%23b1c6f3' d='M14 14h1m-3 2h1'/%3E%3Cpath stroke='%23abc1f4' d='M0 15h1'/%3E%3Cpath stroke='%23b7cbf9' d='M1 15h1'/%3E%3Cpath stroke='%23b9cefb' d='M2 15h1'/%3E%3Cpath stroke='%23b9cffb' d='M7 15h1'/%3E%3Cpath stroke='%23b2cdfb' d='M9 15h2'/%3E%3Cpath stroke='%23aec8f7' d='M13 15h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 15h1m-2 1h1'/%3E%3Cpath stroke='%23dbe3f8' d='M0 16h1'/%3E%3Cpath stroke='%23b7c6f1' d='M1 16h1'/%3E%3Cpath stroke='%23b8c9f2' d='M2 16h1m4 0h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 16h1'/%3E%3C/svg%3E");
background-size: 15px;
font-size: 11px;
border: none;
background-color: #fff;
box-sizing: border-box;
height: 21px;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
position: relative;
padding: 5px 32px 32px 5px;
background-position: top 50% right 2px;
background-repeat: no-repeat;
border-radius: 0;
border: 1px solid black;
}
body {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
background-color: #fafafa;
font-variant: oldstyle-nums;
text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
scroll-behavior: smooth;
text-wrap: balance;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-feature-settings: "ss02", "liga", "onum";
}
.marked_text {
background-color: yellow;
}
.time_picker_container {
font-variant: small-caps;
width: 100%;
}
.time_picker_container > input {
width: 50px;
}
#loader {
display: grid;
justify-content: center;
align-items: center;
height: 100%;
}
.no_linebreak {
line-break: auto;
}
.dark_code_bg {
background-color: #363636;
color: white;
}
.code_bg {
background-color: #C0C0C0;
}
#commands {
line-break: anywhere;
}
.color_red {
color: red;
}
.color_orange {
color: orange;
}
table > tbody > tr:nth-child(odd) {
background-color: #fafafa;
}
table > tbody > tr:nth-child(even) {
background-color: #ddd;
}
table {
border-collapse: collapse;
margin: 25px 0;
min-width: 200px;
}
th {
background-color: #4eae46;
color: #ffffff;
text-align: left;
border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
min-height: 80px;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 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 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 7h1'/%3E%3Cpath stroke='%23c5d5fc' d='M9 3h1M3 9h1m3 0h1'/%3E%3Cpath stroke='%23c5d3fc' d='M10 3h1'/%3E%3Cpath stroke='%23bed0fc' d='M12 3h1M9 4h1m-7 7h1m0 1h1'/%3E%3Cpath stroke='%23bccdfa' d='M13 3h1'/%3E%3Cpath stroke='%23baccf4' d='M14 3h1'/%3E%3Cpath stroke='%23bdcbda' d='M16 3h1'/%3E%3Cpath stroke='%23c4d4f7' d='M2 4h1'/%3E%3Cpath stroke='%23cddbfc' d='M5 4h1M3 6h1'/%3E%3Cpath stroke='%23c8d5fb' d='M8 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M10 4h3M9 5h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 4h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c2d5' d='M16 4h1'/%3E%3Cpath stroke='%23bed0f8' d='M2 5h1'/%3E%3Cpath stroke='%23ceddfd' d='M4 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M6 5h2M3 8h2'/%3E%3Cpath stroke='%234d6185' d='M8 5h1M7 6h3M6 7h5M5 8h3m1 0h3M4 9h3m3 0h3m-8 1h1m5 0h1'/%3E%3Cpath stroke='%23bacdfc' d='M10 5h1m1 0h2M3 12h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1m-2 1h1m1 0h2m-1 1h1'/%3E%3Cpath stroke='%23a8bbd4' d='M16 5h1'/%3E%3Cpath stroke='%23cddafc' d='M4 6h1'/%3E%3Cpath 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' d='M2 11h1'/%3E%3Cpath stroke='%23c3d3fd' d='M5 11h1'/%3E%3Cpath stroke='%23c1d5fb' d='M8 11h1'/%3E%3Cpath stroke='%23b7d3fc' d='M10 11h1m-2 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 11h1'/%3E%3Cpath stroke='%23afcefb' d='M12 11h1'/%3E%3Cpath 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='%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 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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}
.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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}
.title-bar-controls button[aria-label=Maximize]: not(: disabled): active{
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}
.title-bar-controls button[aria-label=Restore]{
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}
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}
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stroke='%23b5381a' d='M9 4h1M4 9h1'/%3E%3Cpath stroke='%23b8391a' d='M10 4h1m-7 6h1'/%3E%3Cpath stroke='%23ba3a1b' d='M11 4h1m-8 7h2'/%3E%3Cpath stroke='%23bc3b1c' d='M12 4h1m-9 8h1'/%3E%3Cpath stroke='%23bd3c1c' d='M13 4h1m-1 1h1m-2 1h1m-7 6h1m-3 1h2'/%3E%3Cpath stroke='%23be3d1c' d='M14 4h3m-1 1h1m-1 1h1M4 14h1m-1 1h1m-1 1h2'/%3E%3Cpath stroke='%23bf3d1c' d='M17 4h3m-3 1h3m-2 1h2m-1 1h1M4 17h2m-2 1h4m-4 1h4'/%3E%3Cpath stroke='%235b1d0d' d='M1 5h1'/%3E%3Cpath stroke='%239c3016' d='M3 5h1'/%3E%3Cpath stroke='%23a43217' d='M4 5h1'/%3E%3Cpath stroke='%23b8553e' d='M5 5h1'/%3E%3Cpath stroke='%23d59485' d='M6 5h1M5 6h1'/%3E%3Cpath stroke='%23b33619' d='M7 5h1'/%3E%3Cpath stroke='%23b53719' d='M8 5h1'/%3E%3Cpath stroke='%23b8381a' d='M9 5h1M6 8h1'/%3E%3Cpath stroke='%23b9391b' d='M10 5h1'/%3E%3Cpath stroke='%23ba391b' d='M11 5h1M6 9h1m-2 1h1'/%3E%3Cpath stroke='%23bc3b1b' d='M12 5h1m-2 1h1m-6 5h1m-2 1h1'/%3E%3Cpath stroke='%23dc9887' d='M14 5h1'/%3E%3Cpath stroke='%23c85d42' d='M15 5h1M5 15h1'/%3E%3Cpath stroke='%23611f0e' d='M1 6h1'/%3E%3Cpath stroke='%23a23217' d='M3 6h1'/%3E%3Cpath stroke='%23d79585' d='M6 6h1'/%3E%3Cpath stroke='%23d89585' d='M7 6h1'/%3E%3Cpath stroke='%23b8371a' d='M8 6h1'/%3E%3Cpath stroke='%23ba391a' d='M9 6h1'/%3E%3Cpath stroke='%23bb3a1b' d='M10 6h1m-5 4h1'/%3E%3Cpath stroke='%23dd9887' d='M13 6h3m-4 1h1m-2 1h1M9 9h1m-2 2h1m-2 1h1m-2 1h1m-2 1h2'/%3E%3Cpath stroke='%23c03e1d' d='M17 6h1m-2 1h3m0 1h1m-1 1h1M7 16h1m-2 1h2m0 1h1'/%3E%3Cpath stroke='%2365200e' d='M1 7h1'/%3E%3Cpath stroke='%23902d15' d='M2 7h1'/%3E%3Cpath stroke='%23a73418' d='M3 7h1'/%3E%3Cpath stroke='%23af3518' d='M4 7h1'/%3E%3Cpath stroke='%23b43619' d='M5 7h1'/%3E%3Cpath stroke='%23d99585' d='M6 7h1'/%3E%3Cpath stroke='%23da9686' d='M7 7h1'/%3E%3Cpath stroke='%23db9686' d='M8 7h1M7 8h1'/%3E%3Cpath stroke='%23bc3a1b' d='M9 7h1M7 9h1'/%3E%3Cpath stroke='%23bd3b1b' d='M10 7h1m-4 3h1'/%3E%3Cpath stroke='%23be3c1c' d='M11 7h1m-2 1h1m-3 2h1m-2 1h1'/%3E%3Cpath stroke='%23de9987' d='M13 7h2m-3 1h2m-4 1h2m-3 1h1m-2 2h1m-2 2h1'/%3E%3Cpath stroke='%23c03f1d' d='M15 7h1m-9 8h1'/%3E%3Cpath stroke='%236a220f' d='M1 8h1'/%3E%3Cpath stroke='%23952f15' d='M2 8h1'/%3E%3Cpath stroke='%23ac3518' d='M3 8h1'/%3E%3Cpath stroke='%23b63719' d='M5 8h1'/%3E%3Cpath stroke='%23dc9786' d='M8 8h2M8 9h1'/%3E%3Cpath stroke='%23c2401d' d='M14 8h1m2 0h1m1 3h1M8 14h1m-1 2h1m-1 1h1m0 1h1m1 1h1'/%3E%3Cpath stroke='%23c2401e' d='M15 8h2m1 1h1M8 15h1'/%3E%3Cpath stroke='%23c13f1d' d='M18 8h1m0 2h1M9 19h2'/%3E%3Cpath stroke='%23702410' d='M1 9h1'/%3E%3Cpath stroke='%239b3016' d='M2 9h1'/%3E%3Cpath stroke='%23b03619' d='M3 9h1'/%3E%3Cpath stroke='%23b9381a' d='M5 9h1'/%3E%3Cpath stroke='%23df9a88' d='M12 9h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23c4421e' d='M13 9h1m2 0h2m0 1h1M9 13h1m9 1h1m-1 1h1M9 16h1m9 0h1M9 17h1m0 1h1m3 1h3'/%3E%3Cpath stroke='%23c5431e' d='M14 9h1'/%3E%3Cpath stroke='%23c5431f' d='M15 9h1m-4 1h1m5 1h1m-9 1h1m-2 2h1m-1 1h1m0 2h1m0 1h1m6 0h1'/%3E%3Cpath 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
}
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];
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));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
upArrow.style.display = "block";
} else {
upArrow.style.display = "none";
}
if (scrollPosition + windowHeight < pageHeight) {
downArrow.style.display = "block";
} else {
downArrow.style.display = "none";
}
}
window.addEventListener("scroll", checkScrollPosition);
upArrow.addEventListener("click", function () {
window.scrollTo({ top: 0, behavior: 'smooth' });
});
downArrow.addEventListener("click", function () {
window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
});
checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
function plotGPUUsage() {
if ($("#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();
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}, 300);
}
function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
if (searchInput) {
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$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();
colorize_table_entries();
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</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>const</th><th>max_depth</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 104</td><td>0.999994</td><td>4</td><td>0.613847</td><td>0.250563 </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></tr></thead><tbody><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int </td></tr><tr><td> const</td><td>range</td><td>0.1</td><td>1</td><td></td><td>float </td></tr><tr><td> max_depth</td><td>range</td><td>2</td><td>4</td><td></td><td>int </td></tr><tr><td> threshold</td><td>range</td><td>0.2</td><td>0.8</td><td></td><td>float </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>496</td>
<td>11</td>
<td>507</td>
</tr>
</tbody>
</table>
<h1> 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 »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_samples,const,max_depth,threshold
0,0_0,COMPLETED,Sobol,0.424606151537884501934172476467,804,0.355191910266876242907585492503,2,0.688604998588562056127670985006
1,1_0,COMPLETED,Sobol,0.389597399349837503201854360668,646,0.494997762795537754598740320944,4,0.709141819179058163769013845013
2,2_0,COMPLETED,Sobol,0.358839709927481820272987533826,260,0.470496729761362142419045540009,2,0.538578139059245675213105641888
3,3_0,COMPLETED,Sobol,0.377594398599649938574884799891,449,0.912662879005074478833137163747,4,0.327099011093378133629983040009
4,4_0,COMPLETED,Sobol,0.354338584646161525171237371978,290,0.807349003944546006472648969066,4,0.386300026066601298602165570628
5,5_0,COMPLETED,Sobol,0.373843460865216359323426331684,423,0.313943139836192153246940961253,3,0.257359432615339778216423383128
6,6_0,COMPLETED,Sobol,0.343835958989747392244851198484,328,0.987501259706914380487319249369,3,0.351141243055462903832619758759
7,7_0,COMPLETED,Sobol,0.324331082770692669114964701294,221,0.490980252064764477459846148122,2,0.427187782898545309606674891256
8,8_0,COMPLETED,Sobol,0.411602900725181242158612349158,709,0.646598942112177610397338867188,2,0.410460004769265696111801844381
9,9_0,COMPLETED,Sobol,0.388347086771692939777267383761,652,0.305540853552520252911506304372,2,0.587577150762081279466997330019
10,10_0,COMPLETED,Sobol,0.363840960240059962949032978941,344,0.935464081354439258575439453125,4,0.294472466409206434789780360006
11,11_0,COMPLETED,Sobol,0.400100025006251525105938071647,718,0.204116850439459096566707785314,2,0.348088733293116125988575504380
12,12_0,COMPLETED,Sobol,0.363840960240059962949032978941,250,0.929595873225480318069458007812,2,0.649391442537307739257812500000
13,13_0,COMPLETED,Sobol,0.393348337084271082453312828875,652,0.589934919681400216084909970959,4,0.481370603851974054876450281881
14,14_0,COMPLETED,Sobol,0.276069017254313542331090047810,116,0.352333464194089174270629882812,2,0.737698853760957895531191752525
15,15_0,COMPLETED,Sobol,0.424856214053513370210168886842,958,0.161001199670135985986263449377,3,0.378761482052505038531364789378
16,16_0,COMPLETED,Sobol,0.426856714178544649485047557391,948,0.614325035829097032546997070312,3,0.466140186786651644634815738755
17,17_0,COMPLETED,Sobol,0.426606651662915781209051147016,919,0.382197991572320483477653851878,4,0.390500460378825686724724164378
18,18_0,COMPLETED,Sobol,0.416354088522130516558661383897,854,0.669267002120614074023308148753,2,0.377037539146840572357177734375
19,19_0,COMPLETED,Sobol,0.388347086771692939777267383761,597,0.947661051340401128229018468119,4,0.222777565941214561462402343750
20,20_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.306444251856041238735173237728,2,0.617700926673672490174737959023
21,21_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.291189111572389136561866962438,2,0.788016810164061443089167369180
22,22_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.419590552525422544327682317089,3,0.681218564753604427508548724290
23,23_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.107219092064853457890727383983,2,0.692766283825185347211572661763
24,24_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.434643338042540405830038707791,2,0.681212125100234544561317306943
25,25_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.469484306122967431917913927464,2,0.496302794864017293718916334910
26,26_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.418763437134718419230239305762,2,0.800000000000000044408920985006
27,27_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.205324796186224789451557626307,3,0.775495919078641238186833106738
28,28_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.218789498844353003104146182523,3,0.572502830901044146294509573636
29,29_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.273266838896060904051665829684,2,0.669960777294955844851642723370
30,30_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.574822450878602064783251535118,2,0.705861387725882361010576460103
31,31_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.550000577284842195879832615901,3,0.494941172955118080523106982582
32,32_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.381080810170699280092776461970,3,0.773677049982346431988844415173
33,33_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.485681394001209021382692299085,2,0.656382055602220293444304388686
34,34_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.217800353368568316847486698862,2,0.496316566166169870211177794772
35,35_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.293274331445568969822801363989,2,0.702896070945567386090147010691
36,36_0,COMPLETED,BoTorch,0.319829957489372374013214539445,150,0.100000000000000005551115123126,2,0.732999933801112835141111645498
37,37_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.303814154247042278456092390115,3,0.490926843364170695238613006950
38,38_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.326023778408373599013714283501,2,0.554441296902848823613396689325
39,39_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.113076100400250345590080769398,2,0.800000000000000044408920985006
40,40_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.693822144385377037600903804559,2,0.200000000000000011102230246252
41,41_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.955358618844174833917293199192,2,0.218991660182812436508115183642
42,42_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.360693524082099425953629179276,2,0.200000000000000011102230246252
43,43_0,COMPLETED,BoTorch,0.317579394848712226462339458521,157,0.254917576997851336173539493757,2,0.800000000000000044408920985006
44,44_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.365026935099056593081456867367,3,0.455997916680213721818404337682
45,45_0,COMPLETED,BoTorch,0.320830207551887958139502643462,171,0.133370385680159492247653929553,3,0.800000000000000044408920985006
46,46_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.100000000000000005551115123126,3,0.430494390014774463981694907488
47,47_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.685784010468876048527420152823,2,0.344804364832543397412223384890
48,48_0,COMPLETED,BoTorch,0.272818204551137810653926862869,136,0.507792075119600117005802530912,3,0.200000000000000011102230246252
49,49_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.368690403436225500044542968681,2,0.651581193878452147316693299217
50,50_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.250546365281839822358733727015,3,0.242676365222636497565034119361
51,51_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.887863512601483884090214360185,3,0.541316418402806309728703126893
52,52_0,COMPLETED,BoTorch,0.299324831207801955734737475723,143,0.300122371534258247649518125399,2,0.200000000000000011102230246252
53,53_0,COMPLETED,BoTorch,0.288322080520130086256358481478,109,0.272554887870771089808386022924,3,0.302214598380782795139509744331
54,54_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.152989410441737666568329245820,4,0.547622286486337039868033116363
55,55_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.705955529139108750591447005718,3,0.200000000000000011102230246252
56,56_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
57,57_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.696918279252083383568106000894,4,0.743144919153433614056325495767
58,58_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.240951007171628206471325484017,4,0.338599669988341678283916280634
59,59_0,COMPLETED,BoTorch,0.290572643160790233807233562402,145,0.314486290097687526401415425426,4,0.472096338115887137476534007874
60,60_0,COMPLETED,BoTorch,0.292073018254563665507816949685,127,0.807583384608269616578013483377,3,0.390832008136810071796674037614
61,61_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.841761008546026423537966820732,2,0.250154467449882789154003148724
62,62_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.455084401112905378994355487521,3,0.296057782760091647844546969282
63,63_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,3,0.639653593977541512494155995228
64,64_0,COMPLETED,BoTorch,0.272568142035508831355627989979,131,1.000000000000000000000000000000,3,0.200000000000000011102230246252
65,65_0,COMPLETED,BoTorch,0.268817204301075252104169521772,131,0.803433343727120385935336344119,4,0.537728500266900333315334137296
66,66_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.551433642703019533115593731054,2,0.200000000000000011102230246252
67,67_0,COMPLETED,BoTorch,0.268817204301075252104169521772,131,0.815720686100123382189508447482,4,0.239004039956049629811474233065
68,68_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.574429573542817095699319906998,3,0.622326501938141207759258577425
69,69_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,2,0.523369565286156968042519110895
70,70_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.100000000000000005551115123126,4,0.200000000000000011102230246252
71,71_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.100000000000000005551115123126,3,0.200000000000000011102230246252
72,72_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.540706476994079365816503468523,4,0.200000000000000011102230246252
73,73_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.100000000000000005551115123126,4,0.200000000000000011102230246252
74,74_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,0.100000000000000005551115123126,2,0.200000000000000011102230246252
75,75_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,0.494589746404622832010034017003,3,0.200000000000000011102230246252
76,76_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.100000000000000005551115123126,4,0.590562607776977666063089600357
77,77_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.438517813872483785964107028121,4,0.200000000000000011102230246252
78,78_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,3,0.200000000000000011102230246252
79,79_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.739082785376435125179739316081,3,0.200000000000000011102230246252
80,80_0,COMPLETED,BoTorch,0.264066016504125977704120487033,121,1.000000000000000000000000000000,4,0.200000000000000011102230246252
81,81_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,4,0.634366332350308370635616483924
82,82_0,COMPLETED,BoTorch,0.273818454613653394780214966886,116,0.874124302662239505146146711922,4,0.200000000000000011102230246252
83,83_0,COMPLETED,BoTorch,0.292823205801450381358108643326,140,1.000000000000000000000000000000,3,0.200000000000000011102230246252
84,84_0,COMPLETED,BoTorch,0.269817454363590947252760088304,116,0.863004108513081735765126722981,3,0.200000000000000011102230246252
85,85_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,1.000000000000000000000000000000,2,0.200000000000000011102230246252
86,86_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,1.000000000000000000000000000000,4,0.361172035751472220166391480234
87,87_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,4,0.394609154010147789026774489685
88,88_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.975852408397702486553271228331,4,0.200000000000000011102230246252
89,89_0,COMPLETED,BoTorch,0.268567141785446383828173111397,115,1.000000000000000000000000000000,2,0.200000000000000011102230246252
90,90_0,COMPLETED,BoTorch,0.293573393348337097208400336967,115,0.611223177940916073680455156136,3,0.200000000000000011102230246252
91,91_0,COMPLETED,BoTorch,0.301575393848462103285612556647,140,1.000000000000000000000000000000,4,0.200000000000000011102230246252
92,92_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.351881429180319726945924685424,3,0.302701587448269804347944500478
93,93_0,COMPLETED,BoTorch,0.281570392598149532581430776190,129,1.000000000000000000000000000000,4,0.440810689282914225373133376706
94,94_0,COMPLETED,BoTorch,0.281570392598149532581430776190,129,1.000000000000000000000000000000,4,0.432497661304634517520639747090
95,95_0,COMPLETED,BoTorch,0.304076019004751230134786510462,125,1.000000000000000000000000000000,4,0.200000000000000011102230246252
96,96_0,COMPLETED,BoTorch,0.286071517879469827683180938038,133,0.100000000000000005551115123126,3,0.200000000000000011102230246252
97,97_0,COMPLETED,BoTorch,0.278319579894973689881965128734,124,0.932849181146925054974872182356,4,0.200000000000000011102230246252
98,98_0,COMPLETED,BoTorch,0.384346086521630381227510042663,521,0.209998931150957912628030044289,2,0.716665702831517981152842367010
99,99_0,COMPLETED,BoTorch,0.294323580895223813058692030609,149,1.000000000000000000000000000000,2,0.200000000000000011102230246252
100,100_0,COMPLETED,BoTorch,0.316829207301825510612047764880,177,1.000000000000000000000000000000,2,0.200000000000000011102230246252
101,101_0,COMPLETED,BoTorch,0.390597649412353087328142464685,524,0.300624071976989759580334293787,3,0.514060622048500448499908088706
102,102_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.994564123595890969831145866920,2,0.200877583763874462130516462821
103,103_0,COMPLETED,BoTorch,0.312328082020505104487995140516,172,0.891076978112465889481086378510,2,0.239148733964359483383788074207
104,104_0,COMPLETED,BoTorch,0.284321080270067527706601140380,135,1.000000000000000000000000000000,2,0.200000000000000011102230246252
105,105_0,COMPLETED,BoTorch,0.267066766691672952127589724114,107,0.794924273575133844005335959082,4,0.200000000000000011102230246252
106,106_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,4,0.435398190927561934415734867798
107,107_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.815006137091721538645572309179,4,0.479645736219914020637133944547
108,108_0,COMPLETED,BoTorch,0.288822205551387822808351302228,126,1.000000000000000000000000000000,2,0.200000000000000011102230246252
109,109_0,COMPLETED,BoTorch,0.305576394098524661835369897744,125,0.742023673686266205251627070538,4,0.200000000000000011102230246252
110,110_0,RUNNING,BoTorch,,174,0.482933293032632882102461735485,4,0.200000000000000011102230246252
111,111_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.810569395106448764565243436664,4,0.499766488547969012223859408550
112,112_0,COMPLETED,BoTorch,0.309077269317329372810831955576,184,0.742123642083571621874682477937,4,0.396004149328738341839795111810
113,113_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,1.000000000000000000000000000000,3,0.200000000000000011102230246252
114,114_0,COMPLETED,BoTorch,0.327831957989497380090426759125,197,0.100000000000000005551115123126,4,0.200000000000000011102230246252
115,115_0,COMPLETED,BoTorch,0.305826456614153530111366308120,125,0.758720311414586401355109046563,3,0.200000000000000011102230246252
116,116_0,COMPLETED,BoTorch,0.319829957489372374013214539445,150,0.470679202442742194989477866329,4,0.200000000000000011102230246252
117,117_0,COMPLETED,BoTorch,0.292073018254563665507816949685,127,0.690125469209516007040861040878,4,0.200000000000000011102230246252
118,118_0,COMPLETED,BoTorch,0.322830707676919237414381314011,180,0.329235935289270420511797965446,3,0.200000000000000011102230246252
119,119_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.187316804149583071570361880731,2,0.435589549928915964471798361046
120,120_0,COMPLETED,BoTorch,0.281070267566891685007135492924,109,1.000000000000000000000000000000,4,0.344082379059187259962016014470
121,121_0,COMPLETED,BoTorch,0.284321080270067527706601140380,135,0.817826042300227151748970300105,4,0.800000000000000044408920985006
122,122_0,RUNNING,BoTorch,,109,1.000000000000000000000000000000,4,0.312452915817014120758443596060
123,123_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,1.000000000000000000000000000000,4,0.375796397073783650100153863605
124,124_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.771861456166849091431458873558,4,0.800000000000000044408920985006
125,125_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.849031030601169578453379926941,4,0.444389355230059179824309012474
126,126_0,COMPLETED,BoTorch,0.312328082020505104487995140516,172,0.386146898570099650349618514156,4,0.200000000000000011102230246252
127,127_0,COMPLETED,BoTorch,0.260065016254063530176665608451,120,0.833691857195334562469213324221,4,0.313856728827658137959133455297
128,128_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.183931821772425674321738142680,3,0.311638779116048592054966093201
129,129_0,COMPLETED,BoTorch,0.330582645661415375215597123315,198,0.430196361213256439626206883986,4,0.200000000000000011102230246252
130,130_0,COMPLETED,BoTorch,0.309077269317329372810831955576,179,0.164687766307813510113788879607,4,0.212450260103791394028505123970
131,131_0,COMPLETED,BoTorch,0.305076269067266814261074614478,181,0.551495677668769168633389199385,3,0.201627516843725901329875682677
132,132_0,COMPLETED,BoTorch,0.259314828707176814326373914810,112,1.000000000000000000000000000000,4,0.410902053365496566783576781745
133,133_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,4,0.317875825487976160221847976572
134,134_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.566399598631598721887314695778,2,0.403521829421623401401575392811
135,135_0,COMPLETED,BoTorch,0.283320830207551832558010573848,133,0.789334273103131645044072683959,2,0.364387340321895958350495448030
136,136_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,3,0.317129241655597304827551852213
137,137_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.509742213052080339608096437587,2,0.443394331004298258847029501339
138,138_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,0.993123264978424957760694269382,4,0.380351317060630966793155494088
139,139_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,2,0.361328690539701402606453939370
140,140_0,COMPLETED,BoTorch,0.286071517879469827683180938038,133,0.499676527138228188107405003393,2,0.473267592536426029425911110593
141,141_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,2,0.375555143706168381712018344842
142,142_0,COMPLETED,BoTorch,0.268317079269817404529874238506,119,0.805543472401036519947581382439,4,0.369518638296195245374065052602
143,143_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.615425905535865958029262401396,3,0.395597999065405825369623471488
144,144_0,COMPLETED,BoTorch,0.321330332583145805713797926728,219,0.100000000000000005551115123126,4,0.800000000000000044408920985006
145,145_0,COMPLETED,BoTorch,0.289322330582645670382646585495,127,1.000000000000000000000000000000,3,0.326273843161262067091854532919
146,146_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,2,0.745180433082964777113943455333
147,147_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.869881937528376281143493997661,2,0.200000000000000011102230246252
148,148_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.200000000000000011102230246252
149,149_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.365170970667487715388688229723,2,0.314281816466359331663227294484
150,150_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.707053424378947026340824777435,2,0.779454584530959948551753768697
151,151_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.578695879337874363734783855762,2,0.385064316364432057682165577717
152,152_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,1.000000000000000000000000000000,2,0.800000000000000044408920985006
153,153_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.168133216273176455679561058787,2,0.200000000000000011102230246252
154,154_0,COMPLETED,BoTorch,0.340585146286571660567688013543,231,0.363095715366456461836719427083,4,0.800000000000000044408920985006
155,155_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,0.823343898349617964171898165660,4,0.311474978668095481282307446236
156,156_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.548515495006930753341123363498,3,0.461741433845498039367782894260
157,157_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.508177623583070392498939327197,2,0.360975277400786775938712480638
158,158_0,COMPLETED,BoTorch,0.289572393098274538658642995870,145,0.763497378353212186041787390423,4,0.656120774559123653979497703403
159,159_0,COMPLETED,BoTorch,0.303575893973493382560491227196,161,1.000000000000000000000000000000,4,0.760259955963611844254046445712
160,160_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,0.937764596806095585002083225845,4,0.394124146019359966608419654222
161,161_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.886316538091796557452539673250,4,0.350218293815040215832823378150
162,162_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.778764703440061434314145571989,3,0.539387403467770920606483286974
163,163_0,COMPLETED,BoTorch,0.298074518629657392310150498815,153,1.000000000000000000000000000000,3,0.628493505113686179441856438643
164,164_0,COMPLETED,BoTorch,0.318579644911227810588627562538,168,0.738572607067920161583174376574,4,0.800000000000000044408920985006
165,165_0,COMPLETED,BoTorch,0.293073268317079249634105053701,148,0.751566712122554658748185829609,3,0.544611584131581061285487521673
166,166_0,COMPLETED,BoTorch,0.288072018004501106958059608587,149,0.797970724371749429160161071195,4,0.683057012718588651978279813193
167,167_0,COMPLETED,BoTorch,0.258564641160290098476082221168,112,0.915763377289078084331208629010,4,0.378650620479047517186188542837
168,168_0,COMPLETED,BoTorch,0.328832208052012964216714863142,241,0.100000000000000005551115123126,3,0.434494296114224742844101001538
169,169_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.629764129201447975248129296233,4,0.588088343398097013192682425142
170,170_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.962964698548357089791238649923,3,0.427658823549549427234239828977
171,171_0,COMPLETED,BoTorch,0.306576644161040245961658001761,153,1.000000000000000000000000000000,2,0.587153476524125084168304056220
172,172_0,COMPLETED,BoTorch,0.299824956239059803309032758989,135,0.745822116784916944176586639514,2,0.544121941815105203410496415017
173,173_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.827611575533053089870350049750,3,0.268758229960108396827678234331
174,174_0,COMPLETED,BoTorch,0.279819954988747232604850978532,141,1.000000000000000000000000000000,2,0.496055723357998634703847073979
175,175_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.690252747052303772257175751292,4,0.379316123232398272335785804898
176,176_0,COMPLETED,BoTorch,0.284571142785696395982597550756,141,0.829531273845949268519461838878,2,0.604076118093288871868651312980
177,177_0,COMPLETED,BoTorch,0.279819954988747232604850978532,138,0.892858250315531565277638037514,2,0.508344152498240275939167531760
178,178_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.802276616479083437560859692894,3,0.375773198110284201156616745720
179,179_0,COMPLETED,BoTorch,0.279819954988747232604850978532,138,0.703059145765578263898021305067,2,0.502117528977239047094371926505
180,180_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.801826514657622446691220829962,4,0.382489158967147635515004822082
181,181_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,1.000000000000000000000000000000,2,0.469273848852979458268208645677
182,182_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.341926562172202797018627506986
183,183_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.361218419901333298582812858513
184,184_0,COMPLETED,BoTorch,0.335833958489622386167638978804,277,0.652037357889697410939788824180,2,0.758349342422176331268701687804
185,185_0,COMPLETED,BoTorch,0.267816954238559667977881417755,119,1.000000000000000000000000000000,4,0.383305619574218492395800694794
186,186_0,COMPLETED,BoTorch,0.260065016254063530176665608451,120,0.866555181411025832183270267706,4,0.354446227755446707785580429118
187,187_0,COMPLETED,BoTorch,0.268317079269817404529874238506,119,0.891011085697958282736408364144,4,0.369120428275470313650430398411
188,188_0,COMPLETED,BoTorch,0.278819704926231537456260412000,131,0.615240076151345571808803924796,4,0.460038000781976041952958667025
189,189_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,4,0.298110710880169160752473089815
190,190_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.204561782784976775584340202840
191,191_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.391419670086716298129658753169,3,0.651583099925149400455381965003
192,192_0,COMPLETED,BoTorch,0.336584146036509102017930672446,272,0.856497506172069100749411063589,2,0.396854329683163031816661714402
193,193_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,0.912871013282556376111642748583,4,0.396425274701186380887918403459
194,194_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,1.000000000000000000000000000000,2,0.322438055455256833425892182277
195,195_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,0.936261693752430290693666847801,4,0.441620754726279707291780596279
196,196_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.634370897759171437080283340038,4,0.202680907845545016376931357627
197,197_0,COMPLETED,BoTorch,0.279819954988747232604850978532,108,1.000000000000000000000000000000,4,0.306861455817827921688234482644
198,198_0,COMPLETED,BoTorch,0.267066766691672952127589724114,107,1.000000000000000000000000000000,4,0.296635269217448538370263122488
199,199_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.267560930379156514113958564849
200,200_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.558457506855587926253292607726,3,0.495368226815706858001675527703
201,201_0,RUNNING,BoTorch,,105,1.000000000000000000000000000000,4,0.276707349586912232375368603243
202,202_0,COMPLETED,BoTorch,0.259314828707176814326373914810,112,1.000000000000000000000000000000,4,0.200000000000000011102230246252
203,203_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,0.868611823450960440773371828982,4,0.329269159059516924870081311383
204,204_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.765459828832301347745215025498,4,0.399337708864830442934135135147
205,205_0,COMPLETED,BoTorch,0.250562640660165092398870001489,104,1.000000000000000000000000000000,4,0.251125074422064775703233863169
206,206_0,COMPLETED,BoTorch,0.267066766691672952127589724114,115,1.000000000000000000000000000000,4,0.299735146995161960692399816253
207,207_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.278950448218898860996972643989
208,208_0,COMPLETED,BoTorch,0.289822455613903517956941868761,147,0.545341513176659908879173599416,2,0.414259569087121692909647663328
209,209_0,COMPLETED,BoTorch,0.253063265816454108225741492788,106,1.000000000000000000000000000000,4,0.305519568595645829578444363506
210,210_0,COMPLETED,BoTorch,0.359089772443110799571286406717,342,0.700004732291977016522821486433,3,0.482526382098135198095434361676
211,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
212,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
213,213_0,COMPLETED,BoTorch,0.306326581645411377685661591386,115,0.445382703240380317666335940885,2,0.362941993874897783634025927313
214,214_0,COMPLETED,BoTorch,0.289072268067016802106650175119,132,0.694642970553881688999808829976,3,0.448362179732559817946224711704
215,215_0,COMPLETED,BoTorch,0.279819954988747232604850978532,108,1.000000000000000000000000000000,4,0.200000000000000011102230246252
216,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
217,217_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,1.000000000000000000000000000000,4,0.245942470023802983725147441874
218,218_0,COMPLETED,BoTorch,0.271567891972993247229339885962,137,0.778954710278577655557796788344,3,0.539542309207637682533231782145
219,211_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
220,220_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,0.217786124134503461524658973758,4,0.365303712186263340733205495781
221,221_0,COMPLETED,BoTorch,0.358839709927481820272987533826,343,0.100000000000000005551115123126,2,0.800000000000000044408920985006
222,222_0,COMPLETED,BoTorch,0.346586646661665387370021562674,288,0.100000000000000005551115123126,2,0.800000000000000044408920985006
223,223_0,COMPLETED,BoTorch,0.288322080520130086256358481478,109,1.000000000000000000000000000000,2,0.720568206042152104018327918311
224,224_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.563146078035753339108282489178,2,0.749625672745976689981262097717
225,225_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.896068730123848466995184480766,3,0.534059884369033177620167407440
226,226_0,COMPLETED,BoTorch,0.274568642160540110630506660527,116,1.000000000000000000000000000000,4,0.320651682662672188328656375234
227,227_0,COMPLETED,BoTorch,0.259314828707176814326373914810,112,1.000000000000000000000000000000,4,0.343685005329892934167190787775
228,228_0,COMPLETED,BoTorch,0.321330332583145805713797926728,125,0.782538794082730304602080195764,3,0.495888682473096475966656271339
229,229_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.574333647445877115700341164484,3,0.622477701345453882098013309587
230,230_0,COMPLETED,BoTorch,0.274568642160540110630506660527,132,0.410872344080077112060678246053,4,0.553965792765449993595439082128
231,231_0,COMPLETED,BoTorch,0.286571642910727675257476221304,140,0.837072930304496365394584245223,2,0.200000000000000011102230246252
232,232_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.368634423676738265385210979730,2,0.651489027673128551221282123151
233,233_0,COMPLETED,BoTorch,0.318079519879969963014332279272,135,0.696379538661256347609196382109,2,0.800000000000000044408920985006
234,234_0,COMPLETED,BoTorch,0.267066766691672952127589724114,115,1.000000000000000000000000000000,4,0.313397998116239806520866295614
235,235_0,COMPLETED,BoTorch,0.336584146036509102017930672446,140,0.500627054817103345207840447983,4,0.653493728278130570075177274703
236,236_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,1.000000000000000000000000000000,3,0.200000000000000011102230246252
237,237_0,COMPLETED,BoTorch,0.274068517129282374078513839777,110,1.000000000000000000000000000000,4,0.211178212019044297953485056496
238,238_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,1.000000000000000000000000000000,3,0.332016367996864458778105699821
239,239_0,COMPLETED,BoTorch,0.274068517129282374078513839777,110,1.000000000000000000000000000000,4,0.218012146608940637904083814647
240,240_0,COMPLETED,BoTorch,0.269817454363590947252760088304,116,0.862934271159563670572367755085,3,0.200000000000000011102230246252
241,241_0,COMPLETED,BoTorch,0.291322830707676949657525256043,139,0.100000000000000005551115123126,4,0.800000000000000044408920985006
242,242_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,0.808899042933486134288045832363,4,0.200000000000000011102230246252
243,243_0,COMPLETED,BoTorch,0.274318579644911242354510250152,110,1.000000000000000000000000000000,4,0.411639440914393639481261288893
244,244_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,1.000000000000000000000000000000,3,0.307448454178484764653944694146
245,245_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.100000000000000005551115123126,3,0.800000000000000044408920985006
246,246_0,COMPLETED,BoTorch,0.296074018504626113035271828267,140,0.909754852166291816395471414580,4,0.800000000000000044408920985006
247,247_0,COMPLETED,BoTorch,0.274068517129282374078513839777,110,1.000000000000000000000000000000,4,0.200000000000000011102230246252
248,248_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.309761848662823191524751109682,3,0.800000000000000044408920985006
249,249_0,COMPLETED,BoTorch,0.336584146036509102017930672446,140,0.466843497959019804177671630896,4,0.800000000000000044408920985006
250,250_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.394125602210631864608103569481
251,251_0,COMPLETED,BoTorch,0.304576144036008966686779331212,125,0.812953784026579362453901467234,4,0.272988858239856690968139218967
252,252_0,COMPLETED,BoTorch,0.294323580895223813058692030609,149,0.261274780514945959009054377020,3,0.414663585542853874166269179113
253,253_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.100000000000000005551115123126,3,0.496999849952154959531469557987
254,254_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.400123605305035656698464663350
255,255_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.391734976195479700500357012061,3,0.651784479376789516180679129320
256,256_0,RUNNING,BoTorch,,139,0.735207535889332408629570636549,3,0.274237824375596628279083688540
257,257_0,COMPLETED,BoTorch,0.327581895473868511814430348750,199,0.646985955364709819370716559206,4,0.200000000000000011102230246252
258,56_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
259,259_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.260655654332802189099282941243,3,0.276161757871757340687679516122
260,260_0,COMPLETED,BoTorch,0.279569892473118253306552105641,136,0.913242927622038735968601486093,2,0.384574173698379428998350704205
261,261_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.100000000000000005551115123126,3,0.475117384893876670837187248253
262,262_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,1.000000000000000000000000000000,2,0.439296380580260714676654743016
263,263_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,2,0.636080261639037636278715126537
264,264_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.321924225653422324544550292558,3,0.618518030849716904384649751591
265,265_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,3,0.589696417472513667590305885824
266,266_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.179724787686078690818192171719,2,0.800000000000000044408920985006
267,267_0,COMPLETED,BoTorch,0.294323580895223813058692030609,149,0.292112099987361706343591549739,3,0.497794681718722209495808783686
268,268_0,COMPLETED,BoTorch,0.267816954238559667977881417755,119,1.000000000000000000000000000000,4,0.305944267056477481325771350384
269,269_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.462033126174450581302721730026,2,0.800000000000000044408920985006
270,270_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.507901938175407630104984946229,2,0.627577790751056463491863723902
271,271_0,COMPLETED,BoTorch,0.288072018004501106958059608587,133,1.000000000000000000000000000000,2,0.436594589517995901317704010580
272,272_0,COMPLETED,BoTorch,0.292073018254563665507816949685,142,0.186797929666732365205916721607,3,0.778620227078443694068710101419
273,273_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,0.100000000000000005551115123126,2,0.605340982541530570060217542050
274,274_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.368745357567170861656791203131,2,0.651487258862999518704839374550
275,275_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.367328151624619514414860077522,2,0.599872681460932533070717909141
276,276_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.173398003657111388076117464152,2,0.658298009012616591739686100482
277,277_0,COMPLETED,BoTorch,0.318079519879969963014332279272,135,0.656172595607408370987911894190,3,0.200000000000000011102230246252
278,278_0,COMPLETED,BoTorch,0.279569892473118253306552105641,136,1.000000000000000000000000000000,2,0.266053842348531743855488684858
279,279_0,COMPLETED,BoTorch,0.295073768442110528908983724250,147,0.755133970020621880792077718070,3,0.369219927858086049976549247731
280,280_0,COMPLETED,BoTorch,0.294573643410852681334688440984,134,1.000000000000000000000000000000,2,0.334715010251796118989631168006
281,281_0,COMPLETED,BoTorch,0.279569892473118253306552105641,136,0.888971972008698707590212961804,3,0.684980645020563772007449188095
282,282_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.241020146552075431589656773212,3,0.800000000000000044408920985006
283,283_0,COMPLETED,BoTorch,0.283320830207551832558010573848,133,0.782474569148861820444551540277,2,0.302350153974637214648879535162
284,284_0,COMPLETED,BoTorch,0.295823955988997244759275417891,143,1.000000000000000000000000000000,3,0.436908041124190571480312428321
285,285_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.100000000000000005551115123126,3,0.800000000000000044408920985006
286,286_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.505889829568764048950413325656,3,0.617280330653734887746963977406
287,287_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,1.000000000000000000000000000000,2,0.517911792475650001321696436207
288,288_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.805012052894248597567639080808,2,0.800000000000000044408920985006
289,289_0,COMPLETED,BoTorch,0.284071017754438659430604730005,135,1.000000000000000000000000000000,3,0.373994731156762960644357463025
290,290_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,2,0.631296959339576635450441699504
291,291_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,3,0.800000000000000044408920985006
292,292_0,RUNNING,BoTorch,,119,1.000000000000000000000000000000,4,0.405362795471797543456204948598
293,293_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.686937584294991743227853930875,3,0.696768265601847236467847324093
294,294_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.379140968943526113221764717309
295,295_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.383046815159447273657633559196,4,0.456288884020715213019059319777
296,296_0,COMPLETED,BoTorch,0.283070767691922964282014163473,134,1.000000000000000000000000000000,2,0.200000000000000011102230246252
297,297_0,COMPLETED,BoTorch,0.306576644161040245961658001761,153,1.000000000000000000000000000000,2,0.620471477037471741411422954116
298,298_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,1.000000000000000000000000000000,2,0.454921349971635469167097198806
299,299_0,COMPLETED,BoTorch,0.294573643410852681334688440984,141,0.529306429997960514732824321982,4,0.460062570952162686044317752021
300,300_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.295711213702062214458976541209,4,0.461894378669080785115852449962
301,301_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.378266192146028523701772883214
302,302_0,COMPLETED,BoTorch,0.274818704676169089928805533418,131,1.000000000000000000000000000000,3,0.616622157130604753305647136585
303,303_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.377171915208691310716915268131
304,304_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.371761937848294587993791537883
305,305_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.370534762480242862991985930421
306,306_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.762625246910083487428266835195,4,0.573666527043149576670089118124
307,307_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.647695718996099678577138547553,4,0.619933025815884497511376594048
308,308_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.366748736894267068908703777197
309,309_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,2,0.476200186878467157658434416589
310,310_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.920911740206398654606800846523,3,0.589396897793263541132091631880
311,311_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.491113369284582046425668977463,2,0.266359857455391035863101478753
312,312_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.372839774533017476176866011883
313,313_0,COMPLETED,BoTorch,0.284821205301325375280896423646,140,0.648487957130945424921719677513,3,0.376006982045066884268180729123
314,314_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,1.000000000000000000000000000000,2,0.800000000000000044408920985006
315,315_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.372097758162890257516153269535
316,316_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,3,0.597073646865899809732525227446
317,317_0,COMPLETED,BoTorch,0.299824956239059803309032758989,135,0.827026791976526398642022286367,2,0.494435537430720661866700993414
318,318_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,0.745391413098200228404266454163,4,0.329220049971043371428436330461
319,319_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,1.000000000000000000000000000000,2,0.200000000000000011102230246252
320,320_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.355810006149157198596100215582
321,321_0,COMPLETED,BoTorch,0.277319329832458105755677024717,132,1.000000000000000000000000000000,3,0.544117701807304010586108233838
322,322_0,COMPLETED,BoTorch,0.299824956239059803309032758989,144,0.598928960994094095049433690292,2,0.580430274457044315106202247989
323,323_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.776550343627608552488084114884,2,0.394422867762336260000211041188
324,324_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.351377457791350267246599514692
325,325_0,COMPLETED,BoTorch,0.286071517879469827683180938038,133,0.640570597045141632008835586021,3,0.630731672418594402351743610780
326,326_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.677483119244375764367305237101,2,0.200000000000000011102230246252
327,327_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,4,0.525092678581893457234741617867
328,328_0,COMPLETED,BoTorch,0.343335833958489655692858377734,248,0.665717795880264917585122930177,3,0.352743170199508460083137606489
329,329_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.601600510989812486961625381809,2,0.439965251976133120415113353374
330,330_0,COMPLETED,BoTorch,0.267066766691672952127589724114,107,0.904910323058552235053753065586,4,0.338583103791986195219010369328
331,331_0,COMPLETED,BoTorch,0.320830207551887958139502643462,157,0.566076796214043165278440028487,4,0.392483898884218695979342328428
332,332_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
333,333_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.346938986638215596247647454220
334,334_0,COMPLETED,BoTorch,0.276319079769942521629388920701,102,1.000000000000000000000000000000,4,0.797650014636681170543397456640
335,335_0,COMPLETED,BoTorch,0.292073018254563665507816949685,127,0.772521224032820863492077023693,3,0.526138142727633151274346801074
336,336_0,COMPLETED,BoTorch,0.286071517879469827683180938038,140,0.765452887083384991839807298675,2,0.200000000000000011102230246252
337,337_0,COMPLETED,BoTorch,0.262815703925981525301835972641,120,1.000000000000000000000000000000,4,0.485506943096034204732092121048
338,338_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.352272778737810432492238987834
339,339_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.100000000000000005551115123126,4,0.800000000000000044408920985006
340,340_0,COMPLETED,BoTorch,0.282070517629407380155726059456,129,0.478862953149110204265070933616,3,0.502821308996568738791665964527
341,341_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.346535899222528864793702041425
342,342_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,4,0.430498586441823039017151586449
343,343_0,COMPLETED,BoTorch,0.339584896224055965419097447011,331,0.209402445890009392126529519373,4,0.560447153076529591686494313763
344,344_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.716158284940703993015631567687,2,0.606057643034333848319761273160
345,345_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.371618402578465345520442042471
346,346_0,COMPLETED,BoTorch,0.291322830707676949657525256043,139,0.315122530730716887692466343651,4,0.635071163932410254204796729027
347,347_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,2,0.800000000000000044408920985006
348,348_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.374034753958239818416586786043
349,349_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.369309990871746651741602818220
350,350_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.370351275521700162851601589864
351,351_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.554966917646870117053481408220,2,0.800000000000000044408920985006
352,352_0,COMPLETED,BoTorch,0.285071267816954243556892834022,118,1.000000000000000000000000000000,4,0.449437308160192561246049081092
353,353_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.376847657684939441047333730239
354,354_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.344644762870198628768036996917
355,355_0,COMPLETED,BoTorch,0.304076019004751230134786510462,162,0.507606027169142048904859620961,4,0.404042353785717178737968424684
356,356_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.360501790394709198395162275119
357,357_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.363228489774211071861031996377
358,358_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.361225729561333919193089059263
359,359_0,COMPLETED,BoTorch,0.279319829957489385030555695266,115,0.844782890543848585807040763029,3,0.581375512015207762672730495979
360,360_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.234476123489465365645756378399,4,0.600745109895956863610422260535
361,361_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.357369846007610902915985207073
362,362_0,COMPLETED,BoTorch,0.332333083270817675192176920973,223,1.000000000000000000000000000000,3,0.200000000000000011102230246252
363,363_0,COMPLETED,BoTorch,0.268317079269817404529874238506,119,1.000000000000000000000000000000,4,0.785992122381078406334609098849
364,364_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.368663830136781611734875241382
365,365_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.358143044386928122158053611201
366,366_0,COMPLETED,BoTorch,0.276319079769942521629388920701,102,0.999412330351672428818687876628,4,0.796259607817824344166979244619
367,367_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.692187289041971243186424089799,4,0.745294659097976985862032961450
368,368_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.385393438797286824737398092111
369,369_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.401676546353689767343553285173,2,0.800000000000000044408920985006
370,370_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,1.000000000000000000000000000000,4,0.595840412660513463194433825265
371,371_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.363536754074063106489944630084
372,372_0,COMPLETED,BoTorch,0.332333083270817675192176920973,223,0.817007565231207655287448687886,2,0.546458496057858011951680055063
373,373_0,COMPLETED,BoTorch,0.376094023505876506874301412608,486,0.565207275332108682874832084053,2,0.348849819379121517393116391759
374,374_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.681544464593317522727033974661,3,0.800000000000000044408920985006
375,375_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.339979950372799821778357909352,3,0.377962472556345130403343546277
376,376_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.900688072709770803925266591250,3,0.800000000000000044408920985006
377,377_0,COMPLETED,BoTorch,0.258314578644661119177783348277,106,0.100000000000000005551115123126,4,0.800000000000000044408920985006
378,378_0,COMPLETED,BoTorch,0.279319829957489385030555695266,115,0.847671920698207514988098409958,3,0.578555377007153648349913055426
379,379_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.602471887758031732218455545080,4,0.800000000000000044408920985006
380,380_0,COMPLETED,BoTorch,0.294573643410852681334688440984,134,0.100000000000000005551115123126,2,0.407307649295201579242586831242
381,381_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.741205869379818471642806798627,3,0.723191571566681234983775539149
382,382_0,COMPLETED,BoTorch,0.283070767691922964282014163473,132,0.745361885044307892478343546827,2,0.276873424877870089044762380581
383,383_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.367332739031138222340899801566
384,384_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.309881409297940391134318360855,3,0.582957850126455179307072285155
385,385_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.788195185885804838754609136231,2,0.792782077678210850280038357596
386,386_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.233516769036349575161537472923,4,0.433904767223168397194399403816
387,387_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.273446907489193702378571515510,4,0.447216803531953033257195784245
388,388_0,COMPLETED,BoTorch,0.278819704926231537456260412000,119,0.881905539666244964180918941565,3,0.231444937465992428560213056699
389,389_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.322995936548953088696123359114,3,0.617731555312260538492807881994
390,390_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,2,0.800000000000000044408920985006
391,391_0,COMPLETED,BoTorch,0.301575393848462103285612556647,212,0.474508074072854113545361087745,3,0.200000000000000011102230246252
392,392_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.473775389618769748878435166262,4,0.497592641856966555469199420259
393,393_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.100000000000000005551115123126,4,0.498294822576290263871356955860
394,394_0,COMPLETED,BoTorch,0.333083270817704391042468614614,246,0.763990211995244417053640972881,3,0.464790229862514447933818928504
395,395_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.100000000000000005551115123126,4,0.642735395803077036447348291404
396,396_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,1.000000000000000000000000000000,2,0.732032297896095807132610389090
397,397_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.249511525336271744457405930007,3,0.515769946450997607279020940041
398,398_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.345175355326192812022156886087,3,0.511452762064493748894733471388
399,399_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.309940546678504569300116600061,3,0.583056621668500030253312615969
400,400_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.900647955736050165320705218619,2,0.533016918716322951610209202045
401,401_0,COMPLETED,BoTorch,0.350587646911727945919778903772,252,1.000000000000000000000000000000,2,0.350062635330719129633791908418
402,402_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.114222794506172398154575375884,4,0.800000000000000044408920985006
403,403_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.376420100982620020602098520612
404,404_0,COMPLETED,BoTorch,0.265066266566641672852711053565,121,1.000000000000000000000000000000,3,0.393866325500411451621118885669
405,405_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.269330672302867668577164295129,3,0.479119717206920814334125680034
406,406_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.360775336142017399865267179848,3,0.494284164839690343118405735368
407,407_0,COMPLETED,BoTorch,0.262815703925981525301835972641,121,0.618871115183078357446788686502,4,0.433219056023665616272921852214
408,408_0,COMPLETED,BoTorch,0.299324831207801955734737475723,137,0.797342654691236107922236442391,3,0.289102204846215293798650236567
409,409_0,COMPLETED,BoTorch,0.275568892223055805779097227060,136,0.460933739731902436531640887551,3,0.419087163023928566119025163061
410,410_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.765105918252003469604005658766
411,411_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.829661718866093855773158338707,2,0.521759694545806729237824583834
412,412_0,COMPLETED,BoTorch,0.317329332333083247164040585631,157,1.000000000000000000000000000000,4,0.470093843759098573009680421819
413,413_0,COMPLETED,BoTorch,0.306326581645411377685661591386,115,0.100000000000000005551115123126,3,0.653224943427757720471049651678
414,414_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.100000000000000005551115123126,4,0.514317761719366828288002579939
415,415_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.597682227022087619872081631911,3,0.443253394888896146142087673070
416,416_0,COMPLETED,BoTorch,0.280320080020004969156843799283,133,1.000000000000000000000000000000,3,0.800000000000000044408920985006
417,417_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.781656202165017077732045436278
418,418_0,COMPLETED,BoTorch,0.304826206551637945985078204103,120,0.267363183558668393580148858746,3,0.200000000000000011102230246252
419,419_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.200000000000000011102230246252
420,420_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,0.100000000000000005551115123126,4,0.684037583846488272953934028919
421,421_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.368874973109499126877608432551,4,0.514890663017425698200213446398
422,422_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,0.100000000000000005551115123126,4,0.793283852038364267755810033123
423,423_0,COMPLETED,BoTorch,0.294323580895223813058692030609,126,0.100000000000000005551115123126,4,0.416834078572020094721750638200
424,424_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.412713681606371785015596742596,4,0.645409703743409113307905045076
425,425_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,1.000000000000000000000000000000,2,0.800000000000000044408920985006
426,426_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,0.596828359893246229717078676913,3,0.325006645601174148918488526760
427,427_0,COMPLETED,BoTorch,0.279819954988747232604850978532,108,1.000000000000000000000000000000,2,0.800000000000000044408920985006
428,428_0,COMPLETED,BoTorch,0.275568892223055805779097227060,117,0.100000000000000005551115123126,4,0.598348067664245308883153029456
429,429_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.362630840739596504995745362976
430,430_0,COMPLETED,BoTorch,0.286571642910727675257476221304,126,1.000000000000000000000000000000,4,0.535386705373522064910218887235
431,431_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,0.674980728529784967939519901847,3,0.760242206616716931222299535875
432,432_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.682069310646121307328826333105
433,433_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.799708994208384593704863618768
434,434_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,3,0.566121064041541721678640897153
435,435_0,COMPLETED,BoTorch,0.291822955738934686209518076794,127,0.401258545223425744374878831877,3,0.463507586543266458800616192093
436,436_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.344612226901362062037037503615
437,437_0,COMPLETED,BoTorch,0.277069267316829237479680614342,131,0.548063956739078705915346745314,2,0.607525843639822982211740054481
438,438_0,COMPLETED,BoTorch,0.294573643410852681334688440984,134,0.435162907096169004894647969195,2,0.691331958695589499086509022163
439,439_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,0.103066223643821175404156065269,3,0.576769925107146197440499690856
440,440_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.347814359324130850659173574968
441,441_0,COMPLETED,BoTorch,0.330582645661415375215597123315,198,0.100000000000000005551115123126,2,0.200000000000000011102230246252
442,442_0,COMPLETED,BoTorch,0.302075518879719950859907839913,143,0.518158665508963167667388916016,4,0.353360534457829111865123650205
443,443_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.835648919229832753963194136304,4,0.439628729300919895983668084227
444,444_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.348840737596048111601731989140
445,445_0,COMPLETED,BoTorch,0.309327331832958241086828365951,130,1.000000000000000000000000000000,2,0.421097117361204809071750787552
446,446_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.337650229893375564138580102735
447,447_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.800000000000000044408920985006
448,448_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,2,0.369217183929255732266483391868
449,449_0,COMPLETED,BoTorch,0.283320830207551832558010573848,113,0.100000000000000005551115123126,4,0.800000000000000044408920985006
450,450_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.800000000000000044408920985006
451,451_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.351298373850245537752812197141
452,452_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.346770191542974948184507866245
453,453_0,COMPLETED,BoTorch,0.344836209052263087393441765016,175,0.195865026358155280838957423839,2,0.294328435073661909271436343261
454,454_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,1.000000000000000000000000000000,3,0.627203350421499772338052025589
455,447_0,COMPLETED,BoTorch,0.269567391847961967954461215413,112,0.100000000000000005551115123126,4,0.800000000000000044408920985006
456,456_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.363792966486353452904012328872
457,457_0,COMPLETED,BoTorch,0.321580395098774673989794337103,125,1.000000000000000000000000000000,2,0.800000000000000044408920985006
458,458_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,1.000000000000000000000000000000,2,0.454075564295686406879326568742
459,459_0,COMPLETED,BoTorch,0.307326831707926961811949695402,100,0.105112557331285758066563573720,2,0.200000000000000011102230246252
460,450_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.800000000000000044408920985006
461,461_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.328192171021130629782192045241
462,462_0,COMPLETED,BoTorch,0.299324831207801955734737475723,143,1.000000000000000000000000000000,2,0.360026572536664701829067780636
463,463_0,COMPLETED,BoTorch,0.276319079769942521629388920701,122,0.310133536304270984729214433173,3,0.800000000000000044408920985006
464,464_0,COMPLETED,BoTorch,0.306326581645411377685661591386,115,0.245113547523504698988361383272,4,0.800000000000000044408920985006
465,465_0,COMPLETED,BoTorch,0.282070517629407380155726059456,113,1.000000000000000000000000000000,4,0.350583923934616237261252535973
466,466_0,COMPLETED,BoTorch,0.387596899224806223926975690119,593,0.811309632570055172529066567222,2,0.502288805221354328622851426189
467,467_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.195019309195396811640321743653,3,0.694549642324797988912621349300
468,450_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.800000000000000044408920985006
469,469_0,COMPLETED,BoTorch,0.281570392598149532581430776190,124,1.000000000000000000000000000000,4,0.800000000000000044408920985006
470,470_0,COMPLETED,BoTorch,0.262815703925981525301835972641,120,1.000000000000000000000000000000,4,0.320793779495005870749935183994
471,471_0,COMPLETED,BoTorch,0.317829457364341094738335868897,110,0.100000000000000005551115123126,4,0.800000000000000044408920985006
472,472_0,COMPLETED,BoTorch,0.276069017254313542331090047810,118,1.000000000000000000000000000000,4,0.800000000000000044408920985006
473,473_0,COMPLETED,BoTorch,0.299324831207801955734737475723,143,0.582452914700959611948860583652,2,0.207011162429791506500720288386
474,474_0,COMPLETED,BoTorch,0.275818954738684674055093637435,137,0.895267730174122866593222624942,3,0.717456889319511015301600309613
475,475_0,COMPLETED,BoTorch,0.272068017004251094803635169228,123,0.683826575239120493243660803273,4,0.523360732246596183081521758140
476,476_0,COMPLETED,BoTorch,0.324081020255063800838968290918,145,0.426354446175510437555544740462,2,0.575735287858712307951236653025
477,477_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,1.000000000000000000000000000000,4,0.352019570094081246658390682569
478,478_0,COMPLETED,BoTorch,0.283070767691922964282014163473,119,0.549356629389723960521507706289,4,0.428206400903140937952429112556
479,479_0,COMPLETED,BoTorch,0.356089022255563936170119632152,306,0.626011414669169652391644831368,3,0.370375719492277322153483964939
480,480_0,COMPLETED,BoTorch,0.267066766691672952127589724114,116,1.000000000000000000000000000000,4,0.800000000000000044408920985006
481,481_0,COMPLETED,BoTorch,0.260065016254063530176665608451,120,0.853789541980178645630417122447,4,0.500896547605649833379004576273
482,482_0,COMPLETED,BoTorch,0.273318329582395547205919683620,111,0.100000000000000005551115123126,4,0.783875327672402200107626413228
483,483_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.362977612960554529841772364307
484,484_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.351800858328863597090929715705
485,485_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,4,0.534979366467124295425605851051
486,486_0,COMPLETED,BoTorch,0.277069267316829237479680614342,114,0.100000000000000005551115123126,4,0.668676281584206666508407579386
487,487_0,COMPLETED,BoTorch,0.265816454113528388703002747206,121,0.711045909478306592532703689358,3,0.440006468383705884317436130004
488,488_0,COMPLETED,BoTorch,0.309327331832958241086828365951,186,0.591177975322017990045253554854,4,0.400213050707425299634678594884
489,489_0,COMPLETED,BoTorch,0.268567141785446383828173111397,121,1.000000000000000000000000000000,3,0.713697892252195265072600705025
490,490_0,COMPLETED,BoTorch,0.269817454363590947252760088304,116,0.895266105785531007832389605028,4,0.623191229394803203334163299587
491,491_0,COMPLETED,BoTorch,0.282570642660665116707718880207,128,0.650002808351968885958171995298,3,0.303300221835209216081352678884
492,492_0,COMPLETED,BoTorch,0.284821205301325375280896423646,114,1.000000000000000000000000000000,4,0.340330137707803959123964432365
493,493_0,COMPLETED,BoTorch,0.337084271067766949592225955712,247,0.439780210046635033904749434441,2,0.304351866098853451880756892933
494,494_0,COMPLETED,BoTorch,0.320080020005001242289210949821,105,0.541925894851806977392527642223,4,0.467258981891562008570417674491
495,495_0,COMPLETED,BoTorch,0.255313828457114255776616573712,106,0.498880026271479537491870814847,3,0.508476001062399429741844869568
496,496_0,COMPLETED,BoTorch,0.250562640660165092398870001489,104,0.999993602368809408886818346218,4,0.613847061085561973570179361559
497,497_0,COMPLETED,BoTorch,0.296824206051512828885563521908,105,0.976699980754624719203604854556,2,0.200000000000000011102230246252
498,498_0,COMPLETED,BoTorch,0.252313078269567392375449799147,103,0.989350662877010900153607053653,4,0.351727088174433655254347286245
499,499_0,COMPLETED,BoTorch,0.297074268567141808183862394799,105,0.654461119449933015346232423326,4,0.200000000000000011102230246252
500,500_0,COMPLETED,BoTorch,0.256814203550887687477199960995,106,0.646246910857836809327636728995,4,0.798045727530231507884650454798
501,501_0,RUNNING,BoTorch,,105,0.662420271798276005803529642435,3,0.681309910502036042423412709468
502,502_0,RUNNING,BoTorch,,107,0.270257330926630179313008284225,2,0.678248232407570594837409316824
503,503_0,RUNNING,BoTorch,,104,0.548590630558805858463244931045,4,0.394919371316218792422603200976
504,504_0,RUNNING,BoTorch,,103,1.000000000000000000000000000000,2,0.249385079050372993059170312335
505,505_0,RUNNING,BoTorch,,106,0.105416195382791275103606665198,4,0.381102809968718814204180489469
506,506_0,RUNNING,BoTorch,,104,1.000000000000000000000000000000,2,0.486254401124945612178152032357
</pre>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> 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 »cpu_ram_usage.csv« as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727455189,473.26171875,49.7
1727455189,473.26953125,50.0
1727455189,473.3515625,49.6
1727455189,473.3515625,56.5
1727455189,473.3515625,40.6
1727455189,473.3515625,49.7
1727455189,473.3515625,59.6
1727455235,478.265625,49.8
1727455235,478.265625,55.6
1727455235,478.265625,49.5
1727455235,478.265625,57.8
1727455236,478.265625,49.7
1727455236,478.265625,56.3
1727455236,478.265625,48.1
1727455236,478.265625,55.6
1727455238,478.265625,49.8
1727455238,478.265625,46.3
1727455238,478.265625,51.8
1727455238,478.265625,39.4
1727455239,478.265625,49.8
1727455239,478.265625,50.0
1727455239,478.265625,50.0
1727455239,478.265625,40.6
1727455240,478.265625,49.8
1727455240,478.265625,38.2
1727455240,478.265625,52.9
1727455240,478.265625,40.6
1727455242,478.265625,49.7
1727455242,478.265625,54.3
1727455242,478.265625,50.0
1727455242,478.265625,55.6
1727455243,478.265625,49.8
1727455243,478.265625,38.2
1727455243,478.265625,48.6
1727455243,478.265625,55.8
1727455245,478.265625,49.8
1727455245,478.265625,37.8
1727455245,478.265625,54.2
1727455245,478.265625,40.6
1727455246,478.265625,49.8
1727455246,478.265625,38.2
1727455246,478.265625,52.9
1727455246,478.265625,38.2
1727455248,478.265625,49.8
1727455248,478.265625,40.5
1727455248,478.265625,52.6
1727455248,478.265625,40.0
1727455249,478.265625,49.8
1727455249,478.265625,52.1
1727455249,478.265625,49.5
1727455249,478.265625,38.7
1727455251,478.265625,49.7
1727455251,478.265625,53.2
1727455251,478.265625,49.5
1727455251,478.265625,36.4
1727455253,479.4140625,49.9
1727455253,479.4140625,53.1
1727455253,479.4140625,47.6
1727455253,479.4140625,57.8
1727455256,479.4140625,49.9
1727455256,479.4140625,55.3
1727455256,479.4140625,47.7
1727455256,479.4140625,53.2
1727455258,479.4140625,49.9
1727455258,479.4140625,56.3
1727455258,479.4140625,48.1
1727455258,479.4140625,55.6
1727455261,479.515625,49.9
1727455261,479.515625,54.3
1727455261,479.515625,48.6
1727455261,479.515625,56.8
1727455263,479.515625,49.9
1727455263,479.515625,55.3
1727455263,479.515625,48.5
1727455263,479.515625,42.4
1727455265,479.515625,49.9
1727455265,479.515625,55.6
1727455265,479.515625,48.6
1727455265,479.515625,55.8
1727455268,479.51953125,49.8
1727455268,479.51953125,56.5
1727455268,479.51953125,49.6
1727455268,479.51953125,50.0
1727455270,479.51953125,49.9
1727455270,479.51953125,48.7
1727455270,479.51953125,52.8
1727455270,479.51953125,38.2
1727455272,479.51953125,49.9
1727455272,479.51953125,54.2
1727455272,479.51953125,47.7
1727455272,479.51953125,57.8
1727455275,479.51953125,49.9
1727455275,479.51953125,36.4
1727455275,479.51953125,51.6
1727455275,479.51953125,55.6
1727455277,479.51953125,49.9
1727455277,479.51953125,53.3
1727455277,479.51953125,48.6
1727455277,479.51953125,53.3
1727455279,479.51953125,49.9
1727455279,479.51953125,54.3
1727455279,479.51953125,48.6
1727455279,479.51953125,40.6
1727455281,479.51953125,49.9
1727455281,479.51953125,56.2
1727455281,479.51953125,47.7
1727455281,479.51953125,56.5
1727455284,479.51953125,49.9
1727455284,479.51953125,37.5
1727455284,479.51953125,49.1
1727455284,479.51953125,56.8
1727455286,479.51953125,49.9
1727455286,479.51953125,55.3
1727455286,479.51953125,47.7
1727455286,479.51953125,55.6
1727455288,479.51953125,49.9
1727455288,479.51953125,36.4
1727455288,479.51953125,52.5
1727455288,479.51953125,39.4
1727455291,479.62109375,49.9
1727455291,479.62109375,54.3
1727455291,479.62109375,48.6
1727455291,479.62109375,54.8
1727455293,479.62109375,49.9
1727455293,479.62109375,45.9
1727455293,479.62109375,52.2
1727455293,479.62109375,38.7
1727455296,479.62109375,49.9
1727455296,479.62109375,55.1
1727455296,479.62109375,46.3
1727455296,479.62109375,56.5
1727455298,479.62109375,49.9
1727455298,479.62109375,52.2
1727455298,479.62109375,48.6
1727455298,479.62109375,48.7
1727455302,479.7109375,49.9
1727455302,479.73046875,54.3
1727455302,479.73046875,50.8
1727455302,479.73046875,42.4
1727455305,481.73046875,49.9
1727455305,481.73046875,40.0
1727455305,481.73046875,50.5
1727455305,481.73046875,55.6
1727455308,481.75390625,49.9
1727455308,481.75390625,54.3
1727455308,481.75390625,52.0
1727455308,481.75390625,40.6
1727455311,481.75390625,49.9
1727455311,481.75390625,55.6
1727455311,481.75390625,51.6
1727455311,481.75390625,39.4
1727455313,481.8046875,49.9
1727455313,481.8046875,40.0
1727455313,481.8046875,50.8
1727455313,481.8046875,57.8
1727455315,481.8046875,49.8
1727455315,481.8046875,52.3
1727455315,481.8046875,46.9
1727455315,481.8046875,56.8
1727455318,481.8046875,49.9
1727455318,481.8046875,38.2
1727455318,481.8046875,50.8
1727455318,481.8046875,56.5
1727455320,481.8046875,49.9
1727455320,481.8046875,55.6
1727455320,481.8046875,48.5
1727455320,481.8046875,59.6
1727455322,481.80859375,49.9
1727455322,481.80859375,54.3
1727455322,481.80859375,50.0
1727455322,481.80859375,48.6
1727455324,481.80859375,49.9
1727455324,481.80859375,47.5
1727455324,481.80859375,50.8
1727455324,481.80859375,56.8
1727455326,481.80859375,49.8
1727455326,481.80859375,53.2
1727455326,481.80859375,52.0
1727455326,481.80859375,38.2
1727455489,521.36328125,50.2
1727455489,521.36328125,55.3
1727455489,521.36328125,48.9
1727455489,521.36328125,56.5
1727455570,531.8046875,50.2
1727455570,531.8046875,56.5
1727455570,531.8046875,48.1
1727455570,531.8046875,50.0
1727455733,526.1171875,50.3
1727455733,526.1171875,55.3
1727455733,526.1171875,47.4
1727455733,526.1171875,55.6
1727455884,535.23046875,50.3
1727455884,535.23046875,56.5
1727455884,535.23046875,49.7
1727455884,535.23046875,58.7
1727456026,535.76953125,50.2
1727456026,535.76953125,36.1
1727456026,535.76953125,52.9
1727456026,535.76953125,38.7
1727456201,536.51953125,50.2
1727456201,536.51953125,55.6
1727456201,536.51953125,49.4
1727456201,536.51953125,43.8
1727456395,544.7265625,50.2
1727456395,544.7265625,55.6
1727456395,544.7265625,47.6
1727456395,544.7265625,55.6
1727456641,534.53125,50.2
1727456641,534.53125,54.3
1727456641,534.53125,50.6
1727456641,534.53125,42.4
1727456903,543.08203125,50.2
1727456903,543.08203125,37.1
1727456903,543.08203125,51.8
1727456903,543.08203125,39.4
1727457178,540.0,50.2
1727457178,540.0,55.3
1727457178,540.0,48.6
1727457178,540.0,57.4
1727457419,540.53125,50.2
1727457419,540.53125,53.2
1727457419,540.53125,51.8
1727457419,540.53125,40.6
1727457697,553.640625,50.2
1727457697,553.640625,54.3
1727457697,553.640625,50.2
1727457697,553.640625,44.4
1727458041,553.828125,50.2
1727458041,553.828125,54.3
1727458041,553.828125,50.2
1727458041,553.828125,39.4
1727458350,561.453125,50.2
1727458350,561.453125,54.2
1727458350,561.453125,50.2
1727458350,561.453125,44.7
1727458684,550.328125,50.2
1727458684,550.328125,41.7
1727458684,550.328125,51.1
1727458684,550.328125,39.4
1727458959,553.76953125,50.1
1727458959,553.76953125,44.6
1727458959,553.76953125,51.8
1727458959,553.76953125,40.0
1727459399,475.4140625,50.2
1727459399,475.4140625,42.4
1727459399,475.4140625,50.2
1727459399,475.4140625,56.8
1727459872,472.6640625,50.2
1727459872,472.6640625,56.2
1727459872,472.6640625,49.6
1727459872,472.6640625,56.8
1727460341,458.72265625,50.2
1727460341,458.72265625,52.3
1727460341,458.72265625,49.2
1727460341,458.72265625,50.0
1727460866,458.140625,50.2
1727460866,458.140625,44.7
1727460866,458.140625,51.0
1727460866,458.140625,39.4
1727461431,483.47265625,50.2
1727461431,483.47265625,39.4
1727461431,483.47265625,51.2
1727461431,483.47265625,48.8
1727461985,435.86328125,50.2
1727461985,435.86328125,56.5
1727461985,435.86328125,50.0
1727461985,435.86328125,38.7
1727462659,457.79296875,50.2
1727462659,457.79296875,55.3
1727462660,457.79296875,50.7
1727462660,457.79296875,37.5
1727463237,457.31640625,50.2
1727463237,457.31640625,35.3
1727463237,457.31640625,50.7
1727463237,457.31640625,51.2
1727463776,489.68359375,50.2
1727463776,489.68359375,41.2
1727463776,489.68359375,50.8
1727463776,489.68359375,44.1
1727464384,466.87890625,50.2
1727464384,466.87890625,54.3
1727464384,466.87890625,50.2
1727464384,466.87890625,39.4
1727465144,454.48828125,50.2
1727465144,454.48828125,56.5
1727465144,454.48828125,49.8
1727465144,454.48828125,40.0
1727465759,473.703125,50.2
1727465759,473.703125,55.3
1727465759,473.703125,49.8
1727465759,473.703125,51.3
1727466758,466.34765625,50.3
1727466758,466.34765625,41.7
1727466758,466.34765625,50.5
1727466758,466.34765625,57.8
1727467492,468.046875,50.2
1727467492,468.046875,52.1
1727467492,468.046875,48.9
1727467492,468.046875,56.5
1727468428,517.84375,50.2
1727468428,517.84375,54.3
1727468428,517.84375,50.3
1727468428,517.84375,39.4
1727469378,510.33984375,50.2
1727469378,510.33984375,40.0
1727469378,510.33984375,51.4
1727469378,510.33984375,37.5
1727470639,486.7421875,50.3
1727470639,486.7421875,40.0
1727470639,486.7421875,50.9
1727470639,486.7421875,39.4
1727471650,488.2421875,50.2
1727471650,488.2421875,52.2
1727471650,488.2421875,48.6
1727471650,488.2421875,55.6
1727472845,478.22265625,50.2
1727472845,478.22265625,54.3
1727472845,478.22265625,49.4
1727472845,478.22265625,58.7
1727474239,497.078125,50.3
1727474239,497.078125,55.6
1727474239,497.078125,49.0
1727474239,497.078125,58.1
1727475410,526.44921875,50.2
1727475410,526.44921875,56.5
1727475410,526.44921875,50.4
1727475410,526.44921875,40.6
1727476352,533.0078125,50.2
1727476352,533.0078125,51.0
1727476384,533.03125,49.8
1727476384,533.03125,38.2
</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 »cpu_ram_usage.csv« as file</button>
<h1> Parallel Plot</h1>
<div class="invert_in_dark_mode" id="parallel-plot"></div>
<h1> Job Status Distribution</h1>
<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
<h1> Boxplots</h1>
<div class="invert_in_dark_mode" id="plotBoxplot"></div>
<h1> Violin</h1>
<div class="invert_in_dark_mode" id="plotViolin"></div>
<h1> Histogram</h1>
<div class="invert_in_dark_mode" id="plotHistogram"></div>
<h1> 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>
</body>
</html>