| n_reference_samples | recent_samples_proportion | threshold | result |
|---|---|---|---|
| 50 | 0.225887 | 0.683044 | 0.174044 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_reference_samples | range | 50 | 500 | int | |
| recent_samples_proport… | range | 0.1 | 1 | float | |
| threshold | range | 0.5 | 0.8 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 499 | 2 | 501 |
trial_index,arm_name,trial_status,generation_method,result,n_reference_samples,recent_samples_proportion,threshold 0,0_0,COMPLETED,Sobol,0.192298074518629680262904457777,51,0.846809870004653908459602007497,0.522989526391029357910156250000 1,1_0,COMPLETED,Sobol,0.441360340085021229938888609468,336,0.697653441224247217178344726562,0.788498062733560822756828656566 2,2_0,COMPLETED,Sobol,0.441360340085021229938888609468,446,0.684619253221899315420273524069,0.757331812940537973943833094381 3,3_0,COMPLETED,Sobol,0.255563890972743235074915446603,157,0.138318423554301267452970591876,0.514619175437837861331047406566 4,4_0,COMPLETED,Sobol,0.317829457364341094738335868897,387,0.149033119343221193142667857501,0.640954717807471707757827061869 5,5_0,COMPLETED,Sobol,0.337834458614653665442517649353,313,0.647527315840125128332260828756,0.621891652047634191369240852509 6,6_0,COMPLETED,Sobol,0.235808952238059532646730076522,112,0.988534315023571252822875976562,0.557409877702593758996840733744 7,7_0,COMPLETED,Sobol,0.441360340085021229938888609468,493,0.719745303224772259298447352194,0.785397396981716178210319867503 8,8_0,COMPLETED,Sobol,0.301825456364090971561608967022,308,0.820041014999151274267319422506,0.576165532972663596567031163431 9,9_0,COMPLETED,Sobol,0.331832958239559938640184100223,134,0.771533321961760498730598101247,0.726162222120910971767671071575 10,10_0,COMPLETED,Sobol,0.441360340085021229938888609468,475,0.485890550911426521984992632497,0.773015015386045001299919476878 11,11_0,COMPLETED,Sobol,0.354588647161790393447233782354,435,0.848481718543916962893547406566,0.618179913703352235110344281566 12,12_0,COMPLETED,Sobol,0.398849712428107072703653557255,328,0.808806919492781162261962890625,0.726745853200554847717285156250 13,13_0,COMPLETED,Sobol,0.311827956989247256913699857250,271,0.822741483151912644800063389994,0.588666524365544341357292523753 14,14_0,COMPLETED,Sobol,0.382845711427856949526926655381,302,0.369282615371048450469970703125,0.736300043482333466116074305319 15,15_0,COMPLETED,Sobol,0.221805451362840688744881845196,134,0.758107978198677256997939366556,0.526157554425299212041977625631 16,16_0,COMPLETED,Sobol,0.330582645661415375215597123315,491,0.113086053542792802639738170001,0.726659548096358798296989789378 17,17_0,COMPLETED,Sobol,0.270317579394848683804752909055,196,0.439315837062895342413071375631,0.660423051379620984491225499369 18,18_0,COMPLETED,Sobol,0.328332083020755227664722042391,141,0.698239339608699105532707562816,0.719711340405047028667695485638 19,19_0,COMPLETED,Sobol,0.398099524881220356853361863614,408,0.634777214843779802322387695312,0.687387890648096799850463867188 20,20_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.649786356525840935738358439266,0.500000000000000000000000000000 21,21_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.967273983933888747976936883788,0.500000000000000000000000000000 22,22_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.435905393963842424653876150842,0.584768165975881881735176648363 23,23_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.100000000000000005551115123126,0.642098335819103471777680169907 24,24_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.590082620744604380291775669320,0.561276017488033018842941146431 25,25_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.494705493128585027662325046549,0.500000000000000000000000000000 26,26_0,COMPLETED,BoTorch,0.182295573893473394910813567549,50,0.815217720624371033899535632372,0.500000000000000000000000000000 27,27_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.294542399565861701127289506985,0.614045227525323755735087161156 28,28_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.707174621587320806348486712523,0.545661934315960883701279726665 29,29_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.504910918217320259238078961062,0.547939206613857088257191207958 30,30_0,COMPLETED,BoTorch,0.197049262315578843640651030000,50,0.100000000000000005551115123126,0.716606955615633145484366650635 31,31_0,COMPLETED,BoTorch,0.328082020505126248366423169500,500,0.100000000000000005551115123126,0.500000000000000000000000000000 32,32_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.801424948740082965237263579183,0.500000000000000000000000000000 33,33_0,COMPLETED,BoTorch,0.211802950737684403392790954967,62,0.100000000000000005551115123126,0.639000975254085723520347528392 34,34_0,COMPLETED,BoTorch,0.306326581645411377685661591386,301,0.100000000000000005551115123126,0.505783679816611431157014067139 35,35_0,COMPLETED,BoTorch,0.184796199049762410737685058848,63,0.785221702540274901771510940307,0.511785914490039739455085054942 36,36_0,COMPLETED,BoTorch,0.234808702175543837498139509989,138,0.210203860445026535774815101831,0.568738770679881877612160678837 37,37_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.575103843008828441440982714994,0.607312939874673718421149715141 38,38_0,COMPLETED,BoTorch,0.181545386346586679060521873907,50,0.541560314479246240892962305225,0.516242201276765677597779813368 39,39_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.495617115233334626367422970361,0.500222122687436820953621463559 40,40_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.174731662884123561951810188475,0.581006283316551352946532915666 41,41_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.282660611517684179361253882234,0.653607132618334030205176077288 42,42_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.316438638771501912216876917228,0.539639514115436425001348652586 43,43_0,COMPLETED,BoTorch,0.313578394598649667912582117424,355,0.285578737668460647114443418104,0.500000000000000000000000000000 44,44_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.100000000000000005551115123126,0.589501410438319384255123623007 45,45_0,COMPLETED,BoTorch,0.293573393348337097208400336967,330,0.115898434034316419327659275496,0.558868198800806847970079616061 46,46_0,COMPLETED,BoTorch,0.360590147536884231271869794000,500,1.000000000000000000000000000000,0.500000000000000000000000000000 47,47_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.254448464455158951391666732889,0.583638385686529925777676908183 48,48_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.270752924709132480884932192566,0.671179546915459779299339970748 49,49_0,COMPLETED,BoTorch,0.197299324831207822938949902891,50,0.100000000000000005551115123126,0.500000000000000000000000000000 50,50_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.362489349568308250049142316129,0.650826029095480262931516790559 51,51_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.294526477927624596997446815294,0.614044591533120964754743908998 52,52_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.330246392031487645546405929053,0.672266837193724930976657105930 53,53_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.100000000000000005551115123126,0.800000000000000044408920985006 54,54_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.409993153499464413336283996614,0.622525419524639356261275224824 55,55_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.313393335804594941329526136542,0.655736719754156149875257142412 56,56_0,COMPLETED,BoTorch,0.209052263065766408267620590777,77,0.132833659418543414965085958102,0.688763510625863717429240296042 57,57_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.725241134597713665854712417058,0.500000000000000000000000000000 58,58_0,COMPLETED,BoTorch,0.347836959239809950794608539582,421,0.999856461173234789541197642393,0.586713459805923154277706998982 59,59_0,COMPLETED,BoTorch,0.372843210802700664174835765152,401,0.804046850503410404087389906636,0.644692649652438887208916185045 60,60_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.333630155924918514465105090494,0.500000000000000000000000000000 61,61_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.296049607202518716420058808581,0.725711602497758900831570372247 62,62_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.337491190206078317537219390942,0.800000000000000044408920985006 63,63_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.398648480884710276761495606479,0.500000000000000000000000000000 64,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 65,65_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.272753877289133916939078972064,0.716172813772965710654716531280 66,66_0,COMPLETED,BoTorch,0.208552138034508671715627770027,58,0.684956001252192248074379676837,0.753771331510694309052666994830 67,67_0,COMPLETED,BoTorch,0.204801200300074981441866839305,51,0.611190772243851543343851062673,0.800000000000000044408920985006 68,68_0,COMPLETED,BoTorch,0.209552388097024255841915874043,53,0.100000000000000005551115123126,0.712665582702354361011032324313 69,69_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.285611669906581822075963827956,0.500000000000000000000000000000 70,70_0,COMPLETED,BoTorch,0.198549637409352386363536879799,50,0.226324615646654264677906098768,0.800000000000000044408920985006 71,71_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.217602826226807044562860937731,0.761623078839635869741186979809 72,72_0,COMPLETED,BoTorch,0.198549637409352386363536879799,50,0.230291653881715491225534719888,0.800000000000000044408920985006 73,73_0,COMPLETED,BoTorch,0.197049262315578843640651030000,50,0.215779663655551678935751169774,0.764173212611893637458138073271 74,74_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.236042605425510587657811356621,0.706741546248125684925867062702 75,75_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.243545967974960753110735822702,0.773452149388051513057007468888 76,76_0,COMPLETED,BoTorch,0.193548387096774243687491434684,50,0.331588944003657570824827871547,0.767481488311701376403561880579 77,77_0,COMPLETED,BoTorch,0.195548887221805411940067642718,50,0.238599870167312738677978245505,0.800000000000000044408920985006 78,78_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.576701430582529983581707710982,0.500000000000000000000000000000 79,79_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.870144445983915826303700669087,0.500000000000000000000000000000 80,80_0,COMPLETED,BoTorch,0.202550637659414833890991758381,50,0.219962278709577879753922502459,0.766475741776040386810109339422 81,81_0,COMPLETED,BoTorch,0.177544386096524120510764532810,50,0.260211064297423799729358506738,0.762475352532608785516288207873 82,82_0,COMPLETED,BoTorch,0.201550387596899249764703654364,50,0.457967922367365054547860836465,0.711599795342949703602641875477 83,83_0,COMPLETED,BoTorch,0.198049512378094538789241596533,78,0.606149050979434500519005268870,0.500000000000000000000000000000 84,84_0,COMPLETED,BoTorch,0.204051012753188265591575145663,73,0.840710867640089620778098833398,0.500000000000000000000000000000 85,85_0,COMPLETED,BoTorch,0.202800700175043813189290631271,50,1.000000000000000000000000000000,0.681179691348950955287477881939 86,86_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.524610066657929197120324715797,0.698874368841887250169975231984 87,87_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,1.000000000000000000000000000000,0.617969924669936387928714793816 88,88_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.809359404243581326277023890725,0.641141421489074714301636959135 89,89_0,COMPLETED,BoTorch,0.254063515878969692352029596805,50,1.000000000000000000000000000000,0.800000000000000044408920985006 90,90_0,COMPLETED,BoTorch,0.203050762690672681465287041647,50,0.923694716617771471867115451460,0.673243607140706634694993226731 91,91_0,COMPLETED,BoTorch,0.200300075018754686340116677457,50,0.465445991908797096492378386756,0.731278173159580546780489385128 92,92_0,COMPLETED,BoTorch,0.243810952738184538723942296201,59,0.757918059831655055447185986850,0.800000000000000044408920985006 93,93_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.713830843353167376896806217701,0.681280106608185609395889059670 94,94_0,COMPLETED,BoTorch,0.234058514628657121647847816348,140,0.100000000000000005551115123126,0.800000000000000044408920985006 95,95_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,1.000000000000000000000000000000,0.561627855545467458142638861318 96,96_0,COMPLETED,BoTorch,0.195798949737434391238366515609,50,0.891580900315491975405279845290,0.571316739836011278086402853660 97,97_0,COMPLETED,BoTorch,0.213303325831457835093374342250,50,0.875228599059007961180611800955,0.739302933692255415110139438184 98,98_0,COMPLETED,BoTorch,0.259314828707176814326373914810,70,0.700491556185313557492122527037,0.794699577367580678455283305084 99,99_0,COMPLETED,BoTorch,0.405101275318829667781983516761,96,0.974941381947132312824066957546,0.800000000000000044408920985006 100,100_0,COMPLETED,BoTorch,0.280070017504376100880847388908,67,0.723327388008552341069901103765,0.796089845443683330472595116589 101,101_0,COMPLETED,BoTorch,0.234058514628657121647847816348,51,0.879162311821145681101086211129,0.740182600837908766244765956799 102,102_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.916069315051276422678938615718,0.555086118939344230760468690278 103,103_0,COMPLETED,BoTorch,0.214803700925231266793957729533,51,0.875751637377343628010351039848,0.669302163177195597043578345620 104,104_0,COMPLETED,BoTorch,0.236059014753688400922726486897,50,0.994946241597996272609805146203,0.680189613791603031600629947206 105,105_0,COMPLETED,BoTorch,0.260815203800950246026957302092,151,0.100000000000000005551115123126,0.788292097117884615897764888359 106,106_0,COMPLETED,BoTorch,0.208552138034508671715627770027,50,0.999220421546680337421264539444,0.679667907432342288664983698254 107,107_0,COMPLETED,BoTorch,0.339334833708427097143101036636,91,0.956664940423261112023567420692,0.800000000000000044408920985006 108,108_0,COMPLETED,BoTorch,0.209802450612653124117912284419,50,0.922414646260579673686663682020,0.672635419184068439335533184931 109,109_0,COMPLETED,BoTorch,0.251062765691422828950862822239,183,1.000000000000000000000000000000,0.500000000000000000000000000000 110,110_0,COMPLETED,BoTorch,0.191547886971742964412612764136,74,0.312709664582845769942309743783,0.500000000000000000000000000000 111,111_0,COMPLETED,BoTorch,0.182295573893473394910813567549,69,0.339919454351074790121600699422,0.530240770350901335916660173098 112,112_0,COMPLETED,BoTorch,0.259564891222805682602370325185,189,0.768163996399901627398776327027,0.500000000000000000000000000000 113,113_0,COMPLETED,BoTorch,0.202800700175043813189290631271,83,0.411989884720331267509152439743,0.500000000000000000000000000000 114,114_0,COMPLETED,BoTorch,0.212803200800200098541381521500,90,0.447712762012801301914066698373,0.543391548752985786840952187049 115,115_0,COMPLETED,BoTorch,0.223305826456614120445465232478,101,0.761723982952132727675120804633,0.536140099639785594476393271179 116,116_0,COMPLETED,BoTorch,0.258064516129032250901786937902,160,0.722345897600692232742858323036,0.578349589953481735271623165318 117,117_0,COMPLETED,BoTorch,0.218804701175293825343715070630,119,0.658747765200158541532005074259,0.559590920872141350805861748086 118,118_0,COMPLETED,BoTorch,0.267316829207301820403586134489,168,1.000000000000000000000000000000,0.500000000000000000000000000000 119,119_0,COMPLETED,BoTorch,0.314078519629907515486877400690,290,0.829749525448565838914305459184,0.628510260044411683821863334742 120,120_0,COMPLETED,BoTorch,0.224556139034758683870052209386,133,0.791383059858784876361426086078,0.522311303899349654855654989660 121,121_0,COMPLETED,BoTorch,0.220055013753438388768302047538,136,0.767446790828263836203859682428,0.526950025026618695811464476719 122,122_0,COMPLETED,BoTorch,0.221805451362840688744881845196,129,0.863622893696706528388062906743,0.500000000000000000000000000000 123,123_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.444462596829201195269831714540,0.669562378535379920663217490073 124,124_0,COMPLETED,BoTorch,0.199799949987496838765821394190,50,0.375001470396787661698567717394,0.697921811022987670369843726803 125,125_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.532152815903539466724225803773 126,126_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.342489500538812718932746292921,0.562339307583924141731301915570 127,127_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.382471845681574440511951706867,0.533412953828685809654075455910 128,128_0,COMPLETED,BoTorch,0.197549387346836691214946313266,50,0.597031275612136091979209595593,0.645726178613784229654015689448 129,129_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.814038466345782585342760739877,0.554783035554184178472780786251 130,130_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.276577611581957982789248262634,0.527702402087004807107462056592 131,131_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.208628045236680687013475221647,0.500000000000000000000000000000 132,132_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.933584226740622469264963001478,0.516066932203674944013016556710 133,133_0,COMPLETED,BoTorch,0.196799199799949975364654619625,50,0.501592835173656603764413830504,0.660245471798773575500263177673 134,134_0,COMPLETED,BoTorch,0.208552138034508671715627770027,85,0.100000000000000005551115123126,0.500000000000000000000000000000 135,135_0,COMPLETED,BoTorch,0.198799699924981254639533290174,72,0.101309618180641367035654809570,0.500000000000000000000000000000 136,136_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.271486919042045471428536984604,0.543708178996996194243251920852 137,137_0,COMPLETED,BoTorch,0.201300325081270270466404781473,74,0.313174044153622710418005681277,0.500030092180918317446014498273 138,138_0,COMPLETED,BoTorch,0.181545386346586679060521873907,50,0.339529034587735145667153346949,0.586464119183446319638619570469 139,139_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.744981897558269934300767545210,0.589861972571526838038380446960 140,140_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.789967550691027420306511430681,0.500000000000000000000000000000 141,141_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.347210463303218530572991085137,0.525096890869235388699109989830 142,142_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.898407243415798584251774627774,0.500000000000000000000000000000 143,143_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.734552539388674374798426924826,0.525093338661215214280275631609 144,144_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.384193161747946043682588879165,0.732303288032380983452185319038 145,145_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,0.411452826072087329833948388114,0.737032918639650591607903606928 146,146_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.110318172301142908287019395175,0.549302848091453910228665336035 147,147_0,COMPLETED,BoTorch,0.289572393098274538658642995870,79,0.138377106355769219359075350440,0.500000000000000000000000000000 148,148_0,COMPLETED,BoTorch,0.217554388597149261919128093723,78,0.100000000000000005551115123126,0.800000000000000044408920985006 149,149_0,COMPLETED,BoTorch,0.255313828457114255776616573712,178,0.141901815922689661375599712301,0.766944282725105397346965219185 150,150_0,COMPLETED,BoTorch,0.184546136534133542461688648473,50,0.712381059947489569950107579643,0.559758928681191814114015414816 151,151_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.378016118008556722962509866193,0.600747959162706623992278309743 152,152_0,COMPLETED,BoTorch,0.233058264566141537521559712332,69,0.100000000000000005551115123126,0.548806625166556982797771979676 153,153_0,COMPLETED,BoTorch,0.194048512128031980239484255435,50,0.402299846505957070519343687920,0.551622774961467832177675063576 154,154_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.764587994581870766808151529403,0.500000000000000000000000000000 155,155_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.782609433422867817320423000638,0.562334009097488984885160334670 156,156_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.652692026049437323820257006446,0.614145673665651758987849007099 157,157_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.444117982232666630437734056613,0.500000000000000000000000000000 158,158_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.638763401423660392985937050980,0.540469899076802429149779527506 159,159_0,COMPLETED,BoTorch,0.204801200300074981441866839305,52,0.123156734460232578087790500376,0.769124391219168535016592613829 160,160_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.515399447155842627132926736522,0.630712249022509885421072794998 161,161_0,COMPLETED,BoTorch,0.271567891972993247229339885962,189,0.136239360535459652634671101623,0.759672294594316177551718283212 162,162_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.443982712270488910633048362797,0.556179231386568884154542047327 163,163_0,COMPLETED,BoTorch,0.193548387096774243687491434684,51,0.371281352516638074590105134121,0.753067925537570070915194264671 164,164_0,COMPLETED,BoTorch,0.231557889472368105820976325049,50,0.725470301804213790752839940978,0.800000000000000044408920985006 165,165_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.473619526029325665916758225649,0.568916698694828193438866037468 166,166_0,COMPLETED,BoTorch,0.183795948987246826611396954831,54,0.153475527832018704410543818994,0.759880605026953093172892295115 167,167_0,COMPLETED,BoTorch,0.193548387096774243687491434684,69,0.942643659473980544127869052318,0.500000000000000000000000000000 168,168_0,COMPLETED,BoTorch,0.186796699174793690012563729397,62,0.565549452604356295282173050509,0.513594987992307627777677225822 169,169_0,RUNNING,BoTorch,,50,0.854675063355610387105798508856,0.535227395441616793370087634685 170,170_0,COMPLETED,BoTorch,0.184546136534133542461688648473,50,0.450705734409799663175988371222,0.521758389460954763450217797072 171,171_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.538738242960678848092470616393,0.500000000000000000000000000000 172,172_0,COMPLETED,BoTorch,0.185046261565391390035983931739,50,0.447180085967830542870160570601,0.530597648941003585676412512839 173,173_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,0.365831722010560689284375257557,0.500000000000000000000000000000 174,174_0,COMPLETED,BoTorch,0.187296824206051537586859012663,69,0.785754237253416309982867460349,0.500000000000000000000000000000 175,175_0,COMPLETED,BoTorch,0.194548637159289827813779538701,50,0.787828499404044646503564308659,0.581829865957509184681839542463 176,176_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.718647515274157711040459162177,0.630063134111960909677918607485 177,177_0,COMPLETED,BoTorch,0.200550137534383554616113087832,57,0.773169248084723648162253084593,0.507871312495567162059728616441 178,178_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.545354099939708403255167468160 179,179_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.300806240200773900017594542078,0.568519538338803420707279201451 180,180_0,COMPLETED,BoTorch,0.182795698924731131462806388299,50,0.260717661945561229863699281850,0.610333764782839516271906177280 181,181_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.905915736546186844968531204358,0.538794370143921286242516544007 182,182_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.513352939950143416503181015287,0.572361886647337114730760276871 183,183_0,COMPLETED,BoTorch,0.195798949737434391238366515609,89,0.559657157485656586715094817919,0.500000000000000000000000000000 184,184_0,COMPLETED,BoTorch,0.184796199049762410737685058848,50,0.309698778801933527482503905048,0.580138437316689792311308337958 185,185_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.371997488283797750341364007909,0.525751137803067369880238857149 186,186_0,COMPLETED,BoTorch,0.216054013503375830218544706440,96,0.595096758457977492717816403456,0.500000000000000000000000000000 187,187_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.660829093507727227674308778660,0.523835460038788447434399131453 188,188_0,COMPLETED,BoTorch,0.176544136034008536384476428793,50,0.459858043667938587439891762187,0.533357833920752755219041318924 189,189_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.207010265383533287320005911170,0.645231071728858629477088015847 190,49_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.100000000000000005551115123126,0.500000000000000000000000000000 191,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000 192,192_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.209275044669737120273111941060,0.500020916525400038743498498661 193,193_0,RUNNING,BoTorch,,66,0.533548314493060504837274038437,0.500000000000000000000000000000 194,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 195,195_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.638944974967135093102399423515,0.593122413922630764560040006472 196,196_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.804641130474754273649296010262,0.534583185485693390681660730479 197,197_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.869252793407701118688635233411,0.528363696563006857154221052042 198,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000 199,199_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.522938953829648944804375787498 200,200_0,COMPLETED,BoTorch,0.199299824956239102213828573440,50,0.144601515659045998241083452740,0.687511703644821725589508787380 201,201_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.944350626967797146527061613597,0.535885857771406626248733573448 202,202_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.588625539313744172709164104162 203,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 204,204_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.349126220241388229847956381491,0.614787802214199596839705463935 205,205_0,COMPLETED,BoTorch,0.197049262315578843640651030000,60,1.000000000000000000000000000000,0.500000000000000000000000000000 206,206_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.932071692077272917487107406487,0.500000000000000000000000000000 207,207_0,COMPLETED,BoTorch,0.201800450112528118040700064739,70,0.370406397995742153739229252096,0.615334463479904281157928380708 208,208_0,COMPLETED,BoTorch,0.193048262065516396113196151418,62,0.567528113387534283162949577672,0.515292554221786991419662626868 209,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000 210,210_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.398097312292544525114124098764,0.535441114693999575813165847649 211,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000 212,212_0,COMPLETED,BoTorch,0.184796199049762410737685058848,63,0.787899502211990232503069364611,0.500000000000000000000000000000 213,213_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.322259501034363848859243262268,0.712605549247603664575478887855 214,214_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.597626392439718157056915970315,0.500000000000000000000000000000 215,215_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.935409427107838697956765372510,0.552094338693611352830714622542 216,216_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.410485744684218056832492038666,0.500000000000000000000000000000 217,217_0,COMPLETED,BoTorch,0.184796199049762410737685058848,50,0.730679799830542875405114955356,0.572106471424271734171895786858 218,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000 219,219_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.426000678805463661724672874698,0.647400715526813774758352337813 220,220_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.528513450921707983454211898788,0.707609356181271298424917404191 221,221_0,COMPLETED,BoTorch,0.228057014253563394845514267217,126,0.174748472473160831874849918677,0.555298130230690745179344958160 222,222_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.403894891117930199264662860514,0.569322727632765701599737440119 223,223_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.455198994688842617506452370435,0.608142242123440701639935923595 224,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 225,225_0,COMPLETED,BoTorch,0.209552388097024255841915874043,50,0.927292302712519389551459880749,0.583087565808178442949838427012 226,226_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.100000000000000005551115123126,0.531696611313915101781901739741 227,227_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,0.488326217606287471539872058202,0.731323166766490562196167957154 228,228_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.374637369352278093437291772716,0.623732302768671198478500627971 229,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000 230,230_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.544016875018127010754653838376 231,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 232,232_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.525353811641059986747848142841,0.590263963053190177099338598055 233,233_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.403349381369930815033342241804,0.511975446638067821503170762298 234,234_0,COMPLETED,BoTorch,0.194048512128031980239484255435,50,0.100000000000000005551115123126,0.583243071349429986760526389844 235,235_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.581108309320764515604196276399,0.527300206290547190945972033660 236,236_0,COMPLETED,BoTorch,0.197049262315578843640651030000,50,0.158372935996547642423237789444,0.541986307645733300653034802963 237,237_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.303184079167966691326085992841,0.638505679197377218336839632684 238,238_0,COMPLETED,BoTorch,0.199549887471867970489824983815,50,0.100000000000000005551115123126,0.752166613480570678262893125066 239,239_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.524627171916830392639496949414,0.536896210948017316155755906948 240,240_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.353971511700671981337507077114,0.549652822449042788299777839711 241,241_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.341635883616674740359542283841,0.569391932199675210313216666691 242,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 243,243_0,COMPLETED,BoTorch,0.181545386346586679060521873907,50,0.545855179339290841333820480941,0.545331624061694664185040437587 244,244_0,COMPLETED,BoTorch,0.198799699924981254639533290174,50,0.263209271483330953245172167954,0.720049752475542370611094611377 245,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 246,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 247,247_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.459392414904354451365975364752,0.514964904873884687930285508628 248,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 249,249_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.814103939532104692311520466319,0.512200298947206422717215446028 250,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 251,251_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.210240153371606147691963428770,0.735459111361490669445117873693 252,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 253,253_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,0.386903523945401284223066795676,0.500000000000000000000000000000 254,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 255,255_0,COMPLETED,BoTorch,0.180045011252813247359938486625,50,0.381863804186298616549777307227,0.513234315953521536002313041536 256,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 257,257_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.748964817966614959665605510963,0.580231798115811825411469726532 258,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000 259,259_0,COMPLETED,BoTorch,0.197549387346836691214946313266,66,0.487019784224768992331178196764,0.567335659632140387742538223392 260,260_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.976159623012681554321545718267,0.523053350430102370616225471167 261,261_0,COMPLETED,BoTorch,0.195548887221805411940067642718,60,0.999559271280566652428944962594,0.500076443550252602854300221225 262,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000 263,64_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,1.000000000000000000000000000000,0.500000000000000000000000000000 264,264_0,COMPLETED,BoTorch,0.180545136284070983911931307375,50,0.432763444941595687431856731564,0.500000000000000000000000000000 265,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000 266,266_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.453154905231889060246430744883,0.640787051532197948766622630501 267,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 268,268_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.638649294981019965966595464124,0.572550557352942579569798908778 269,269_0,COMPLETED,BoTorch,0.195548887221805411940067642718,50,0.584951192977362421920872748160,0.571255100651174352321959304390 270,270_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.342796882501694710754236439243,0.500000000000000000000000000000 271,271_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.339732338212254847409354852061,0.528814672920231321207040764421 272,272_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.640456918388409235376457218081,0.500000000000000000000000000000 273,273_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.469504822019437217939241691056,0.500000000000000000000000000000 274,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000 275,275_0,COMPLETED,BoTorch,0.181295323830957699762223001017,50,0.423156009991628501154536934337,0.679047851746629405056410178076 276,276_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.340562641922253273030207765260,0.521671913525359243202217385260 277,277_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.417507433042165310155269253301,0.800000000000000044408920985006 278,278_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.689656700009414924679163050314,0.500000000000000000000000000000 279,64_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,1.000000000000000000000000000000,0.500000000000000000000000000000 280,280_0,COMPLETED,BoTorch,0.182295573893473394910813567549,50,0.511539765795757328525894536142,0.588461947778365912498088619031 281,281_0,COMPLETED,BoTorch,0.196049012253063259514362925984,60,0.998541046681809429941267808317,0.500323702740395503951731370762 282,282_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.613031677369160865609387656150,0.663202174389217580241506766470 283,64_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,1.000000000000000000000000000000,0.500000000000000000000000000000 284,284_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.352444484561499016272989592835,0.800000000000000044408920985006 285,285_0,COMPLETED,BoTorch,0.194548637159289827813779538701,50,0.210061456478748326270888924228,0.593791048398628840132573714072 286,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 287,287_0,COMPLETED,BoTorch,0.191797949487371832688609174511,58,0.584198674697140152289875913993,0.500000000000000000000000000000 288,288_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.297292476833203866970478657095,0.678413861846507537656236763723 289,289_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.379607187756637376452317766962,0.712458416563205743088360577531 290,290_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.389361991054173461890286489506,0.781093601243838753234172145312 291,291_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.588540385740966121019823731331,0.691296998279221197591937198013 292,292_0,COMPLETED,BoTorch,0.200800200050012533914411960723,50,0.539420578070084477673162837164,0.736588885519427361003863552469 293,293_0,COMPLETED,BoTorch,0.181295323830957699762223001017,50,0.584663942695581417829941983655,0.514884386245281877592105956865 294,294_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.381760446164720046446916512650,0.500000000000000000000000000000 295,295_0,COMPLETED,BoTorch,0.203800950237559397315578735288,50,0.423806385124041562484364931152,0.755451717956675983245418137813 296,296_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.397477535149667327019074036798,0.672536109224622813407279409148 297,297_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.372870465920332572196116416308,0.521296847927623763219173724792 298,298_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.275529479300566149113649316860,0.800000000000000044408920985006 299,299_0,COMPLETED,BoTorch,0.184046011502875694887393365207,50,0.490275950557296669174434100569,0.681958557589167013723852051044 300,300_0,COMPLETED,BoTorch,0.212803200800200098541381521500,96,0.426368338138419344929275212053,0.558550867323062250058285371779 301,301_0,COMPLETED,BoTorch,0.181045261315328831486226590641,50,0.420866735770609512456985612516,0.702582226308535284786671581969 302,302_0,COMPLETED,BoTorch,0.203550887721930529039582324913,50,0.167868169071138301218226729361,0.800000000000000044408920985006 303,303_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.391379339642995538461889282189,0.762501222951289525653351120127 304,304_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.365300130674745426873073483875,0.531244682449988681050001559925 305,305_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.841977539560282095543186642317,0.500000000000000000000000000000 306,306_0,COMPLETED,BoTorch,0.195048762190547675388074821967,50,0.376741117470672071121384760772,0.500000000000000000000000000000 307,307_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.366027456593396305351006958517,0.552523008336156973285824278719 308,308_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.719396530052714866521057501814,0.500000000000000000000000000000 309,309_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.370611170221879615560567344801,0.657452627378011134062774090125 310,310_0,COMPLETED,BoTorch,0.182795698924731131462806388299,50,0.403239482669721871488377473725,0.533128338091727882463999321772 311,311_0,COMPLETED,BoTorch,0.196299074768692127790359336359,50,0.461021422525776936041097542329,0.745377958271499330145104522671 312,53_0,COMPLETED,BoTorch,0.198799699924981254639533290174,50,0.100000000000000005551115123126,0.800000000000000044408920985006 313,313_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.369152948684562720593760332122,0.500000000000000000000000000000 314,314_0,COMPLETED,BoTorch,0.198049512378094538789241596533,50,0.256556032096753705573632942105,0.796453643473606254232777246216 315,315_0,COMPLETED,BoTorch,0.197549387346836691214946313266,50,0.595949570122136340621921135607,0.658240684589882274480032720021 316,316_0,COMPLETED,BoTorch,0.206551637909477392440749099478,74,0.100015653221823777596632965015,0.799997458379825943097785057034 317,317_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.347928596046918836570682742604,0.500000000000000000000000000000 318,318_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.414327767460737628191225212504,0.613986597244317033883476142364 319,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 320,64_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,1.000000000000000000000000000000,0.500000000000000000000000000000 321,321_0,COMPLETED,BoTorch,0.200550137534383554616113087832,50,0.100000000000000019428902930940,0.724153951445219767890648654429 322,322_0,COMPLETED,BoTorch,0.199049762440610122915529700549,50,0.411020175934334153211580087373,0.660480038124953150457940864726 323,323_0,COMPLETED,BoTorch,0.267316829207301820403586134489,178,0.903184896401491577044851055689,0.539212374595242183161758475762 324,324_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.100000000000000005551115123126,0.508884676785355982708836108941 325,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 326,326_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.364672012215856433670069236541,0.566066162276287365706650689390 327,327_0,COMPLETED,BoTorch,0.198299574893723407065238006908,50,0.377847605923691043372514286602,0.582191249292318580010885398224 328,328_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.352889629663519222013690068707,0.500000000000000000000000000000 329,329_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.903806068121394523551259680971,0.519803977184655208176877749793 330,330_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.878260412234960652355653110135,0.510985882733827834201179030060 331,331_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.437450948215405976959857525799,0.540734699023231346615148140700 332,332_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.100000000000000005551115123126,0.624829600906597781850848605245 333,333_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.511426498791174966029871029605 334,334_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.457000639479605119674943125574,0.529961398872461297848701633484 335,335_0,COMPLETED,BoTorch,0.185046261565391390035983931739,50,0.342015040641369560958651163673,0.619846100780894748716320918902 336,336_0,COMPLETED,BoTorch,0.198549637409352386363536879799,50,0.177229760245530110207567986436,0.654747616843177171475076647766 337,337_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.542440388943294271584250054730,0.667358309913701708282474100997 338,338_0,COMPLETED,BoTorch,0.189047261815453837563438810321,50,0.686458813378676535599254293629,0.521953075780997366450719709974 339,339_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.318573618459392138291264018335,0.526994221643042992653249712021 340,340_0,COMPLETED,BoTorch,0.183295823955988979037101671565,50,0.280090734897204263198489115894,0.623968009772629339515503943403 341,341_0,COMPLETED,BoTorch,0.178294573643410836361056226451,50,0.350091209927966162673840244679,0.678171372878784373128269180597 342,342_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.530794738226223472565834526904 343,343_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.419514253238369994036816024163,0.616050522144240941813109202485 344,344_0,COMPLETED,BoTorch,0.185046261565391390035983931739,50,0.403706858603698259813086224312,0.767036639247117735251890735526 345,345_0,COMPLETED,BoTorch,0.179544886221555399785643203359,50,0.354987756710988033859166534967,0.651791281222186991151090751373 346,346_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.879144422166368122439905619103,0.552455221404233531856675654126 347,347_0,COMPLETED,BoTorch,0.199799949987496838765821394190,50,0.412796594755190149328427651199,0.737824672074026910451038929750 348,348_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.603906035585979661384214978170,0.500000000000000000000000000000 349,349_0,COMPLETED,BoTorch,0.202300575143785965614995348005,50,0.430324269839404527360215979570,0.785532366071003074203815685905 350,350_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.287559274459247837807396308563,0.500000000000000000000000000000 351,351_0,COMPLETED,BoTorch,0.213803450862715682667669625516,50,0.401452374942600109797297136538,0.800000000000000044408920985006 352,352_0,COMPLETED,BoTorch,0.181295323830957699762223001017,50,0.235489006210967521948163039269,0.694676045628319416280760378868 353,353_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.290881691380718232498026054600,0.716074155405533652185567916604 354,354_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.538445780258377526550361835689 355,64_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,1.000000000000000000000000000000,0.500000000000000000000000000000 356,356_0,COMPLETED,BoTorch,0.201300325081270270466404781473,50,0.343283551920363971809990744077,0.721784379326930292108954745345 357,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 358,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 359,359_0,COMPLETED,BoTorch,0.193548387096774243687491434684,50,0.326917882142228610753420525725,0.696904549479442247950089495134 360,360_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.345656108585776999930772035441,0.540133010225478882304628314159 361,361_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.962266955098403320434385932458,0.531648345455089321731634299795 362,362_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.606501225369156737876608076476,0.625805149268970239972986746579 363,363_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.338719621114231206338018864699,0.697794563881288798512514404138 364,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 365,365_0,COMPLETED,BoTorch,0.260565141285321377750960891717,177,0.913558493095204982026302786835,0.542254582043029453863880462450 366,366_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.653367863400310389110359210463,0.514590919665448787156947219046 367,367_0,COMPLETED,BoTorch,0.174043510877719409535302474978,50,0.225887353628974468788825902266,0.683044326531255019396837724344 368,368_0,COMPLETED,BoTorch,0.204051012753188265591575145663,77,0.653582313591051788925767596083,0.500000000000000000000000000000 369,369_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.289992807574675937054564656137,0.579687462579378420812759031833 370,370_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.311451275326714238644854049198,0.619830834377778527866098556842 371,371_0,COMPLETED,BoTorch,0.201800450112528118040700064739,82,0.560971421302151163068572259363,0.507510894059416517443139582610 372,372_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.262533071270815709929991044191,0.561041695764529890766425523907 373,373_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.342881767681622151577869317407,0.518937497802316571871017458761 374,374_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.767369234492963459004499782168,0.500000000000000000000000000000 375,375_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.331811174303643596772417367902,0.518686157220989096927610262355 376,376_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.555016721942769519770877195697,0.533769715739977890223144640913 377,377_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.302784021701473671228654893639,0.666130630674380497247000221250 378,378_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.643967551631823953428579443425,0.637865390648939989404198058764 379,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 380,380_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.274579381527525590023230961378,0.640460362014749540193747634476 381,381_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.369662933221642675540863365313,0.500000000000000000000000000000 382,382_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.404162075622059746571324012621,0.500000000000000000000000000000 383,383_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.913813340303445964529771572415,0.522981512018749539194573117129 384,384_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.736990802060846661447612859774,0.500000000000000000000000000000 385,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 386,386_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.546958791651856790494434790162,0.570494272878494523837389351684 387,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 388,388_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.517722560989907321093994596595 389,389_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.602658433296178075444515798154,0.535559954363865609039407900127 390,390_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.364615398519987032877054389246,0.500000000000000000000000000000 391,391_0,COMPLETED,BoTorch,0.182545636409102263186809977924,50,0.320106588629211707974775436014,0.523740931512669094516354562074 392,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000 393,393_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,0.537389382221507339032484651398,0.599404206477934575758581559057 394,394_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.316961759261636999429612160384,0.643098404717624916315799055155 395,49_0,COMPLETED,BoTorch,0.198299574893723407065238006908,50,0.100000000000000005551115123126,0.500000000000000000000000000000 396,396_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.554855736910768504444035897905 397,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000 398,398_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.684727421774331546089342737105,0.564088203065787663348373826011 399,399_0,COMPLETED,BoTorch,0.181795448862215547336518284283,50,0.281708211391811735868628829849,0.636422174245226734967673110077 400,64_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,1.000000000000000000000000000000,0.500000000000000000000000000000 401,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 402,402_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.639482383433387813198578442098,0.550843955403799934167352603254 403,403_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.919810561818984018245259903779,0.539547316058045400843923289358 404,404_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.315199436466140237023125791893,0.627106753083499501855158086983 405,405_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.925269402693248088631605696719,0.500000000000000000000000000000 406,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 407,407_0,COMPLETED,BoTorch,0.180545136284070983911931307375,50,0.435483280788934279392776716122,0.500000000000000000000000000000 408,408_0,COMPLETED,BoTorch,0.184796199049762410737685058848,50,0.655736283414006138059448858257,0.549739044672193921670100280608 409,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 410,410_0,COMPLETED,BoTorch,0.184546136534133542461688648473,50,0.292663076194693783094180616899,0.545143764170563982496275912126 411,411_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.555311427881579056098360069882,0.706652153066541499626396216627 412,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 413,413_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.336846474582980448531088768505,0.553131371199966026530603357969 414,414_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.296678888646297933551210235237,0.534879236588653439454787985596 415,415_0,COMPLETED,BoTorch,0.215803950987746961942548296065,102,1.000000000000000000000000000000,0.500000000000000000000000000000 416,416_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.506057812643168736244092542620,0.605985501013661997937731484853 417,417_0,COMPLETED,BoTorch,0.183295823955988979037101671565,50,0.284193236125531101254892973884,0.630016041010267757727092430287 418,418_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.852084262859597596495575544395,0.500000000000000000000000000000 419,419_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.665362683418703748650102625106,0.514860941277073602684311026678 420,64_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,1.000000000000000000000000000000,0.500000000000000000000000000000 421,49_0,COMPLETED,BoTorch,0.199299824956239102213828573440,50,0.100000000000000005551115123126,0.500000000000000000000000000000 422,422_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.932998203013783000692171754054,0.515034670687837925484586776292 423,64_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,1.000000000000000000000000000000,0.500000000000000000000000000000 424,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 425,425_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.956375570628997029309914523765,0.539328398497139760436880351335 426,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 427,427_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.883234524260766984404824597732,0.500000000000000000000000000000 428,428_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.284791800160602459612846359960,0.653604316906256421759735530941 429,429_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.966655440363399676151345829567,0.536843173108926152714559520973 430,430_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.721840184503232173973685803503,0.533949440742214176403024339379 431,431_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.665030190472247539901218260638,0.544660753824110166476657468593 432,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 433,433_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.303477046633177538481618285005,0.632610330893489947179375576525 434,434_0,COMPLETED,BoTorch,0.180295073768442115635934897000,50,0.292991557122806500768064097429,0.646190293056261633175552105968 435,435_0,COMPLETED,BoTorch,0.194048512128031980239484255435,50,0.773564865035695770068002730113,0.519622155646338956813679033075 436,436_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.285844584119506195385440605605,0.674893993050649565468290802528 437,437_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.535440059969595871791625540936,0.627023791264731178429769897775 438,438_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,0.383976710520015696026518980943,0.701259212245332941293440853769 439,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 440,440_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.756563130826383045679506267334,0.531152226949441419456832136348 441,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 442,442_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.470280157739651372139633167535,0.661112269975318911363615370647 443,443_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.510382760155499881626894875808,0.500000000000000000000000000000 444,444_0,COMPLETED,BoTorch,0.180295073768442115635934897000,50,0.768922478541656628792111405346,0.561089333250431310240458060434 445,445_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,0.753794961323090428884086122707,0.580317280215367770068723984878 446,446_0,COMPLETED,BoTorch,0.183295823955988979037101671565,59,0.472359549232347242231355721742,0.500000000000000000000000000000 447,447_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.314415155074783458921672263386,0.518525412369845173365945356636 448,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 449,449_0,COMPLETED,BoTorch,0.197799449862465670513245186157,61,0.856010560733706515890162336291,0.500000000000000000000000000000 450,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 451,451_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.677300792992510447554366237455,0.615481753896617633792232027190 452,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 453,453_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.405722571524761210781662157387,0.500000000000000000000000000000 454,454_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,1.000000000000000000000000000000,0.535738889431352061087920901628 455,64_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,1.000000000000000000000000000000,0.500000000000000000000000000000 456,456_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.262554452654409853362693638701,0.560482743665254856679780459672 457,457_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.611605446699628019224803665566,0.637940740425373409294707016670 458,458_0,COMPLETED,BoTorch,0.195548887221805411940067642718,62,0.293328003518769697688384212597,0.696873446048822731135885533149 459,459_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.656250350699094320283677461703,0.597827216110904835488781827735 460,460_0,COMPLETED,BoTorch,0.200050012503125818064120267081,51,0.789909697844259017784906973247,0.623751981475111905162123093760 461,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000 462,462_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.613005596433220278917985979206,0.663673793557769453599348707939 463,463_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.675867600351706454553379899153,0.595730314311224473655670408334 464,464_0,COMPLETED,BoTorch,0.184046011502875694887393365207,50,0.595077787873350083636125873454,0.620540414022890729484061012045 465,465_0,COMPLETED,BoTorch,0.182295573893473394910813567549,50,0.308373733589650533826187484010,0.655160544006075706846559114638 466,466_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.356089883996532408083623977291,0.500000000000000000000000000000 467,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 468,468_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.733778010300686789335600224149,0.601991070324807031610703234037 469,469_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.303811701107085108120031691215,0.654363024731915765563883269351 470,470_0,COMPLETED,BoTorch,0.178294573643410836361056226451,50,0.620019421106727808279401870095,0.500000000000000000000000000000 471,471_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.647787384996491466537804626569,0.534750533695551477642027293768 472,472_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.556754596162072012965893463843,0.527298861175770738896062539425 473,473_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.310698231018934456493241214048,0.650231218700586532932561567577 474,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 475,475_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.322191282313596727426840971020,0.500000000000000000000000000000 476,476_0,COMPLETED,BoTorch,0.183295823955988979037101671565,50,0.426531348462183657055390995083,0.500000000000000000000000000000 477,477_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.844830283784090196874672074046,0.511557933168035106064053252339 478,478_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.315442894481137381390567497874,0.544811600813915442031998281891 479,64_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,1.000000000000000000000000000000,0.500000000000000000000000000000 480,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 481,481_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.727997531753198146020622516517,0.514693242713986864877995230927 482,482_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.655599648519003763702528431168,0.500000000000000000000000000000 483,483_0,COMPLETED,BoTorch,0.181795448862215547336518284283,54,0.589170756138263973511470794620,0.539246557678844440353316258552 484,484_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.301517855210633067830627851436,0.627587004425708916421910998906 485,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 486,486_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.866910637299097119878865669307,0.521062131871933731375179377210 487,487_0,COMPLETED,BoTorch,0.189297324331082816861737683212,53,0.336449317292861982409135634953,0.555430628783738389309121430415 488,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 489,489_0,COMPLETED,BoTorch,0.196799199799949975364654619625,58,0.289774233275913783991484251601,0.674990903302228328897172104917 490,490_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.939260894699990078748896848992,0.500000000000000000000000000000 491,64_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,1.000000000000000000000000000000,0.500000000000000000000000000000 492,492_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,0.440319863895973551137785761966,0.500000000000000000000000000000 493,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 494,494_0,COMPLETED,BoTorch,0.193548387096774243687491434684,50,0.422640903285450453275018389832,0.500000000000000000000000000000 495,495_0,COMPLETED,BoTorch,0.182795698924731131462806388299,50,0.416895915044817821915046351933,0.500000000000000000000000000000 496,496_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.618032801086244476529429903167,0.604734491208909763493295486114 497,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000 498,498_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.310349556061717657406973103207,0.643957295017805853021286566218 499,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 500,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000
start_time,end_time,run_time,program_string,n_reference_samples,recent_samples_proportion,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727476464,1727476493,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.8468098700046539 threshold 0.5229895263910294,51,0.8468098700046539,0.5229895263910294,0.19229807451862968,0,None,i7186,25,0.006159074015079112 1727476479,1727476506,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 112 recent_samples_proportion 0.9885343150235713 threshold 0.5574098777025938,112,0.9885343150235713,0.5574098777025938,0.23580895223805953,0,None,i7181,23,0.01269067266816704 1727476479,1727476510,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 157 recent_samples_proportion 0.13831842355430127 threshold 0.5146191754378379,157,0.13831842355430127,0.5146191754378379,0.25556389097274324,0,None,i7186,27,0.015453863465866463 1727476484,1727476514,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 308 recent_samples_proportion 0.8200410149991513 threshold 0.5761655329726636,308,0.8200410149991513,0.5761655329726636,0.30182545636409097,0,None,i7181,28,0.028340418437942824 1727476479,1727476515,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 387 recent_samples_proportion 0.1490331193432212 threshold 0.6409547178074717,387,0.1490331193432212,0.6409547178074717,0.3178294573643411,0,None,i7186,32,0.03600900225056264 1727476479,1727476515,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 313 recent_samples_proportion 0.6475273158401251 threshold 0.6218916520476342,313,0.6475273158401251,0.6218916520476342,0.33783445861465367,0,None,i7186,32,0.03378622433386124 1727476479,1727476529,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 446 recent_samples_proportion 0.6846192532218993 threshold 0.757331812940538,446,0.6846192532218993,0.757331812940538,0.44136034008502123,0,None,i7186,46,0 1727476479,1727476529,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 336 recent_samples_proportion 0.6976534412242472 threshold 0.7884980627335608,336,0.6976534412242472,0.7884980627335608,0.44136034008502123,0,None,i7186,46,0 1727476484,1727476530,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 493 recent_samples_proportion 0.7197453032247723 threshold 0.7853973969817162,493,0.7197453032247723,0.7853973969817162,0.44136034008502123,0,None,i7181,43,0 1727476504,1727476536,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 271 recent_samples_proportion 0.8227414831519126 threshold 0.5886665243655443,271,0.8227414831519126,0.5886665243655443,0.31182795698924726,0,None,i7181,29,0.03000750187546887 1727476504,1727476542,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 134 recent_samples_proportion 0.7715333219617605 threshold 0.726162222120911,134,0.7715333219617605,0.726162222120911,0.33183295823955994,0,None,i7186,34,0.03445305770887166 1727476504,1727476546,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 435 recent_samples_proportion 0.848481718543917 threshold 0.6181799137033522,435,0.848481718543917,0.6181799137033522,0.3545886471617904,0,None,i7181,38,0.07183045761440361 1727476504,1727476548,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 328 recent_samples_proportion 0.8088069194927812 threshold 0.7267458532005548,328,0.8088069194927812,0.7267458532005548,0.3988497124281071,0,None,i7181,40,0.24306076519129777 1727476504,1727476550,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 475 recent_samples_proportion 0.4858905509114265 threshold 0.773015015386045,475,0.4858905509114265,0.773015015386045,0.44136034008502123,0,None,i7181,42,0 1727476504,1727476550,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 302 recent_samples_proportion 0.36928261537104845 threshold 0.7363000434823335,302,0.36928261537104845,0.7363000434823335,0.38284571142785695,0,None,i7181,42,0.06476619154788697 1727476531,1727476613,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 134 recent_samples_proportion 0.7581079781986773 threshold 0.5261575544252992,134,0.7581079781986773,0.5261575544252992,0.2218054513628407,0,None,i7177,26,0.015003750937734433 1727476531,1727476614,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 196 recent_samples_proportion 0.43931583706289534 threshold 0.660423051379621,196,0.43931583706289534,0.660423051379621,0.2703175793948487,0,None,i7177,27,0.021858405777915067 1727476531,1727476618,87,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 141 recent_samples_proportion 0.6982393396086991 threshold 0.719711340405047,141,0.6982393396086991,0.719711340405047,0.3283320830207552,0,None,i7177,32,0.031357839459864964 1727476531,1727476620,89,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 491 recent_samples_proportion 0.1130860535427928 threshold 0.7266595480963588,491,0.1130860535427928,0.7266595480963588,0.3305826456614154,0,None,i7177,34,0.04447540456542707 1727476531,1727476629,98,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 408 recent_samples_proportion 0.6347772148437798 threshold 0.6873878906480968,408,0.6347772148437798,0.6873878906480968,0.39809952488122036,0,None,i7177,42,0.24381095273818448 1727476723,1727476751,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6497863565258409 threshold 0.5,50,0.6497863565258409,0.5,0.18829707426856712,0,None,i7186,24,0.00589108316040049 1727476743,1727476768,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.815217720624371 threshold 0.5,50,0.815217720624371,0.5,0.1822955738934734,0,None,i7181,22,0.006128198716345752 1727476743,1727476771,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5900826207446044 threshold 0.561276017488033,50,0.5900826207446044,0.561276017488033,0.19604901225306326,0,None,i7186,24,0.005866598228504495 1727476743,1727476771,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.494705493128585 threshold 0.5,50,0.494705493128585,0.5,0.18529632408102026,0,None,i7186,24,0.005930053942056942 1727476744,1727476771,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9672739839338887 threshold 0.5,50,0.9672739839338887,0.5,0.19004751187796953,0,None,i7186,24,0.005868350204434224 1727476744,1727476771,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4359053939638424 threshold 0.5847681659758819,50,0.4359053939638424,0.5847681659758819,0.19429857464366096,0,None,i7186,24,0.006048809499672215 1727476744,1727476772,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6420983358191035,50,0.1,0.6420983358191035,0.1965491372843211,0,None,i7186,24,0.006100840278562791 1727476750,1727476776,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5049109182173203 threshold 0.5479392066138571,50,0.5049109182173203,0.5479392066138571,0.19129782445611399,0,None,i7181,22,0.006008168708843878 1727476750,1727476776,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2945423995658617 threshold 0.6140452275253238,50,0.2945423995658617,0.6140452275253238,0.18929732433108282,0,None,i7181,22,0.006116393963355703 1727476750,1727476776,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7071746215873208 threshold 0.5456619343159609,50,0.7071746215873208,0.5456619343159609,0.18929732433108282,0,None,i7181,22,0.005878092899848338 1727476763,1727476788,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.801424948740083 threshold 0.5,50,0.801424948740083,0.5,0.18529632408102026,0,None,i7181,22,0.0060881887138451276 1727476763,1727476788,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.1 threshold 0.6390009752540857,62,0.1,0.6390009752540857,0.2118029507376844,0,None,i7181,22,0.007050943063634761 1727476763,1727476792,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.7166069556156331,50,0.1,0.7166069556156331,0.19704926231557884,0,None,i7186,25,0.006265650919772197 1727476763,1727476801,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.1 threshold 0.5,500,0.1,0.5,0.32808202050512625,0,None,i7181,33,0.044832636730611226 1727476781,1727476809,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5751038430088284 threshold 0.6073129398746737,50,0.5751038430088284,0.6073129398746737,0.18929732433108282,0,None,i7186,24,0.006374833144905944 1727476781,1727476809,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5415603144792462 threshold 0.5162422012767657,50,0.5415603144792462,0.5162422012767657,0.18154538634658668,0,None,i7186,24,0.005978767419127508 1727476781,1727476810,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 63 recent_samples_proportion 0.7852217025402749 threshold 0.5117859144900397,63,0.7852217025402749,0.5117859144900397,0.1847961990497624,0,None,i7186,25,0.007618571309494041 1727476781,1727476811,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 138 recent_samples_proportion 0.21020386044502654 threshold 0.5687387706798819,138,0.21020386044502654,0.5687387706798819,0.23480870217554384,0,None,i7186,26,0.014539349122995036 1727476781,1727476815,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 301 recent_samples_proportion 0.1 threshold 0.5057836798166114,301,0.1,0.5057836798166114,0.3063265816454114,0,None,i7186,30,0.025814145844153345 1727476803,1727476831,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4956171152333346 threshold 0.5002221226874368,50,0.4956171152333346,0.5002221226874368,0.18629657414353584,0,None,i7186,24,0.005994919782577224 1727476864,1727476892,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5895014104383194,50,0.1,0.5895014104383194,0.19279819954988742,0,None,i7186,24,0.005988163707593566 1727476864,1727476892,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3164386387715019 threshold 0.5396395141154364,50,0.3164386387715019,0.5396395141154364,0.1867966991747937,0,None,i7186,24,0.0059883391900606734 1727476864,1727476892,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2826606115176842 threshold 0.653607132618334,50,0.2826606115176842,0.653607132618334,0.18204551137784442,0,None,i7186,25,0.006214391435696762 1727476864,1727476893,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.17473166288412356 threshold 0.5810062833165514,50,0.17473166288412356,0.5810062833165514,0.1965491372843211,0,None,i7186,25,0.005938151204467783 1727476864,1727476899,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 355 recent_samples_proportion 0.28557873766846065 threshold 0.5,355,0.28557873766846065,0.5,0.31357839459864967,0,None,i7186,31,0.03283320830207552 1727476872,1727476905,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 330 recent_samples_proportion 0.11589843403431642 threshold 0.5588681988008068,330,0.11589843403431642,0.5588681988008068,0.2935733933483371,0,None,i7186,30,0.03166700766100616 1727476871,1727476907,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 1 threshold 0.5,500,1,0.5,0.36059014753688423,0,None,i7181,32,0.0468867216804201 1727476883,1727476908,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.25444846445515895 threshold 0.5836383856865299,50,0.25444846445515895,0.5836383856865299,0.18554638659664913,0,None,i7181,22,0.0059268063769189056 1727476883,1727476908,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.19729932483120782,0,None,i7181,22,0.005850146747213118 1727476883,1727476908,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2707529247091325 threshold 0.6711795469154598,50,0.2707529247091325,0.6711795469154598,0.18704676169042256,0,None,i7181,22,0.006064849545719763 1727477044,1727477072,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2945264779276246 threshold 0.614044591533121,50,0.2945264779276246,0.614044591533121,0.1885471367841961,0,None,i7186,24,0.005887835595262451 1727477044,1727477072,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33024639203148765 threshold 0.6722668371937249,50,0.33024639203148765,0.6722668371937249,0.18529632408102026,0,None,i7186,24,0.006254988404635405 1727477044,1727477073,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.36248934956830825 threshold 0.6508260290954803,50,0.36248934956830825,0.6508260290954803,0.18729682420605154,0,None,i7186,25,0.006403009203004976 1727477053,1727477081,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4099931534994644 threshold 0.6225254195246394,50,0.4099931534994644,0.6225254195246394,0.19279819954988742,0,None,i7186,24,0.005909372079862071 1727477053,1727477081,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.8,50,0.1,0.8,0.18954738684671169,0,None,i7186,25,0.006371311137643566 1727477054,1727477081,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31339333580459494 threshold 0.6557367197541561,50,0.31339333580459494,0.6557367197541561,0.19329832458114526,0,None,i7186,24,0.005981495373843461 1727477063,1727477088,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7252411345977137 threshold 0.5,50,0.7252411345977137,0.5,0.19129782445611399,0,None,i7181,21,0.005929113857411722 1727477063,1727477089,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 77 recent_samples_proportion 0.13283365941854341 threshold 0.6887635106258637,77,0.13283365941854341,0.6887635106258637,0.2090522630657664,0,None,i7181,22,0.00883384111333956 1727477063,1727477098,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 421 recent_samples_proportion 0.9998564611732348 threshold 0.5867134598059232,421,0.9998564611732348,0.5867134598059232,0.34783695923980995,0,None,i7181,31,0.049012253063265815 1727477083,1727477125,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 401 recent_samples_proportion 0.8040468505034104 threshold 0.6446926496524389,401,0.8040468505034104,0.6446926496524389,0.37284321080270066,0,None,i7186,38,0.06726681670417604 1727477224,1727477252,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3336301559249185 threshold 0.5,50,0.3336301559249185,0.5,0.19154788697174296,0,None,i7186,24,0.005848864813605998 1727477234,1727477261,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2960496072025187 threshold 0.7257116024977589,50,0.2960496072025187,0.7257116024977589,0.18729682420605154,0,None,i7186,24,0.006314078519629907 1727477244,1727477272,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3986484808847103 threshold 0.5,50,0.3986484808847103,0.5,0.18804701175293825,0,None,i7186,24,0.005971887708769297 1727477244,1727477272,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3374911902060783 threshold 0.8,50,0.3374911902060783,0.8,0.19329832458114526,0,None,i7186,24,0.007009564891222806 1727477244,1727477272,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727477263,1727477288,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2856116699065818 threshold 0.5,50,0.2856116699065818,0.5,0.18729682420605154,0,None,i7181,22,0.005981758597544122 1727477263,1727477289,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 53 recent_samples_proportion 0.1 threshold 0.7126655827023544,53,0.1,0.7126655827023544,0.20955238809702426,0,None,i7181,22,0.006862826817815565 1727477263,1727477289,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.6111907722438515 threshold 0.8,51,0.6111907722438515,0.8,0.20480120030007498,0,None,i7181,22,0.009300197389772976 1727477264,1727477292,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2727538772891339 threshold 0.7161728137729657,50,0.2727538772891339,0.7161728137729657,0.18929732433108282,0,None,i7186,24,0.006374833144905944 1727477264,1727477292,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 58 recent_samples_proportion 0.6849560012521922 threshold 0.7537713315106943,58,0.6849560012521922,0.7537713315106943,0.20855213803450867,0,None,i7186,25,0.008844047746630534 1727477405,1727477432,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.21760282622680704 threshold 0.7616230788396359,50,0.21760282622680704,0.7616230788396359,0.1937984496124031,0,None,i7186,24,0.006223778166763913 1727477405,1727477433,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2302916538817155 threshold 0.8,50,0.2302916538817155,0.8,0.1985496374093524,0,None,i7186,24,0.006617325973284365 1727477405,1727477433,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.22632461564665426 threshold 0.8,50,0.22632461564665426,0.8,0.1985496374093524,0,None,i7186,25,0.006617325973284365 1727477415,1727477443,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2360426054255106 threshold 0.7067415462481257,50,0.2360426054255106,0.7067415462481257,0.18429607401850467,0,None,i7186,24,0.006183978427039191 1727477415,1727477443,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.21577966365555168 threshold 0.7641732126118936,50,0.21577966365555168,0.7641732126118936,0.19704926231557884,0,None,i7186,25,0.006265650919772197 1727477424,1727477450,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33158894400365757 threshold 0.7674814883117014,50,0.33158894400365757,0.7674814883117014,0.19354838709677424,0,None,i7181,22,0.0064051727217518655 1727477425,1727477452,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.24354596797496075 threshold 0.7734521493880515,50,0.24354596797496075,0.7734521493880515,0.1930482620655164,0,None,i7186,24,0.006505249500780992 1727477444,1727477469,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.21996227870957788 threshold 0.7664757417760404,50,0.21996227870957788,0.7664757417760404,0.20255063765941483,0,None,i7181,22,0.006188166759999859 1727477445,1727477472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.57670143058253 threshold 0.5,50,0.57670143058253,0.5,0.18779694923730927,0,None,i7186,24,0.005897578290676566 1727477445,1727477472,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8701444459839158 threshold 0.5,50,0.8701444459839158,0.5,0.18429607401850467,0,None,i7186,24,0.0060212421526434235 1727477445,1727477473,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.23859987016731274 threshold 0.8,50,0.23859987016731274,0.8,0.1955488872218054,0,None,i7186,25,0.0067630543999636274 1727477466,1727477493,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2602110642974238 threshold 0.7624753525326088,50,0.2602110642974238,0.7624753525326088,0.17754438609652412,0,None,i7186,24,0.006633801307469725 1727477585,1727477613,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 73 recent_samples_proportion 0.8407108676400896 threshold 0.5,73,0.8407108676400896,0.5,0.20405101275318827,0,None,i7186,24,0.008585479703259149 1727477585,1727477613,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 78 recent_samples_proportion 0.6061490509794345 threshold 0.5,78,0.6061490509794345,0.5,0.19804951237809454,0,None,i7186,24,0.008877219304826206 1727477585,1727477614,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.45796792236736505 threshold 0.7115997953429497,50,0.45796792236736505,0.7115997953429497,0.20155038759689925,0,None,i7186,25,0.0063820302901812405 1727477596,1727477624,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5246100666579292 threshold 0.6988743688418873,50,0.5246100666579292,0.6988743688418873,0.18654663665916482,0,None,i7186,24,0.006599475955945507 1727477596,1727477624,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.681179691348951,50,1,0.681179691348951,0.2028007001750438,0,None,i7186,24,0.0066531784461266825 1727477604,1727477630,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8093594042435813 threshold 0.6411414214890747,50,0.8093594042435813,0.6411414214890747,0.19429857464366096,0,None,i7181,22,0.006394455756796341 1727477605,1727477632,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.6179699246699364,50,1,0.6179699246699364,0.18804701175293825,0,None,i7186,24,0.006217307751595433 1727477625,1727477657,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4654459919087971 threshold 0.7312781731595805,50,0.4654459919087971,0.7312781731595805,0.2003000750187547,0,None,i7186,24,0.006691066706070457 1727477630,1727477657,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 59 recent_samples_proportion 0.757918059831655 threshold 0.8,59,0.757918059831655,0.8,0.24381095273818454,0,None,i7181,24,0.010476303286347903 1727477625,1727477657,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9236947166177715 threshold 0.6732436071407066,50,0.9236947166177715,0.6732436071407066,0.20305076269067268,0,None,i7186,25,0.007314328582145536 1727477625,1727477661,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.8,50,1,0.8,0.2540635158789697,0,None,i7186,28,0.012928232058014504 1727477645,1727477674,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7138308433531674 threshold 0.6812801066081856,50,0.7138308433531674,0.6812801066081856,0.19604901225306326,0,None,i7186,25,0.006461760367628138 1727477645,1727477674,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 0.1 threshold 0.8,140,0.1,0.8,0.23405851462865712,0,None,i7186,26,0.015686613961182604 1727477808,1727477836,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.891580900315492 threshold 0.5713167398360113,50,0.891580900315492,0.5713167398360113,0.1957989497374344,0,None,i7186,24,0.006465384462057543 1727477808,1727477837,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.875228599059008 threshold 0.7393029336922554,50,0.875228599059008,0.7393029336922554,0.21330332583145784,0,None,i7186,25,0.0077928573052354 1727477808,1727477837,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5616278555454675,50,1,0.5616278555454675,0.18879719929982497,0,None,i7186,25,0.006041510377594398 1727477829,1727477857,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.8791623118211457 threshold 0.7401826008379088,51,0.8791623118211457,0.7401826008379088,0.23405851462865712,0,None,i7181,24,0.009710761023589231 1727477829,1727477862,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 67 recent_samples_proportion 0.7233273880085523 threshold 0.7960898454436833,67,0.7233273880085523,0.7960898454436833,0.2800700175043761,0,None,i7186,29,0.015076685838126198 1727477829,1727477863,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 70 recent_samples_proportion 0.7004915561853136 threshold 0.7946995773675807,70,0.7004915561853136,0.7946995773675807,0.2593148287071768,0,None,i7186,30,0.014715217265854924 1727477838,1727477864,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9160693150512764 threshold 0.5550861189393442,50,0.9160693150512764,0.5550861189393442,0.19079769942485625,0,None,i7181,22,0.006096118624250657 1727477829,1727477875,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 96 recent_samples_proportion 0.9749413819471323 threshold 0.8,96,0.9749413819471323,0.8,0.40510127531882967,0,None,i7186,42,0.05920230057514379 1727477849,1727477878,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.8757516373773436 threshold 0.6693021631771956,51,0.8757516373773436,0.6693021631771956,0.21480370092523127,0,None,i7186,25,0.007118446278236226 1727477849,1727477879,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9949462415979963 threshold 0.680189613791603,50,0.9949462415979963,0.680189613791603,0.2360590147536884,0,None,i7186,26,0.0086351375077812 1727477869,1727477897,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9992204215466803 threshold 0.6796679074323423,50,0.9992204215466803,0.6796679074323423,0.20855213803450867,0,None,i7186,24,0.007104235075162232 1727477869,1727477900,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 151 recent_samples_proportion 0.1 threshold 0.7882920971178846,151,0.1,0.7882920971178846,0.26081520380095025,0,None,i7186,27,0.01732251244629339 1727477869,1727477907,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 91 recent_samples_proportion 0.9566649404232611 threshold 0.8,91,0.9566649404232611,0.8,0.3393348337084271,0,None,i7186,34,0.030257564391097773 1727477889,1727477917,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9224146462605797 threshold 0.6726354191840684,50,0.9224146462605797,0.6726354191840684,0.20980245061265312,0,None,i7186,24,0.006858857571535742 1727478070,1727478098,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.31270966458284577 threshold 0.5,74,0.31270966458284577,0.5,0.19154788697174296,0,None,i7186,24,0.008660819050916575 1727478070,1727478104,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 183 recent_samples_proportion 1 threshold 0.5,183,1,0.5,0.25106276569142283,0,None,i7186,31,0.020570932206735896 1727478080,1727478108,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 69 recent_samples_proportion 0.3399194543510748 threshold 0.5302407703509013,69,0.3399194543510748,0.5302407703509013,0.1822955738934734,0,None,i7186,24,0.008207408995105919 1727478090,1727478118,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 83 recent_samples_proportion 0.41198988472033127 threshold 0.5,83,0.41198988472033127,0.5,0.2028007001750438,0,None,i7186,25,0.009545864727051327 1727478090,1727478121,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 189 recent_samples_proportion 0.7681639963999016 threshold 0.5,189,0.7681639963999016,0.5,0.2595648912228057,0,None,i7186,27,0.01911727931982996 1727478110,1727478139,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 101 recent_samples_proportion 0.7617239829521327 threshold 0.5361400996397856,101,0.7617239829521327,0.5361400996397856,0.22330582645661412,0,None,i7186,25,0.011015911872705019 1727478110,1727478140,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 90 recent_samples_proportion 0.4477127620128013 threshold 0.5433915487529858,90,0.4477127620128013,0.5433915487529858,0.2128032008002001,0,None,i7186,26,0.00997923899579546 1727478110,1727478141,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 160 recent_samples_proportion 0.7223458976006922 threshold 0.5783495899534817,160,0.7223458976006922,0.5783495899534817,0.25806451612903225,0,None,i7186,27,0.01827837911858917 1727478130,1727478159,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 119 recent_samples_proportion 0.6587477652001585 threshold 0.5595909208721414,119,0.6587477652001585,0.5595909208721414,0.21880470117529383,0,None,i7186,25,0.01322205551387847 1727478130,1727478160,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 168 recent_samples_proportion 1 threshold 0.5,168,1,0.5,0.2673168292073018,0,None,i7186,27,0.017026984018731955 1727478130,1727478164,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 290 recent_samples_proportion 0.8297495254485658 threshold 0.6285102600444117,290,0.8297495254485658,0.6285102600444117,0.3140785196299075,0,None,i7186,31,0.032783195798949734 1727478141,1727478168,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 133 recent_samples_proportion 0.7913830598587849 threshold 0.5223113038993497,133,0.7913830598587849,0.5223113038993497,0.22455613903475868,0,None,i7181,24,0.014905512092308792 1727478141,1727478168,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 136 recent_samples_proportion 0.7674467908282638 threshold 0.5269500250266187,136,0.7674467908282638,0.5269500250266187,0.2200550137534384,0,None,i7181,24,0.015066266566641659 1727478150,1727478179,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 129 recent_samples_proportion 0.8636228936967065 threshold 0.5,129,0.8636228936967065,0.5,0.2218054513628407,0,None,i7186,25,0.014486380215743591 1727478290,1727478318,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4444625968292012 threshold 0.6695623785353799,50,0.4444625968292012,0.6695623785353799,0.19079769942485625,0,None,i7186,24,0.006096118624250657 1727478310,1727478338,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5321528159035395,50,1,0.5321528159035395,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727478310,1727478338,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3424895005388127 threshold 0.5623393075839241,50,0.3424895005388127,0.5623393075839241,0.18379594898724683,0,None,i7186,24,0.005949539332885169 1727478310,1727478338,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37500147039678766 threshold 0.6979218110229877,50,0.37500147039678766,0.6979218110229877,0.19979994998749684,0,None,i7186,24,0.006315864680455829 1727478322,1727478350,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.38247184568157444 threshold 0.5334129538286858,50,0.38247184568157444,0.5334129538286858,0.18979744936234055,0,None,i7186,24,0.005948855634961372 1727478322,1727478350,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5970312756121361 threshold 0.6457261786137842,50,0.5970312756121361,0.6457261786137842,0.1975493873468367,0,None,i7186,24,0.006943142035508877 1727478330,1727478359,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8140384663457826 threshold 0.5547830355541842,50,0.8140384663457826,0.5547830355541842,0.18529632408102026,0,None,i7186,25,0.0060881887138451276 1727478350,1727478378,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2086280452366807 threshold 0.5,50,0.2086280452366807,0.5,0.18829707426856712,0,None,i7186,24,0.00589108316040049 1727478350,1727478378,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.276577611581958 threshold 0.5277024020870048,50,0.276577611581958,0.5277024020870048,0.19054763690922727,0,None,i7186,24,0.00586185507415815 1727478352,1727478380,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9335842267406225 threshold 0.5160669322036749,50,0.9335842267406225,0.5160669322036749,0.18829707426856712,0,None,i7186,24,0.0061299108560924015 1727478370,1727478398,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 85 recent_samples_proportion 0.1 threshold 0.5,85,0.1,0.5,0.20855213803450867,0,None,i7186,25,0.009420833469236873 1727478370,1727478399,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5015928351736566 threshold 0.6602454717987736,50,0.5015928351736566,0.6602454717987736,0.19679919979994998,0,None,i7186,25,0.006097414764650067 1727478382,1727478409,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.27148691904204547 threshold 0.5437081789969962,50,0.27148691904204547,0.5437081789969962,0.18379594898724683,0,None,i7186,23,0.006027822745159974 1727478382,1727478410,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 72 recent_samples_proportion 0.10130961818064137 threshold 0.5,72,0.10130961818064137,0.5,0.19879969992498125,0,None,i7186,24,0.008360580711215539 1727478579,1727478608,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.3131740441536227 threshold 0.5000300921809183,74,0.3131740441536227,0.5000300921809183,0.20130032508127027,0,None,i7186,25,0.008639414755649697 1727478594,1727478621,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7449818975582699 threshold 0.5898619725715268,50,0.7449818975582699,0.5898619725715268,0.18654663665916482,0,None,i7186,24,0.006413575224792113 1727478594,1727478621,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33952903458773515 threshold 0.5864641191834463,50,0.33952903458773515,0.5864641191834463,0.18154538634658668,0,None,i7186,24,0.00605743541148445 1727478600,1727478627,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7899675506910274 threshold 0.5,50,0.7899675506910274,0.5,0.19004751187796953,0,None,i7186,24,0.005945565338703096 1727478619,1727478647,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8984072434157986 threshold 0.5,50,0.8984072434157986,0.5,0.18429607401850467,0,None,i7186,23,0.0060212421526434235 1727478619,1727478648,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34721046330321853 threshold 0.5250968908692354,50,0.34721046330321853,0.5250968908692354,0.18604651162790697,0,None,i7186,24,0.00592031124664283 1727478624,1727478651,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7345525393886744 threshold 0.5250933386612152,50,0.7345525393886744,0.5250933386612152,0.18629657414353584,0,None,i7186,24,0.005994919782577224 1727478639,1727478667,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.38419316174794604 threshold 0.732303288032381,50,0.38419316174794604,0.732303288032381,0.1930482620655164,0,None,i7186,24,0.006600914934616006 1727478655,1727478682,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.11031817230114291 threshold 0.5493028480914539,50,0.11031817230114291,0.5493028480914539,0.18929732433108282,0,None,i7186,24,0.006034842043844294 1727478654,1727478682,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 79 recent_samples_proportion 0.13837710635576922 threshold 0.5,79,0.13837710635576922,0.5,0.28957239309827454,0,None,i7186,24,0.007190573153492455 1727478654,1727478682,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41145282607208733 threshold 0.7370329186396506,50,0.41145282607208733,0.7370329186396506,0.19104776194048512,0,None,i7186,25,0.00663033405410176 1727478679,1727478708,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 78 recent_samples_proportion 0.1 threshold 0.8,78,0.1,0.8,0.21755438859714926,0,None,i7186,25,0.009868746256331525 1727478679,1727478710,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 178 recent_samples_proportion 0.14190181592268966 threshold 0.7669442827251054,178,0.14190181592268966,0.7669442827251054,0.25531382845711426,0,None,i7186,27,0.020347192061173188 1727478684,1727478711,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7123810599474896 threshold 0.5597589286811918,50,0.7123810599474896,0.5597589286811918,0.18454613653413354,0,None,i7186,24,0.006098191214470284 1727478700,1727478727,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 69 recent_samples_proportion 0.1 threshold 0.548806625166557,69,0.1,0.548806625166557,0.23305826456614154,0,None,i7186,24,0.00730093237595113 1727478700,1727478728,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3780161180085567 threshold 0.6007479591627066,50,0.3780161180085567,0.6007479591627066,0.18779694923730927,0,None,i7186,25,0.006054847045094607 1727478940,1727478968,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40229984650595707 threshold 0.5516227749614678,50,0.40229984650595707,0.5516227749614678,0.19404851212803198,0,None,i7186,24,0.005892920598570696 1727478956,1727478983,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7645879945818708 threshold 0.5,50,0.7645879945818708,0.5,0.19129782445611399,0,None,i7186,23,0.005929113857411722 1727478960,1727478987,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7826094334228678 threshold 0.562334009097489,50,0.7826094334228678,0.562334009097489,0.19004751187796953,0,None,i7186,24,0.005945565338703096 1727478980,1727479008,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.44411798223266663 threshold 0.5,50,0.44411798223266663,0.5,0.18554638659664913,0,None,i7186,24,0.006004790671352049 1727478980,1727479008,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6526920260494373 threshold 0.6141456736656518,50,0.6526920260494373,0.6141456736656518,0.19079769942485625,0,None,i7186,24,0.005858607509020112 1727478987,1727479014,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6387634014236604 threshold 0.5404698990768024,50,0.6387634014236604,0.5404698990768024,0.18529632408102026,0,None,i7186,23,0.006170461534302494 1727479000,1727479027,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 52 recent_samples_proportion 0.12315673446023258 threshold 0.7691243912191685,52,0.12315673446023258,0.7691243912191685,0.20480120030007498,0,None,i7186,24,0.006334917062598983 1727479000,1727479027,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5153994471558426 threshold 0.6307122490225099,50,0.5153994471558426,0.6307122490225099,0.1885471367841961,0,None,i7186,24,0.006296713067155677 1727479017,1727479045,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4439827122704889 threshold 0.5561792313865689,50,0.4439827122704889,0.5561792313865689,0.18954738684671169,0,None,i7186,24,0.006031507876969242 1727479017,1727479049,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 189 recent_samples_proportion 0.13623936053545965 threshold 0.7596722945943162,189,0.13623936053545965,0.7596722945943162,0.27156789197299325,0,None,i7186,28,0.021784857979200684 1727479040,1727479068,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.3712813525166381 threshold 0.7530679255375701,51,0.3712813525166381,0.7530679255375701,0.19354838709677424,0,None,i7186,24,0.006498001311922182 1727479040,1727479069,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7254703018042138 threshold 0.8,50,0.7254703018042138,0.8,0.2315578894723681,0,None,i7186,25,0.008207051762940735 1727479047,1727479075,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.47361952602932567 threshold 0.5689166986948282,50,0.47361952602932567,0.5689166986948282,0.18629657414353584,0,None,i7186,24,0.006241286348984507 1727479060,1727479088,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 69 recent_samples_proportion 0.9426436594739805 threshold 0.5,69,0.9426436594739805,0.5,0.19354838709677424,0,None,i7186,24,0.008006465902189832 1727479060,1727479089,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 54 recent_samples_proportion 0.1534755278320187 threshold 0.7598806050269531,54,0.1534755278320187,0.7598806050269531,0.18379594898724683,0,None,i7186,25,0.006736978362237618 1727479290,1727479318,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.5655494526043563 threshold 0.5135949879923076,62,0.5655494526043563,0.5135949879923076,0.1867966991747937,0,None,i7186,24,0.00734054481362276 1727479310,1727479338,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.45070573440979966 threshold 0.5217583894609548,50,0.45070573440979966,0.5217583894609548,0.18454613653413354,0,None,i7186,23,0.005939796637471056 1727479310,1727479338,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8546750633556104 threshold 0.5352273954416168,50,0.8546750633556104,0.5352273954416168,0.18929732433108282,0,None,i7186,24,0.0062001801820318085 1727479320,1727479347,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5387382429606788 threshold 0.5,50,0.5387382429606788,0.5,0.1885471367841961,0,None,i7186,24,0.005887835595262451 1727479330,1727479358,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.44718008596783054 threshold 0.5305976489410036,50,0.44718008596783054,0.5305976489410036,0.1850462615653914,0,None,i7186,24,0.006011371263868598 1727479350,1727479378,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3658317220105607 threshold 0.5,50,0.3658317220105607,0.5,0.19104776194048512,0,None,i7186,24,0.0059324041536699965 1727479350,1727479378,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7878284994040446 threshold 0.5818298659575092,50,0.7878284994040446,0.5818298659575092,0.19454863715928983,0,None,i7186,24,0.0058863400060541445 1727479350,1727479379,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 69 recent_samples_proportion 0.7857542372534163 threshold 0.5,69,0.7857542372534163,0.5,0.18729682420605154,0,None,i7186,25,0.00811810095380988 1727479371,1727479398,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 57 recent_samples_proportion 0.7731692480847236 threshold 0.5078713124955672,57,0.7731692480847236,0.5078713124955672,0.20055013753438355,0,None,i7186,24,0.00668727788007608 1727479371,1727479399,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7186475152741577 threshold 0.6300631341119609,50,0.7186475152741577,0.6300631341119609,0.1965491372843211,0,None,i7186,25,0.006549431475515937 1727479380,1727479408,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5453540999397084,50,1,0.5453540999397084,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727479390,1727479417,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3008062402007739 threshold 0.5685195383388034,50,0.3008062402007739,0.5685195383388034,0.1867966991747937,0,None,i7186,23,0.005910568551228716 1727479411,1727479438,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9059157365461868 threshold 0.5387943701439213,50,0.9059157365461868,0.5387943701439213,0.1902975743935984,0,None,i7186,23,0.006021505376344086 1727479411,1727479439,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.26071766194556123 threshold 0.6103337647828395,50,0.26071766194556123,0.6103337647828395,0.18279569892473113,0,None,i7186,24,0.006204253766144239 1727479431,1727479459,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5133529399501434 threshold 0.5723618866473371,50,0.5133529399501434,0.5723618866473371,0.1857964491122781,0,None,i7186,25,0.006001500375093773 1727479686,1727479715,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 89 recent_samples_proportion 0.5596571574856566 threshold 0.5,89,0.5596571574856566,0.5,0.1957989497374344,0,None,i7186,25,0.010374686694929545 1727479707,1727479734,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3096987788019335 threshold 0.5801384373166898,50,0.3096987788019335,0.5801384373166898,0.1847961990497624,0,None,i7186,23,0.006014661560126874 1727479713,1727479740,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37199748828379775 threshold 0.5257511378030674,50,0.37199748828379775,0.5257511378030674,0.18729682420605154,0,None,i7186,23,0.005981758597544122 1727479726,1727479755,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 96 recent_samples_proportion 0.5950967584579775 threshold 0.5,96,0.5950967584579775,0.5,0.21605401350337583,0,None,i7186,25,0.010646411602900726 1727479743,1727479770,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6608290935077272 threshold 0.5238354600387884,50,0.6608290935077272,0.5238354600387884,0.18954738684671169,0,None,i7186,24,0.006031507876969242 1727479743,1727479771,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4598580436679386 threshold 0.5333578339207528,50,0.4598580436679386,0.5333578339207528,0.17654413603400854,0,None,i7186,24,0.006204884554471951 1727479746,1727479773,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2070102653835333 threshold 0.6452310717288586,50,0.2070102653835333,0.6452310717288586,0.18979744936234055,0,None,i7186,23,0.005948855634961372 1727479766,1727479794,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.19054763690922727,0,None,i7186,24,0.005938984746186547 1727479773,1727479800,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18379594898724683,0,None,i7186,23,0.006027822745159974 1727479786,1727479813,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.20927504466973712 threshold 0.5000209165254,50,0.20927504466973712,0.5000209165254,0.18604651162790697,0,None,i7186,23,0.00592031124664283 1727479803,1727479830,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727479803,1727479832,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 66 recent_samples_proportion 0.5335483144930605 threshold 0.5,66,0.5335483144930605,0.5,0.20330082520630155,0,None,i7186,25,0.007562235386432815 1727479826,1727479853,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6389449749671351 threshold 0.5931224139226308,50,0.6389449749671351,0.5931224139226308,0.1857964491122781,0,None,i7186,23,0.006248137376809956 1727479826,1727479855,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8046411304747543 threshold 0.5345831854856934,50,0.8046411304747543,0.5345831854856934,0.1857964491122781,0,None,i7186,25,0.006001500375093773 1727479846,1727479874,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727479847,1727479874,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8692527934077011 threshold 0.5283636965630069,50,0.8692527934077011,0.5283636965630069,0.1930482620655164,0,None,i7186,24,0.006065705615593087 1727480106,1727480133,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.522938953829649,50,1,0.522938953829649,0.18379594898724683,0,None,i7186,24,0.006027822745159974 1727480106,1727480133,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.144601515659046 threshold 0.6875117036448217,50,0.144601515659046,0.6875117036448217,0.1992998249562391,0,None,i7186,24,0.006063159625522819 1727480127,1727480154,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9443506269677971 threshold 0.5358858577714066,50,0.9443506269677971,0.5358858577714066,0.18804701175293825,0,None,i7186,24,0.006051512878219555 1727480127,1727480154,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5886255393137442,50,1,0.5886255393137442,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727480136,1727480163,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727480147,1727480175,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34912622024138823 threshold 0.6147878022141996,50,0.34912622024138823,0.6147878022141996,0.19254813703425855,0,None,i7186,25,0.006155648501166388 1727480166,1727480194,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 60 recent_samples_proportion 1 threshold 0.5,60,1,0.5,0.19704926231557884,0,None,i7186,24,0.007061289131806762 1727480166,1727480194,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9320716920772729 threshold 0.5,50,0.9320716920772729,0.5,0.19179794948737183,0,None,i7186,24,0.006165925042904562 1727480187,1727480215,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 70 recent_samples_proportion 0.37040639799574215 threshold 0.6153344634799043,70,0.37040639799574215,0.6153344634799043,0.20180045011252812,0,None,i7186,24,0.008002000500125032 1727480197,1727480224,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.5675281133875343 threshold 0.515292554221787,62,0.5675281133875343,0.515292554221787,0.1930482620655164,0,None,i7186,24,0.007239713154094975 1727480207,1727480234,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1902975743935984,0,None,i7186,24,0.0061864781263809105 1727480227,1727480254,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727480227,1727480255,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3980973122925445 threshold 0.5354411146939996,50,0.3980973122925445,0.5354411146939996,0.18979744936234055,0,None,i7186,24,0.005948855634961372 1727480247,1727480274,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 63 recent_samples_proportion 0.7878995022119902 threshold 0.5,63,0.7878995022119902,0.5,0.1847961990497624,0,None,i7186,24,0.007618571309494041 1727480524,1727480552,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32225950103436385 threshold 0.7126055492476037,50,0.32225950103436385,0.7126055492476037,0.18354588647161796,0,None,i7186,24,0.006278967002024478 1727480529,1727480556,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5976263924397182 threshold 0.5,50,0.5976263924397182,0.5,0.19054763690922727,0,None,i7186,23,0.00586185507415815 1727480544,1727480571,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9354094271078387 threshold 0.5520943386936114,50,0.9354094271078387,0.5520943386936114,0.19254813703425855,0,None,i7186,24,0.0060724640619614365 1727480560,1727480587,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7306797998305429 threshold 0.5721064714242717,50,0.7306797998305429,0.5721064714242717,0.1847961990497624,0,None,i7186,24,0.006177219980670843 1727480560,1727480588,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41048574468421806 threshold 0.5,50,0.41048574468421806,0.5,0.18204551137784442,0,None,i7186,24,0.005972272288851434 1727480584,1727480612,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18379594898724683,0,None,i7186,24,0.006027822745159974 1727480584,1727480612,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.42600067880546366 threshold 0.6474007155268138,50,0.42600067880546366,0.6474007155268138,0.18629657414353584,0,None,i7186,24,0.006156944641565797 1727480604,1727480633,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.528513450921708 threshold 0.7076093561812713,50,0.528513450921708,0.7076093561812713,0.19179794948737183,0,None,i7186,25,0.006718097434806463 1727480604,1727480633,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 126 recent_samples_proportion 0.17474847247316083 threshold 0.5552981302306907,126,0.17474847247316083,0.5552981302306907,0.2280570142535634,0,None,i7186,25,0.013350111721478757 1727480620,1727480647,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4038948911179302 threshold 0.5693227276327657,50,0.4038948911179302,0.5693227276327657,0.18929732433108282,0,None,i7186,24,0.006116393963355703 1727480624,1727480651,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4551989946888426 threshold 0.6081422421234407,50,0.4551989946888426,0.6081422421234407,0.18654663665916482,0,None,i7186,23,0.006071517879469867 1727480644,1727480673,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,25,0.005978468301285848 1727480650,1727480678,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9272923027125194 threshold 0.5830875658081784,50,0.9272923027125194,0.5830875658081784,0.20955238809702426,0,None,i7186,24,0.006358207198858538 1727480664,1727480691,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5316966113139151,50,0.1,0.5316966113139151,0.19329832458114526,0,None,i7186,24,0.005981495373843461 1727480681,1727480709,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.48832621760628747 threshold 0.7313231667664906,50,0.48832621760628747,0.7313231667664906,0.1830457614403601,0,None,i7186,25,0.006748010532044775 1727481004,1727481033,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3746373693522781 threshold 0.6237323027686712,50,0.3746373693522781,0.6237323027686712,0.18654663665916482,0,None,i7186,25,0.006071517879469867 1727481012,1727481039,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18829707426856712,0,None,i7186,23,0.006048178711344503 1727481024,1727481051,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.544016875018127,50,1,0.544016875018127,0.18829707426856712,0,None,i7186,23,0.0061299108560924015 1727481043,1727481070,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.52535381164106 threshold 0.5902639630531902,50,0.52535381164106,0.5902639630531902,0.19604901225306326,0,None,i7186,24,0.0061076913063882405 1727481043,1727481071,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727481064,1727481091,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4033493813699308 threshold 0.5119754466380678,50,0.4033493813699308,0.5119754466380678,0.18804701175293825,0,None,i7186,24,0.005894330725538527 1727481073,1727481100,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.58324307134943,50,0.1,0.58324307134943,0.19404851212803198,0,None,i7186,23,0.005892920598570696 1727481084,1727481112,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5811083093207645 threshold 0.5273002062905472,50,0.5811083093207645,0.5273002062905472,0.1937984496124031,0,None,i7186,24,0.00589621089482897 1727481103,1727481130,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.15837293599654764 threshold 0.5419863076457333,50,0.15837293599654764,0.5419863076457333,0.19704926231557884,0,None,i7186,24,0.005853437043471394 1727481103,1727481130,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3031840791679667 threshold 0.6385056791973772,50,0.3031840791679667,0.6385056791973772,0.19154788697174296,0,None,i7186,24,0.006004834541968825 1727481124,1727481153,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.7521666134805707,50,0.1,0.7521666134805707,0.19954988747186797,0,None,i7186,25,0.00631943700210767 1727481133,1727481160,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5246271719168304 threshold 0.5368962109480173,50,0.5246271719168304,0.5368962109480173,0.18954738684671169,0,None,i7186,23,0.005952145931219646 1727481144,1727481172,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.353971511700672 threshold 0.5496528224490428,50,0.353971511700672,0.5496528224490428,0.19054763690922727,0,None,i7186,24,0.005938984746186547 1727481163,1727481191,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727481163,1727481191,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34163588361667474 threshold 0.5693919321996752,50,0.34163588361667474,0.5693919321996752,0.19229807451862968,0,None,i7186,24,0.00591595267237862 1727481184,1727481211,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5458551793392908 threshold 0.5453316240616947,50,0.5458551793392908,0.5453316240616947,0.18154538634658668,0,None,i7186,23,0.005978767419127508 1727481554,1727481582,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.26320927148333095 threshold 0.7200497524755424,50,0.26320927148333095,0.7200497524755424,0.19879969992498125,0,None,i7186,24,0.006154316356866994 1727481574,1727481601,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727481587,1727481615,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727481594,1727481621,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.45939241490435445 threshold 0.5149649048738847,50,0.45939241490435445,0.5149649048738847,0.1902975743935984,0,None,i7186,23,0.005865102639296188 1727481614,1727481641,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727481634,1727481661,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727481634,1727481662,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8141039395321047 threshold 0.5122002989472064,50,0.8141039395321047,0.5122002989472064,0.1857964491122781,0,None,i7186,24,0.006001500375093773 1727481648,1727481674,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.21024015337160615 threshold 0.7354591113614907,50,0.21024015337160615,0.7354591113614907,0.19004751187796953,0,None,i7186,23,0.0063642671231188075 1727481674,1727481701,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3869035239454013 threshold 0.5,50,0.3869035239454013,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727481674,1727481702,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,24,0.005959282028299282 1727481694,1727481721,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727481709,1727481736,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727481710,1727481738,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3818638041862986 threshold 0.5132343159535215,50,0.3818638041862986,0.5132343159535215,0.18004501125281325,0,None,i7186,25,0.005998252809955735 1727481734,1727481762,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.748964817966615 threshold 0.5802317981158118,50,0.748964817966615,0.5802317981158118,0.19229807451862968,0,None,i7186,24,0.006159074015079112 1727481734,1727481762,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1902975743935984,0,None,i7186,24,0.006102877070619006 1727482186,1727482214,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 66 recent_samples_proportion 0.487019784224769 threshold 0.5673356596321404,66,0.487019784224769,0.5673356596321404,0.1975493873468367,0,None,i7186,24,0.0077958086012731255 1727482206,1727482234,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9761596230126816 threshold 0.5230533504301024,50,0.9761596230126816,0.5230533504301024,0.18954738684671169,0,None,i7186,23,0.006113014740171529 1727482223,1727482250,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18829707426856712,0,None,i7186,23,0.006048178711344503 1727482223,1727482251,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 60 recent_samples_proportion 0.9995592712805667 threshold 0.5000764435502526,60,0.9995592712805667,0.5000764435502526,0.1955488872218054,0,None,i7186,25,0.007199380490283862 1727482246,1727482274,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4327634449415957 threshold 0.5,50,0.4327634449415957,0.5,0.18054513628407098,0,None,i7186,24,0.00599175767967966 1727482246,1727482274,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.19179794948737183,0,None,i7186,24,0.006165925042904562 1727482266,1727482294,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18379594898724683,0,None,i7186,24,0.006027822745159974 1727482284,1727482311,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.45315490523188906 threshold 0.640787051532198,50,0.45315490523188906,0.640787051532198,0.18704676169042256,0,None,i7186,23,0.006146806972013274 1727482286,1727482313,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727482306,1727482334,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.63864929498102 threshold 0.5725505573529426,50,0.63864929498102,0.5725505573529426,0.19154788697174296,0,None,i7186,24,0.006004834541968825 1727482326,1727482353,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5849511929773624 threshold 0.5712551006511744,50,0.5849511929773624,0.5712551006511744,0.1955488872218054,0,None,i7186,23,0.005873178821021045 1727482344,1727482371,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33973233821225485 threshold 0.5288146729202313,50,0.33973233821225485,0.5288146729202313,0.18554638659664913,0,None,i7186,23,0.00616708231111832 1727482344,1727482372,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3427968825016947 threshold 0.5,50,0.3427968825016947,0.5,0.18654663665916482,0,None,i7186,24,0.005991629486318948 1727482366,1727482394,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6404569183884092 threshold 0.5,50,0.6404569183884092,0.5,0.18979744936234055,0,None,i7186,23,0.005871597769572264 1727482366,1727482394,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4695048220194372 threshold 0.5,50,0.4695048220194372,0.5,0.18529632408102026,0,None,i7186,24,0.005930053942056942 1727482386,1727482414,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727482887,1727482915,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4231560099916285 threshold 0.6790478517466294,50,0.4231560099916285,0.6790478517466294,0.1812953238309577,0,None,i7186,24,0.006397432691506211 1727482908,1727482934,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3405626419222533 threshold 0.5216719135253592,50,0.3405626419222533,0.5216719135253592,0.18354588647161796,0,None,i7186,23,0.005952786898023206 1727482919,1727482948,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4175074330421653 threshold 0.8,50,0.4175074330421653,0.8,0.19079769942485625,0,None,i7186,25,0.007645979291433027 1727482927,1727482954,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6896567000094149 threshold 0.5,50,0.6896567000094149,0.5,0.18929732433108282,0,None,i7186,24,0.005878092899848338 1727482947,1727482974,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1937984496124031,0,None,i7186,23,0.006138520931602763 1727482967,1727482995,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 60 recent_samples_proportion 0.9985410466818094 threshold 0.5003237027403955,60,0.9985410466818094,0.5003237027403955,0.19604901225306326,0,None,i7186,24,0.007077166116926057 1727482967,1727482996,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5115397657957573 threshold 0.5884619477783659,50,0.5115397657957573,0.5884619477783659,0.1822955738934734,0,None,i7186,25,0.0062110122125125875 1727482980,1727483007,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6130316773691609 threshold 0.6632021743892176,50,0.6130316773691609,0.6632021743892176,0.1857964491122781,0,None,i7186,24,0.0065159146929589535 1727483007,1727483035,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.352444484561499 threshold 0.8,50,0.352444484561499,0.8,0.18729682420605154,0,None,i7186,24,0.0074526828428418575 1727483007,1727483035,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1930482620655164,0,None,i7186,24,0.006148797473340938 1727483027,1727483054,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.21006145647874833 threshold 0.5937910483986288,50,0.21006145647874833,0.5937910483986288,0.19454863715928983,0,None,i7186,24,0.006045430276488041 1727483041,1727483069,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727483047,1727483074,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 58 recent_samples_proportion 0.5841986746971402 threshold 0.5,58,0.5841986746971402,0.5,0.19179794948737183,0,None,i7186,24,0.006718097434806463 1727483067,1727483094,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.29729247683320387 threshold 0.6784138618465075,50,0.29729247683320387,0.6784138618465075,0.18779694923730927,0,None,i7186,24,0.006136669302460751 1727483087,1727483115,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3796071877566374 threshold 0.7124584165632057,50,0.3796071877566374,0.7124584165632057,0.19004751187796953,0,None,i7186,24,0.006645043613844637 1727483814,1727483843,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.38936199105417346 threshold 0.7810936012438388,50,0.38936199105417346,0.7810936012438388,0.18429607401850467,0,None,i7186,24,0.00726372069207778 1727483826,1727483853,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5885403857409661 threshold 0.6912969982792212,50,0.5885403857409661,0.6912969982792212,0.1937984496124031,0,None,i7186,24,0.007001750437609402 1727483834,1727483862,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5394205780700845 threshold 0.7365888855194274,50,0.5394205780700845,0.7365888855194274,0.20080020005001253,0,None,i7186,24,0.007876969242310577 1727483854,1727483882,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.38176044616472005 threshold 0.5,50,0.38176044616472005,0.5,0.1857964491122781,0,None,i7186,24,0.005923558811780866 1727483855,1727483883,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5846639426955814 threshold 0.5148843862452819,50,0.5846639426955814,0.5148843862452819,0.1812953238309577,0,None,i7186,24,0.006060725707742726 1727483874,1727483902,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.42380638512404156 threshold 0.755451717956676,50,0.42380638512404156,0.755451717956676,0.2038009502375594,0,None,i7186,24,0.006845461365341335 1727483886,1727483914,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3974775351496673 threshold 0.6725361092246228,50,0.3974775351496673,0.6725361092246228,0.19229807451862968,0,None,i7186,24,0.006423034330011073 1727483915,1727483943,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37287046592033257 threshold 0.5212968479276238,50,0.37287046592033257,0.5212968479276238,0.19254813703425855,0,None,i7186,24,0.005835874553053848 1727483917,1727483944,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.27552947930056615 threshold 0.8,50,0.27552947930056615,0.8,0.18529632408102026,0,None,i7186,24,0.0068151366199758895 1727483934,1727483962,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.49027595055729667 threshold 0.681958557589167,50,0.49027595055729667,0.681958557589167,0.1840460115028757,0,None,i7186,24,0.006448795297415904 1727483947,1727483974,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 96 recent_samples_proportion 0.42636833813841934 threshold 0.5585508673230623,96,0.42636833813841934,0.5585508673230623,0.2128032008002001,0,None,i7186,24,0.010727681920480119 1727483975,1727484002,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1678681690711383 threshold 0.8,50,0.1678681690711383,0.8,0.20355088772193053,0,None,i7186,24,0.006446464557315798 1727483975,1727484003,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4208667357706095 threshold 0.7025822263085353,50,0.4208667357706095,0.7025822263085353,0.18104526131532883,0,None,i7186,24,0.006400905782001056 1727483995,1727484022,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.39137933964299554 threshold 0.7625012229512895,50,0.39137933964299554,0.7625012229512895,0.19429857464366096,0,None,i7186,24,0.007104950840884823 1727484007,1727484034,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3653001306747454 threshold 0.5312446824499887,50,0.3653001306747454,0.5312446824499887,0.1885471367841961,0,None,i7186,23,0.005965307116252747 1727484035,1727484062,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8419775395602821 threshold 0.5,50,0.8419775395602821,0.5,0.18554638659664913,0,None,i7186,24,0.006004790671352049 1727484731,1727484759,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37674111747067207 threshold 0.5,50,0.37674111747067207,0.5,0.19504876219054768,0,None,i7186,24,0.005879759413537594 1727484751,1727484778,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3660274565933963 threshold 0.552523008336157,50,0.3660274565933963,0.552523008336157,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727484763,1727484790,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7193965300527149 threshold 0.5,50,0.7193965300527149,0.5,0.18654663665916482,0,None,i7186,24,0.005913816116366754 1727484771,1727484799,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3706111702218796 threshold 0.6574526273780111,50,0.3706111702218796,0.6574526273780111,0.19429857464366096,0,None,i7186,24,0.006216831985774221 1727484791,1727484818,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40323948266972187 threshold 0.5331283380917279,50,0.40323948266972187,0.5331283380917279,0.18279569892473113,0,None,i7186,23,0.005962529593437321 1727484811,1727484839,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.46102142252577694 threshold 0.7453779582714993,50,0.46102142252577694,0.7453779582714993,0.19629907476869213,0,None,i7186,24,0.006855560043857119 1727484823,1727484851,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.8,50,0.1,0.8,0.19879969992498125,0,None,i7186,24,0.0064218953289046895 1727484852,1727484879,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2565560320967537 threshold 0.7964536434736063,50,0.2565560320967537,0.7964536434736063,0.19804951237809454,0,None,i7186,24,0.006624790525989706 1727484852,1727484880,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3691529486845627 threshold 0.5,50,0.3691529486845627,0.5,0.18629657414353584,0,None,i7186,24,0.00607485204634492 1727484872,1727484900,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5959495701221363 threshold 0.6582406845898823,50,0.5959495701221363,0.6582406845898823,0.1975493873468367,0,None,i7186,24,0.006440015801051712 1727484883,1727484911,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.10001565322182378 threshold 0.7999974583798259,74,0.10001565322182378,0.7999974583798259,0.2065516379094774,0,None,i7186,24,0.009262954036381435 1727484892,1727484919,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34792859604691884 threshold 0.5,50,0.34792859604691884,0.5,0.18629657414353584,0,None,i7186,24,0.005994919782577224 1727484912,1727484939,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41432776746073763 threshold 0.613986597244317,50,0.41432776746073763,0.613986597244317,0.18929732433108282,0,None,i7186,24,0.006034842043844294 1727484932,1727484959,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727484952,1727484978,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18554638659664913,0,None,i7186,23,0.006084854546970076 1727484972,1727484999,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.10000000000000002 threshold 0.7241539514452198,50,0.10000000000000002,0.7241539514452198,0.20055013753438355,0,None,i7186,24,0.006216342818098891 1727485640,1727485668,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41102017593433415 threshold 0.6604800381249532,50,0.41102017593433415,0.6604800381249532,0.19904976244061012,0,None,i7186,24,0.006813241771981457 1727485657,1727485687,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 178 recent_samples_proportion 0.9031848964014916 threshold 0.5392123745952422,178,0.9031848964014916,0.5392123745952422,0.2673168292073018,0,None,i7186,26,0.017837792781528715 1727485670,1727485696,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.508884676785356,50,0.1,0.508884676785356,0.1965491372843211,0,None,i7186,23,0.005860017635987944 1727485697,1727485725,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,24,0.005959282028299282 1727485700,1727485727,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.36467201221585643 threshold 0.5660661622762874,50,0.36467201221585643,0.5660661622762874,0.19129782445611399,0,None,i7186,23,0.005929113857411722 1727485730,1727485758,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37784760592369104 threshold 0.5821912492923186,50,0.37784760592369104,0.5821912492923186,0.1982995748937234,0,None,i7186,24,0.005836985562180019 1727485737,1727485764,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3528896296635192 threshold 0.5,50,0.3528896296635192,0.5,0.18379594898724683,0,None,i7186,24,0.005949539332885169 1727485757,1727485784,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9038060681213945 threshold 0.5198039771846552,50,0.9038060681213945,0.5198039771846552,0.19179794948737183,0,None,i7186,23,0.006001500375093774 1727485777,1727485805,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8782604122349607 threshold 0.5109858827338278,50,0.8782604122349607,0.5109858827338278,0.1867966991747937,0,None,i7186,24,0.0059883391900606734 1727485790,1727485818,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.437450948215406 threshold 0.5407346990232313,50,0.437450948215406,0.5407346990232313,0.19429857464366096,0,None,i7186,24,0.005968158706343252 1727485797,1727485824,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6248296009065978,50,0.1,0.6248296009065978,0.18954738684671169,0,None,i7186,24,0.006031507876969242 1727485818,1727485845,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.511426498791175,50,1,0.511426498791175,0.1830457614403601,0,None,i7186,24,0.005959282028299282 1727485837,1727485864,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4570006394796051 threshold 0.5299613988724613,50,0.4570006394796051,0.5299613988724613,0.18729682420605154,0,None,i7186,23,0.00590407342095264 1727485851,1727485878,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34201504064136956 threshold 0.6198461007808947,50,0.34201504064136956,0.6198461007808947,0.1850462615653914,0,None,i7186,24,0.006173840757486668 1727485877,1727485904,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1772297602455301 threshold 0.6547476168431772,50,0.1772297602455301,0.6547476168431772,0.1985496374093524,0,None,i7186,23,0.005833695265921743 1727485877,1727485904,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5424403889432943 threshold 0.6673583099137017,50,0.5424403889432943,0.6673583099137017,0.18879719929982497,0,None,i7186,24,0.006970973512608921 1727486718,1727486746,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6864588133786765 threshold 0.5219530757809974,50,0.6864588133786765,0.5219530757809974,0.18904726181545384,0,None,i7186,23,0.005881340464986376 1727486738,1727486765,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31857361845939214 threshold 0.526994221643043,50,0.31857361845939214,0.526994221643043,0.18654663665916482,0,None,i7186,23,0.005991629486318948 1727486756,1727486784,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28009073489720426 threshold 0.6239680097726293,50,0.28009073489720426,0.6239680097726293,0.18329582395598898,0,None,i7186,24,0.006034403337676525 1727486778,1727486806,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.35009120992796616 threshold 0.6781713728787844,50,0.35009120992796616,0.6781713728787844,0.17829457364341084,0,None,i7186,24,0.006265079783459379 1727486786,1727486813,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5307947382262235,50,1,0.5307947382262235,0.18829707426856712,0,None,i7186,23,0.006048178711344503 1727486799,1727486827,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41951425323837 threshold 0.6160505221442409,50,0.41951425323837,0.6160505221442409,0.1885471367841961,0,None,i7186,24,0.00604484454446945 1727486817,1727486844,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40370685860369826 threshold 0.7670366392471177,50,0.40370685860369826,0.7670366392471177,0.1850462615653914,0,None,i7186,24,0.007028680246984822 1727486839,1727486866,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.35498775671098803 threshold 0.651791281222187,50,0.35498775671098803,0.651791281222187,0.1795448862215554,0,None,i7186,24,0.00608375778155065 1727486859,1727486886,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8791444221663681 threshold 0.5524552214042335,50,0.8791444221663681,0.5524552214042335,0.18354588647161796,0,None,i7186,24,0.0060311130414182484 1727486877,1727486905,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41279659475519015 threshold 0.7378246720740269,50,0.41279659475519015,0.7378246720740269,0.19979994998749684,0,None,i7186,24,0.006801700425106277 1727486899,1727486926,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6039060355859797 threshold 0.5,50,0.6039060355859797,0.5,0.1867966991747937,0,None,i7186,24,0.006068183712594815 1727486907,1727486935,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4303242698394045 threshold 0.7855323660710031,50,0.4303242698394045,0.7855323660710031,0.20230057514378597,0,None,i7186,24,0.007992907317738525 1727486919,1727486947,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28755927445924784 threshold 0.5,50,0.28755927445924784,0.5,0.1867966991747937,0,None,i7186,25,0.0059883391900606734 1727486937,1727486965,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4014523749426001 threshold 0.8,50,0.4014523749426001,0.8,0.21380345086271568,0,None,i7186,25,0.009306674494710634 1727486959,1727486987,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.23548900621096752 threshold 0.6946760456283194,50,0.23548900621096752,0.6946760456283194,0.1812953238309577,0,None,i7186,24,0.0063097966272390015 1727486979,1727487006,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.29088169138071823 threshold 0.7160741554055337,50,0.29088169138071823,0.7160741554055337,0.19079769942485625,0,None,i7186,23,0.00635370110133167 1727487740,1727487770,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5384457802583775,50,1,0.5384457802583775,0.18379594898724683,0,None,i7186,24,0.00610819371509544 1727487760,1727487787,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.19104776194048512,0,None,i7186,23,0.006092739401066483 1727487780,1727487808,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34328355192036397 threshold 0.7217843793269303,50,0.34328355192036397,0.7217843793269303,0.20130032508127027,0,None,i7186,25,0.0065762709334049934 1727487782,1727487809,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727487800,1727487827,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727487820,1727487848,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3269178821422286 threshold 0.6969045494794422,50,0.3269178821422286,0.6969045494794422,0.19354838709677424,0,None,i7186,24,0.006314959021445501 1727487840,1727487867,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.345656108585777 threshold 0.5401330102254789,50,0.345656108585777,0.5401330102254789,0.18804701175293825,0,None,i7186,23,0.005971887708769297 1727487860,1727487888,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9622669550984033 threshold 0.5316483454550893,50,0.9622669550984033,0.5316483454550893,0.19054763690922727,0,None,i7186,24,0.006268928343196911 1727487880,1727487907,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6065012253691567 threshold 0.6258051492689702,50,0.6065012253691567,0.6258051492689702,0.19254813703425855,0,None,i7186,23,0.0060724640619614365 1727487900,1727487928,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3387196211142312 threshold 0.6977945638812888,50,0.3387196211142312,0.6977945638812888,0.18429607401850467,0,None,i7186,24,0.006268690460286303 1727487920,1727487947,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727487933,1727487963,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 177 recent_samples_proportion 0.913558493095205 threshold 0.5422545820430295,177,0.913558493095205,0.5422545820430295,0.2605651412853214,0,None,i7186,27,0.01815930173019445 1727487940,1727487968,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6533678634003104 threshold 0.5145909196654488,50,0.6533678634003104,0.5145909196654488,0.18879719929982497,0,None,i7186,24,0.006041510377594398 1727487960,1727487987,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.22588735362897447 threshold 0.683044326531255,50,0.22588735362897447,0.683044326531255,0.1740435108777194,0,None,i7186,23,0.006409136530708019 1727487980,1727488008,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 77 recent_samples_proportion 0.6535823135910518 threshold 0.5,77,0.6535823135910518,0.5,0.20405101275318827,0,None,i7186,24,0.008935907446249319 1727488000,1727488027,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28999280757467594 threshold 0.5796874625793784,50,0.28999280757467594,0.5796874625793784,0.18629657414353584,0,None,i7186,23,0.005994919782577224 1727488947,1727488977,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31145127532671424 threshold 0.6198308343777785,50,0.31145127532671424,0.6198308343777785,0.18204551137784442,0,None,i7186,24,0.006131532883220806 1727488967,1727488995,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 82 recent_samples_proportion 0.5609714213021512 threshold 0.5075108940594165,82,0.5609714213021512,0.5075108940594165,0.20180045011252812,0,None,i7186,24,0.009364043138444186 1727488987,1727489015,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2625330712708157 threshold 0.5610416957645299,50,0.2625330712708157,0.5610416957645299,0.1867966991747937,0,None,i7186,24,0.005910568551228716 1727489007,1727489034,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34288176768162215 threshold 0.5189374978023166,50,0.34288176768162215,0.5189374978023166,0.18629657414353584,0,None,i7186,23,0.005994919782577224 1727489047,1727489075,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3318111743036436 threshold 0.5186861572209891,50,0.3318111743036436,0.5186861572209891,0.18354588647161796,0,None,i7186,24,0.0060311130414182484 1727489048,1727489075,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5550167219427695 threshold 0.5337697157399779,50,0.5550167219427695,0.5337697157399779,0.18379594898724683,0,None,i7186,24,0.006275541488111753 1727489067,1727489096,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.30278402170147367 threshold 0.6661306306743805,50,0.30278402170147367,0.6661306306743805,0.18429607401850467,0,None,i7186,25,0.0061015253813453355 1727489079,1727489120,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.643967551631824 threshold 0.63786539064894,50,0.643967551631824,0.63786539064894,0.18604651162790697,0,None,i7173,21,0.006244711862897231 1727489037,1727489119,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7673692344929635 threshold 0.5,50,0.7673692344929635,0.5,0.19129782445611399,0,None,i7173,21,0.005929113857411722 1727489107,1727489135,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.006058181211969659 1727489127,1727489154,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2745793815275256 threshold 0.6404603620147495,50,0.2745793815275256,0.6404603620147495,0.18779694923730927,0,None,i7186,23,0.005897578290676566 1727489140,1727489168,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3696629332216427 threshold 0.5,50,0.3696629332216427,0.5,0.18379594898724683,0,None,i7186,24,0.005949539332885169 1727489167,1727489194,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40416207562205975 threshold 0.5,50,0.40416207562205975,0.5,0.1937984496124031,0,None,i7186,23,0.0058196367273636584 1727489188,1727489215,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.913813340303446 threshold 0.5229815120187495,50,0.913813340303446,0.5229815120187495,0.19179794948737183,0,None,i7186,24,0.006001500375093774 1727489201,1727489228,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7369908020608467 threshold 0.5,50,0.7369908020608467,0.5,0.18654663665916482,0,None,i7186,23,0.005913816116366754 1727489228,1727489255,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727490287,1727490315,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5469587916518568 threshold 0.5704942728784945,50,0.5469587916518568,0.5704942728784945,0.19254813703425855,0,None,i7186,24,0.0060724640619614365 1727490307,1727490333,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727490327,1727490354,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5177225609899073,50,1,0.5177225609899073,0.18829707426856712,0,None,i7186,24,0.006048178711344503 1727490347,1727490374,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6026584332961781 threshold 0.5355599543638656,50,0.6026584332961781,0.5355599543638656,0.18929732433108282,0,None,i7186,23,0.006034842043844294 1727490367,1727490394,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.36461539851998703 threshold 0.5,50,0.36461539851998703,0.5,0.18379594898724683,0,None,i7186,24,0.005949539332885169 1727490387,1727490413,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3201065886292117 threshold 0.5237409315126691,50,0.3201065886292117,0.5237409315126691,0.18254563640910226,0,None,i7186,23,0.005965777158575358 1727490407,1727490434,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1902975743935984,0,None,i7186,24,0.006102877070619006 1727490427,1727490454,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5373893822215073 threshold 0.5994042064779346,50,0.5373893822215073,0.5994042064779346,0.1875468867216804,0,None,i7186,23,0.006140048525644925 1727490439,1727490466,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.316961759261637 threshold 0.6430984047176249,50,0.316961759261637,0.6430984047176249,0.18529632408102026,0,None,i7186,24,0.006170461534302494 1727490467,1727490494,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.1982995748937234,0,None,i7186,23,0.005914812036342419 1727490469,1727490495,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5548557369107685,50,1,0.5548557369107685,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727490499,1727490524,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18829707426856712,0,None,i7173,21,0.006048178711344503 1727490507,1727490534,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6847274217743315 threshold 0.5640882030657877,50,0.6847274217743315,0.5640882030657877,0.19054763690922727,0,None,i7186,24,0.006099497847434832 1727490529,1727490556,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28170821139181174 threshold 0.6364221742452267,50,0.28170821139181174,0.6364221742452267,0.18179544886221555,0,None,i7186,23,0.006134867050095858 1727490547,1727490574,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1857964491122781,0,None,i7186,24,0.006081520380095023 1727490567,1727490593,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727491797,1727491830,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6394823834333878 threshold 0.5508439554037999,50,0.6394823834333878,0.5508439554037999,0.18204551137784442,0,None,i7186,24,0.0060508548189679 1727491819,1727491846,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.919810561818984 threshold 0.5395473160580454,50,0.919810561818984,0.5395473160580454,0.19079769942485625,0,None,i7186,23,0.006014837042593982 1727491827,1727491854,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31519943646614024 threshold 0.6271067530834995,50,0.31519943646614024,0.6271067530834995,0.18979744936234055,0,None,i7186,24,0.005948855634961372 1727491839,1727491866,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9252694026932481 threshold 0.5,50,0.9252694026932481,0.5,0.18879719929982497,0,None,i7186,24,0.005962016819994472 1727491857,1727491884,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727491879,1727491906,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4354832807889343 threshold 0.5,50,0.4354832807889343,0.5,0.18054513628407098,0,None,i7186,24,0.00599175767967966 1727491899,1727491925,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6557362834140061 threshold 0.5497390446721939,50,0.6557362834140061,0.5497390446721939,0.1847961990497624,0,None,i7186,23,0.006094857047595232 1727491917,1727491945,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,24,0.005959282028299282 1727491939,1727491966,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2926630761946938 threshold 0.545143764170564,50,0.2926630761946938,0.545143764170564,0.18454613653413354,0,None,i7186,23,0.006017951856385148 1727491959,1727491987,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5553114278815791 threshold 0.7066521530665415,50,0.5553114278815791,0.7066521530665415,0.1930482620655164,0,None,i7186,24,0.006600914934616006 1727491977,1727492004,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727491999,1727492026,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33684647458298045 threshold 0.553131371199966,50,0.33684647458298045,0.553131371199966,0.18604651162790697,0,None,i7186,24,0.005998210078835498 1727492019,1727492046,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.29667888864629793 threshold 0.5348792365886534,50,0.29667888864629793,0.5348792365886534,0.1867966991747937,0,None,i7186,23,0.005910568551228716 1727492038,1727492067,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 102 recent_samples_proportion 1 threshold 0.5,102,1,0.5,0.21580395098774696,0,None,i7186,25,0.011836292406434941 1727492059,1727492085,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5060578126431687 threshold 0.605985501013662,50,0.5060578126431687,0.605985501013662,0.19004751187796953,0,None,i7186,23,0.006024839543219138 1727492079,1727492106,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2841932361255311 threshold 0.6300160410102678,50,0.2841932361255311,0.6300160410102678,0.18329582395598898,0,None,i7186,24,0.006034403337676525 1727492098,1727492125,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8520842628595976 threshold 0.5,50,0.8520842628595976,0.5,0.1902975743935984,0,None,i7186,23,0.006360745115856429 1727493306,1727493337,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6653626834187037 threshold 0.5148609412770736,50,0.6653626834187037,0.5148609412770736,0.1857964491122781,0,None,i7186,24,0.006163703087934145 1727493333,1727493360,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.1992998249562391,0,None,i7186,23,0.00590147536884221 1727493333,1727493360,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.19179794948737183,0,None,i7186,24,0.00608260173151396 1727493353,1727493381,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.932998203013783 threshold 0.5150346706878379,50,0.932998203013783,0.5150346706878379,0.18654663665916482,0,None,i7186,24,0.005991629486318948 1727493373,1727493400,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.19179794948737183,0,None,i7186,23,0.006165925042904562 1727493393,1727493421,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727493413,1727493440,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.956375570628997 threshold 0.5393283984971398,50,0.956375570628997,0.5393283984971398,0.18804701175293825,0,None,i7186,23,0.006051512878219555 1727493433,1727493461,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727493453,1727493480,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.883234524260767 threshold 0.5,50,0.883234524260767,0.5,0.18829707426856712,0,None,i7186,23,0.005968597412511023 1727493487,1727493514,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28479180016060246 threshold 0.6536043169062564,50,0.28479180016060246,0.6536043169062564,0.19279819954988742,0,None,i7186,23,0.006069084838777263 1727493513,1727493542,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9666554403633997 threshold 0.5368431731089262,50,0.9666554403633997,0.5368431731089262,0.1902975743935984,0,None,i7186,25,0.0062724014336917565 1727493517,1727493544,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7218401845032322 threshold 0.5339494407422142,50,0.7218401845032322,0.5339494407422142,0.18654663665916482,0,None,i7186,23,0.005913816116366754 1727493533,1727493561,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6650301904722475 threshold 0.5446607538241102,50,0.6650301904722475,0.5446607538241102,0.18804701175293825,0,None,i7186,24,0.005894330725538527 1727493553,1727493580,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727493578,1727493605,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.30347704663317754 threshold 0.63261033089349,50,0.30347704663317754,0.63261033089349,0.18629657414353584,0,None,i7186,24,0.006241286348984507 1727493608,1727493635,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2929915571228065 threshold 0.6461902930562616,50,0.2929915571228065,0.6461902930562616,0.18029507376844212,0,None,i7186,23,0.006411325053485593 1727493634,1727493661,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7735648650356958 threshold 0.519622155646339,50,0.7735648650356958,0.519622155646339,0.19404851212803198,0,None,i7186,24,0.005892920598570696 1727494925,1727494958,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2858445841195062 threshold 0.6748939930506496,50,0.2858445841195062,0.6748939930506496,0.18704676169042256,0,None,i7186,24,0.006317551610124754 1727494937,1727494964,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5354400599695959 threshold 0.6270237912647312,50,0.5354400599695959,0.6270237912647312,0.18929732433108282,0,None,i7186,24,0.006286293795671139 1727494965,1727494992,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3839767105200157 threshold 0.7012592122453329,50,0.3839767105200157,0.7012592122453329,0.1830457614403601,0,None,i7186,24,0.006748010532044775 1727494967,1727494994,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727494997,1727495024,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.756563130826383 threshold 0.5311522269494414,50,0.756563130826383,0.5311522269494414,0.18979744936234055,0,None,i7186,23,0.005948855634961372 1727495025,1727495051,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.006037693633934799 1727495045,1727495073,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.47028015773965137 threshold 0.6611122699753189,50,0.47028015773965137,0.6611122699753189,0.18554638659664913,0,None,i7186,24,0.006519487014610796 1727495058,1727495085,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5103827601554999 threshold 0.5,50,0.5103827601554999,0.5,0.18704676169042256,0,None,i7186,23,0.005907320986090679 1727495085,1727495112,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7689224785416566 threshold 0.5610893332504313,50,0.7689224785416566,0.5610893332504313,0.18029507376844212,0,None,i7186,23,0.0063234986828899 1727495105,1727495132,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7537949613230904 threshold 0.5803172802153678,50,0.7537949613230904,0.5803172802153678,0.19104776194048512,0,None,i7186,23,0.006261982162207218 1727495118,1727495146,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 59 recent_samples_proportion 0.47235954923234724 threshold 0.5,59,0.47235954923234724,0.5,0.18329582395598898,0,None,i7186,24,0.006948706873688119 1727495145,1727495171,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31441515507478346 threshold 0.5185254123698452,50,0.31441515507478346,0.5185254123698452,0.18604651162790697,0,None,i7186,23,0.00592031124664283 1727495165,1727495192,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727495179,1727495206,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 61 recent_samples_proportion 0.8560105607337065 threshold 0.5,61,0.8560105607337065,0.5,0.19779944986246567,0,None,i7186,23,0.007163081092853857 1727495205,1727495232,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727495225,1727495251,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6773007929925104 threshold 0.6154817538966176,50,0.6773007929925104,0.6154817538966176,0.19229807451862968,0,None,i7186,23,0.00607584328514561 1727496808,1727496837,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727496828,1727496855,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4057225715247612 threshold 0.5,50,0.4057225715247612,0.5,0.19279819954988742,0,None,i7186,23,0.00583262698791581 1727496838,1727496865,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5357388894313521,50,1,0.5357388894313521,0.19154788697174296,0,None,i7186,24,0.006169350556817286 1727496849,1727496876,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18954738684671169,0,None,i7186,24,0.006196754668119084 1727496889,1727496916,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.26255445265440985 threshold 0.5604827436652549,50,0.26255445265440985,0.5604827436652549,0.18529632408102026,0,None,i7186,24,0.006008080967610323 1727496899,1727496926,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.611605446699628 threshold 0.6379407404253734,50,0.611605446699628,0.6379407404253734,0.18729682420605154,0,None,i7186,24,0.006314078519629907 1727496929,1727496956,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.2933280035187697 threshold 0.6968734460488227,62,0.2933280035187697,0.6968734460488227,0.1955488872218054,0,None,i7186,24,0.007970742685671418 1727496949,1727496975,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6562503506990943 threshold 0.5978272161109048,50,0.6562503506990943,0.5978272161109048,0.19329832458114526,0,None,i7186,23,0.006230724347753605 1727496959,1727496985,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.789909697844259 threshold 0.6237519814751119,51,0.789909697844259,0.6237519814751119,0.20005001250312582,0,None,i7186,23,0.006223386832623648 1727496989,1727497017,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18379594898724683,0,None,i7186,24,0.006027822745159974 1727497009,1727497036,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6130055964332203 threshold 0.6636737935577695,50,0.6130055964332203,0.6636737935577695,0.19279819954988742,0,None,i7186,23,0.006415889686707392 1727497029,1727497056,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6758676003517065 threshold 0.5957303143112245,50,0.6758676003517065,0.5957303143112245,0.19154788697174296,0,None,i7186,24,0.0062550359812175264 1727497049,1727497076,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5950777878733501 threshold 0.6205404140228907,50,0.5950777878733501,0.6205404140228907,0.1840460115028757,0,None,i7186,23,0.00627211597419903 1727497069,1727497096,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.30837373358965053 threshold 0.6551605440060757,50,0.30837373358965053,0.6551605440060757,0.1822955738934734,0,None,i7186,24,0.006383540329526826 1727497109,1727497136,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3560898839965324 threshold 0.5,50,0.3560898839965324,0.5,0.18554638659664913,0,None,i7186,23,0.006004790671352049 1727497129,1727497155,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727497157,1727497234,77,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7337780103006868 threshold 0.601991070324807,50,0.7337780103006868,0.601991070324807,0.18704676169042256,0,None,i7173,21,0.0065922277670867 1727498654,1727498682,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3038117011070851 threshold 0.6543630247319158,50,0.3038117011070851,0.6543630247319158,0.18954738684671169,0,None,i7186,24,0.006113014740171529 1727498694,1727498721,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6200194211067278 threshold 0.5,50,0.6200194211067278,0.5,0.17829457364341084,0,None,i7186,23,0.0061002092628420265 1727498707,1727498734,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6477873849964915 threshold 0.5347505336955515,50,0.6477873849964915,0.5347505336955515,0.18879719929982497,0,None,i7186,23,0.006041510377594398 1727498734,1727498762,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.556754596162072 threshold 0.5272988611757707,50,0.556754596162072,0.5272988611757707,0.19604901225306326,0,None,i7186,24,0.005866598228504495 1727498737,1727498764,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31069823101893446 threshold 0.6502312187005865,50,0.31069823101893446,0.6502312187005865,0.18204551137784442,0,None,i7186,23,0.006131532883220806 1727498767,1727498795,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,24,0.005959282028299282 1727498794,1727498821,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3221912823135967 threshold 0.5,50,0.3221912823135967,0.5,0.19129782445611399,0,None,i7186,23,0.0058521123787440375 1727498814,1727498841,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.42653134846218366 threshold 0.5,50,0.42653134846218366,0.5,0.18329582395598898,0,None,i7186,24,0.006034403337676525 1727498834,1727498860,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8448302837840902 threshold 0.5115579331680351,50,0.8448302837840902,0.5115579331680351,0.18529632408102026,0,None,i7186,23,0.006008080967610323 1727498854,1727498882,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3154428944811374 threshold 0.5448116008139154,50,0.3154428944811374,0.5448116008139154,0.18929732433108282,0,None,i7186,24,0.005878092899848338 1727498874,1727498901,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18879719929982497,0,None,i7186,23,0.006041510377594398 1727498915,1727498942,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,24,0.005959282028299282 1727498934,1727498961,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7279975317531981 threshold 0.5146932427139869,50,0.7279975317531981,0.5146932427139869,0.18654663665916482,0,None,i7186,23,0.005913816116366754 1727498948,1727498976,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6555996485190038 threshold 0.5,50,0.6555996485190038,0.5,0.18979744936234055,0,None,i7186,24,0.005871597769572264 1727498974,1727499001,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 54 recent_samples_proportion 0.589170756138264 threshold 0.5392465576788444,54,0.589170756138264,0.5392465576788444,0.18179544886221555,0,None,i7186,23,0.006390486510516518 1727498994,1727499022,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.30151785521063307 threshold 0.6275870044257089,50,0.30151785521063307,0.6275870044257089,0.18879719929982497,0,None,i7186,24,0.006123152409724052 1727500657,1727500685,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727500697,1727500724,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8669106372990971 threshold 0.5210621318719337,50,0.8669106372990971,0.5210621318719337,0.19154788697174296,0,None,i7186,24,0.006085980954698133 1727500717,1727500746,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 53 recent_samples_proportion 0.336449317292862 threshold 0.5554306287837384,53,0.336449317292862,0.5554306287837384,0.18929732433108282,0,None,i7186,23,0.0062001801820318085 1727500727,1727500753,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,23,0.005978468301285848 1727500757,1727500785,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 58 recent_samples_proportion 0.2897742332759138 threshold 0.6749909033022283,58,0.2897742332759138,0.6749909033022283,0.19679919979994998,0,None,i7186,24,0.006847865812606998 1727500777,1727500804,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9392608946999901 threshold 0.5,50,0.9392608946999901,0.5,0.1867966991747937,0,None,i7186,23,0.006068183712594815 1727500797,1727500826,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.18729682420605154,0,None,i7186,23,0.006061515378844711 1727500817,1727500843,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.44031986389597355 threshold 0.5,50,0.44031986389597355,0.5,0.1830457614403601,0,None,i7186,23,0.006037693633934799 1727500847,1727500874,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727500877,1727500904,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.42264090328545045 threshold 0.5,50,0.42264090328545045,0.5,0.19354838709677424,0,None,i7186,23,0.005822884292501696 1727500897,1727500924,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4168959150448178 threshold 0.5,50,0.4168959150448178,0.5,0.18279569892473113,0,None,i7186,23,0.005962529593437321 1727500917,1727500944,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6180328010862445 threshold 0.6047344912089098,50,0.6180328010862445,0.6047344912089098,0.1902975743935984,0,None,i7186,23,0.006021505376344086 1727500937,1727500965,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1830457614403601,0,None,i7186,23,0.005959282028299282 1727500957,1727500984,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31034955606171766 threshold 0.6439572950178059,50,0.31034955606171766,0.6439572950178059,0.18379594898724683,0,None,i7186,23,0.00610819371509544 1727500998,1727501025,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1875468867216804,0,None,i7186,24,0.005978468301285848 1727501018,1727501044,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.1902975743935984,0,None,i7186,23,0.006102877070619006
timestamp,ram_usage_mb,cpu_usage_percent 1727476458,473.08984375,49.8 1727476458,473.08984375,54.0 1727476458,473.08984375,49.7 1727476458,473.08984375,55.3 1727476458,473.08984375,37.5 1727476458,473.08984375,50.3 1727476458,473.08984375,40.6 1727476505,478.9609375,49.8 1727476505,478.9609375,54.3 1727476505,478.9609375,49.7 1727476505,478.9609375,39.4 1727476508,479.078125,49.9 1727476508,479.078125,54.3 1727476508,479.078125,47.6 1727476508,479.078125,58.7 1727476510,479.078125,49.9 1727476510,479.078125,39.4 1727476510,479.078125,54.2 1727476510,479.078125,42.4 1727476513,479.078125,49.9 1727476513,479.078125,48.7 1727476513,479.078125,49.0 1727476513,479.078125,57.8 1727476515,479.125,49.8 1727476515,479.125,53.1 1727476515,479.125,52.5 1727476515,479.125,40.6 1727476518,479.1796875,49.9 1727476518,479.1796875,39.4 1727476518,479.1796875,52.6 1727476518,479.1796875,44.4 1727476520,479.1796875,49.8 1727476520,479.1796875,54.3 1727476520,479.1796875,49.1 1727476520,479.1796875,55.6 1727476523,479.1796875,49.8 1727476523,479.1796875,56.5 1727476523,479.1796875,48.6 1727476523,479.1796875,58.1 1727476525,479.1796875,49.9 1727476525,479.1796875,54.3 1727476525,479.1796875,48.6 1727476525,479.1796875,56.5 1727476527,479.1796875,49.8 1727476527,479.1796875,54.3 1727476527,479.1796875,49.1 1727476527,479.1796875,40.0 1727476529,479.1796875,49.9 1727476529,479.1796875,46.2 1727476529,479.1796875,49.1 1727476529,479.1796875,55.8 1727476532,479.2578125,49.9 1727476532,479.2578125,55.3 1727476532,479.2578125,48.1 1727476532,479.2578125,57.8 1727476534,479.2578125,49.9 1727476534,479.2578125,47.4 1727476534,479.2578125,50.9 1727476534,479.2578125,40.6 1727476536,479.2578125,49.9 1727476536,479.2578125,57.4 1727476536,479.2578125,48.1 1727476536,479.2578125,45.9 1727476539,479.2890625,49.9 1727476539,479.2890625,39.4 1727476539,479.2890625,53.3 1727476539,479.2890625,38.7 1727476541,479.2890625,49.8 1727476541,479.2890625,52.1 1727476541,479.2890625,53.7 1727476541,479.2890625,38.7 1727476543,481.33984375,49.9 1727476543,481.33984375,56.2 1727476543,481.33984375,46.7 1727476543,481.33984375,56.8 1727476546,481.33984375,49.8 1727476546,481.33984375,55.3 1727476546,481.33984375,52.5 1727476546,481.33984375,39.4 1727476548,481.359375,49.9 1727476548,481.359375,44.7 1727476548,481.359375,52.0 1727476548,481.359375,40.6 1727476551,481.3671875,49.9 1727476551,481.3671875,55.6 1727476551,481.3671875,49.9 1727476551,481.3671875,40.6 1727476553,481.3671875,49.9 1727476553,481.3671875,37.5 1727476553,481.3671875,52.1 1727476553,481.3671875,41.9 1727476555,481.3671875,49.8 1727476555,481.3671875,55.6 1727476555,481.3671875,48.2 1727476555,481.3671875,51.2 1727476557,481.3671875,49.9 1727476557,481.3671875,38.2 1727476557,481.3671875,50.3 1727476557,481.3671875,58.7 1727476560,481.3671875,49.8 1727476560,481.3671875,39.4 1727476560,481.3671875,51.6 1727476560,481.3671875,55.6 1727476562,481.3671875,49.9 1727476562,481.3671875,36.4 1727476562,481.3671875,51.6 1727476562,481.3671875,58.7 1727476564,481.375,49.8 1727476564,481.375,45.9 1727476564,481.375,50.4 1727476564,481.375,56.8 1727476566,481.37890625,49.9 1727476566,481.37890625,39.4 1727476566,481.37890625,52.8 1727476566,481.37890625,40.6 1727476568,481.37890625,49.8 1727476568,481.37890625,38.2 1727476568,481.37890625,52.0 1727476568,481.37890625,40.6 1727476570,481.37890625,49.8 1727476570,481.37890625,55.6 1727476570,481.37890625,47.3 1727476570,481.37890625,55.6 1727476572,481.37890625,49.8 1727476572,481.37890625,53.2 1727476572,481.37890625,46.4 1727476572,481.37890625,56.5 1727476575,481.37890625,49.9 1727476575,481.37890625,41.2 1727476575,481.37890625,49.6 1727476575,481.37890625,56.3 1727476577,481.37890625,49.8 1727476577,481.37890625,55.3 1727476577,481.37890625,46.4 1727476577,481.37890625,56.5 1727476579,481.37890625,49.9 1727476579,481.37890625,40.0 1727476579,481.37890625,52.4 1727476579,481.37890625,40.6 1727476581,481.37890625,49.9 1727476581,481.37890625,37.1 1727476581,481.37890625,52.0 1727476581,481.37890625,48.8 1727476583,481.37890625,49.9 1727476583,481.37890625,54.3 1727476583,481.37890625,52.0 1727476583,481.37890625,37.1 1727476585,481.37890625,49.9 1727476585,481.37890625,55.3 1727476585,481.37890625,50.8 1727476585,481.37890625,42.4 1727476587,481.37890625,49.8 1727476587,481.37890625,54.3 1727476587,481.37890625,51.2 1727476587,481.37890625,40.6 1727476590,481.37890625,49.9 1727476590,481.37890625,40.0 1727476590,481.37890625,51.2 1727476590,481.37890625,56.8 1727476592,481.37890625,49.9 1727476592,481.37890625,56.3 1727476592,481.37890625,49.6 1727476592,481.37890625,44.1 1727476594,481.37890625,49.9 1727476594,481.37890625,50.0 1727476594,481.37890625,48.7 1727476594,481.37890625,55.6 1727476596,481.37890625,49.8 1727476596,481.37890625,53.3 1727476596,481.37890625,48.5 1727476596,481.37890625,45.5 1727476598,481.37890625,49.8 1727476598,481.37890625,38.2 1727476598,481.37890625,51.6 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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 (2024-09-28 00:34:22,123) - Starting with JobEnvironment(job_id=2267512, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-28 00:34:22,125) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/DiscriminativeDriftDetector2019_OutdoorObjects/0/single_runs/2267512/2267512_submitted.pkl
Parameters: {"n_reference_samples": 51, "recent_samples_proportion": 0.8468098700046539, "threshold": 0.5229895263910294}
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 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.8468098700046539 threshold 0.5229895263910294
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: 1727476345.0
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: 1727476345.0
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 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.8468098700046539 threshold 0.5229895263910294
stdout:
RESULT: 0.19229807451862968
OO-Info: runtime: 25
OO-Info: lpd: 0.006159074015079112
Result: 0.19229807451862968
EXIT_CODE: 0
submitit INFO (2024-09-28 00:34:53,208) - Job completed successfully
submitit INFO (2024-09-28 00:34:53,211) - Exiting after successful completion
Job-Infos
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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 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: :-webkit-scrollbar-button: vertical: end{
height: 17px;
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}
: :-webkit-scrollbar-button: horizontal: start{
width: 17px;
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}
: :-webkit-scrollbar-button: horizontal: end{
width: 17px;
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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
menu[role=tablist] button{
background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
margin-left: -1px;
margin-right: 2px;
border-radius: 0;
border-color: #91a7b4;
border-top-right-radius: 3px;
border-top-left-radius: 3px;
padding: 0 12px 3px
}
menu[role=tablist] button: hover{
box-shadow: unset;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]{
border-color: #919b9c;
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]: first-of-type: before{
content: "";
display: block;
position: absolute;
z-index: -1;
top: 100%;
left: -1px;
height: 2px;
width: 0;
border-left: 1px solid #919b9c
}
[role=tabpanel]{
box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
}
ul.tree-view{
-webkit-font-smoothing: auto;
border: 1px solid #7f9db9;
padding: 2px 5px
}
@keyframes sliding{
0%{
transform: translateX(-30px)
}
to{
transform: translateX(100%)
}
}
progress{
box-sizing: border-box;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
height: 14px;
border: 1px solid #686868;
border-radius: 4px;
padding: 1px 2px 1px 0;
overflow: hidden;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress,progress: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
height: 14px
}
progress[value]: :-webkit-progress-bar{
background-color: transparent
}
progress[value]: :-webkit-progress-value{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress[value]: :-moz-progress-bar{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-webkit-progress-bar{
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite
}
progress: not([value]): :-webkit-progress-bar: not([value]){
animation: sliding 2s linear 0s infinite;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]){
position: relative
}
progress: not([value]): before{
box-sizing: border-box;
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before,progress: not([value]): before: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): after{
box-sizing: border-box;
content: "";
position: absolute;
top: 1px;
left: 2px;
width: 100%;
height: calc(100% - 2px);
padding: 1px 2px;
border-radius: 2px;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): after,progress: not([value]): after: not([value]){
animation: sliding 2s linear 0s infinite
}
progress: not([value]): after: not([value]){
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-moz-progress-bar{
width: 100%;
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[
1727476587,
481.37890625,
49.8
],
[
1727476587,
481.37890625,
54.3
],
[
1727476587,
481.37890625,
51.2
],
[
1727476587,
481.37890625,
40.6
],
[
1727476590,
481.37890625,
49.9
],
[
1727476590,
481.37890625,
40
],
[
1727476590,
481.37890625,
51.2
],
[
1727476590,
481.37890625,
56.8
],
[
1727476592,
481.37890625,
49.9
],
[
1727476592,
481.37890625,
56.3
],
[
1727476592,
481.37890625,
49.6
],
[
1727476592,
481.37890625,
44.1
],
[
1727476594,
481.37890625,
49.9
],
[
1727476594,
481.37890625,
50
],
[
1727476594,
481.37890625,
48.7
],
[
1727476594,
481.37890625,
55.6
],
[
1727476596,
481.37890625,
49.8
],
[
1727476596,
481.37890625,
53.3
],
[
1727476596,
481.37890625,
48.5
],
[
1727476596,
481.37890625,
45.5
],
[
1727476598,
481.37890625,
49.8
],
[
1727476598,
481.37890625,
38.2
],
[
1727476598,
481.37890625,
51.6
],
[
1727476598,
481.37890625,
58.7
],
[
1727476600,
481.37890625,
49.8
],
[
1727476600,
481.37890625,
54.3
],
[
1727476600,
481.37890625,
51.6
],
[
1727476600,
481.37890625,
39.4
],
[
1727476602,
481.37890625,
49.8
],
[
1727476602,
481.37890625,
54.3
],
[
1727476602,
481.37890625,
50.8
],
[
1727476602,
481.37890625,
45.7
],
[
1727476604,
481.37890625,
49.9
],
[
1727476604,
481.37890625,
52.1
],
[
1727476604,
481.37890625,
50.8
],
[
1727476604,
481.37890625,
42.4
],
[
1727476606,
481.37890625,
49.8
],
[
1727476606,
481.37890625,
50
],
[
1727476606,
481.37890625,
51.2
],
[
1727476606,
481.37890625,
38.7
],
[
1727476609,
481.37890625,
49.8
],
[
1727476609,
481.37890625,
55.3
],
[
1727476609,
481.37890625,
50.8
],
[
1727476609,
481.37890625,
39.4
],
[
1727476611,
481.38671875,
49.9
],
[
1727476611,
481.38671875,
41.7
],
[
1727476611,
481.38671875,
49.6
],
[
1727476611,
481.38671875,
54.8
],
[
1727476613,
481.38671875,
49.9
],
[
1727476613,
481.38671875,
39.4
],
[
1727476613,
481.38671875,
50.4
],
[
1727476613,
481.38671875,
56.8
],
[
1727476616,
481.46484375,
49.9
],
[
1727476616,
481.46484375,
39.4
],
[
1727476616,
481.46484375,
50.4
],
[
1727476616,
481.46484375,
56.5
],
[
1727476618,
481.46484375,
49.8
],
[
1727476618,
481.46484375,
55.3
],
[
1727476618,
481.46484375,
49.2
],
[
1727476618,
481.46484375,
40.6
],
[
1727476620,
481.4765625,
49.9
],
[
1727476620,
481.4765625,
48.8
],
[
1727476620,
481.4765625,
51.1
],
[
1727476620,
481.4765625,
41.2
],
[
1727476623,
481.515625,
49.9
],
[
1727476623,
481.515625,
55.3
],
[
1727476623,
481.515625,
50
],
[
1727476623,
481.515625,
48.7
],
[
1727476625,
481.515625,
49.9
],
[
1727476625,
481.515625,
38.2
],
[
1727476625,
481.515625,
52.9
],
[
1727476625,
481.515625,
47.2
],
[
1727476627,
481.515625,
49.9
],
[
1727476627,
481.515625,
38.2
],
[
1727476627,
481.515625,
53.1
],
[
1727476627,
481.515625,
37.5
],
[
1727476629,
481.515625,
49.8
],
[
1727476629,
481.515625,
54.3
],
[
1727476629,
481.515625,
50.4
],
[
1727476629,
481.515625,
40.6
],
[
1727476784,
519.4921875,
50.2
],
[
1727476784,
519.4921875,
53.2
],
[
1727476784,
519.4921875,
48.5
],
[
1727476784,
519.4921875,
56.8
],
[
1727476878,
523.36328125,
50.2
],
[
1727476878,
523.36328125,
56.5
],
[
1727476878,
523.36328125,
47.4
],
[
1727476878,
523.36328125,
56.8
],
[
1727477065,
524.625,
50.3
],
[
1727477065,
524.625,
52.1
],
[
1727477065,
524.625,
47.4
],
[
1727477065,
524.625,
58.7
],
[
1727477262,
527.05078125,
50.3
],
[
1727477262,
527.05078125,
46.2
],
[
1727477262,
527.05078125,
49.7
],
[
1727477262,
527.05078125,
58.7
],
[
1727477447,
530.25,
50.2
],
[
1727477447,
530.25,
40
],
[
1727477447,
530.25,
52.4
],
[
1727477447,
530.25,
37.1
],
[
1727477641,
531.3828125,
50.2
],
[
1727477641,
531.3828125,
43.2
],
[
1727477641,
531.3828125,
51.2
],
[
1727477641,
531.3828125,
42.4
],
[
1727477874,
532.46875,
50.2
],
[
1727477874,
532.46875,
39.4
],
[
1727477874,
532.46875,
50.6
],
[
1727477874,
532.46875,
54.3
],
[
1727478149,
539.4453125,
50.2
],
[
1727478149,
539.4453125,
56.2
],
[
1727478149,
539.4453125,
50.6
],
[
1727478149,
539.4453125,
40.6
],
[
1727478384,
539.87890625,
50.2
],
[
1727478384,
539.87890625,
55.3
],
[
1727478384,
539.87890625,
48.9
],
[
1727478384,
539.87890625,
55.6
],
[
1727478697,
547.6640625,
50.2
],
[
1727478697,
547.6640625,
40
],
[
1727478697,
547.6640625,
50.8
],
[
1727478697,
547.6640625,
50
],
[
1727479061,
562.5703125,
50.2
],
[
1727479061,
562.5703125,
39.4
],
[
1727479061,
562.5703125,
51.3
],
[
1727479061,
562.5703125,
42.4
],
[
1727479423,
567.55859375,
50.2
],
[
1727479423,
567.55859375,
53.2
],
[
1727479423,
567.55859375,
50
],
[
1727479423,
567.55859375,
55.6
],
[
1727479844,
572.56640625,
50.2
],
[
1727479844,
572.56640625,
38.2
],
[
1727479844,
572.56640625,
51.8
],
[
1727479844,
572.56640625,
41.2
],
[
1727480233,
578.9921875,
50.2
],
[
1727480233,
578.9921875,
54.3
],
[
1727480233,
578.9921875,
48.6
],
[
1727480233,
578.9921875,
57.8
],
[
1727480672,
472.4140625,
50.2
],
[
1727480672,
472.4140625,
51.3
],
[
1727480672,
472.4140625,
49
],
[
1727480672,
472.4140625,
56.8
],
[
1727481180,
455.328125,
50.2
],
[
1727481180,
455.328125,
38.2
],
[
1727481180,
455.328125,
51.4
],
[
1727481180,
455.328125,
40.6
],
[
1727481734,
454.31640625,
50.2
],
[
1727481734,
454.31640625,
54.2
],
[
1727481734,
454.31640625,
49
],
[
1727481734,
454.31640625,
54.8
],
[
1727482384,
478.26171875,
50.2
],
[
1727482384,
478.26171875,
54.5
],
[
1727482384,
478.26171875,
50.4
],
[
1727482384,
478.26171875,
41.2
],
[
1727483083,
479.79296875,
50.3
],
[
1727483083,
479.79296875,
37.1
],
[
1727483083,
479.79296875,
51.1
],
[
1727483083,
479.79296875,
41.2
],
[
1727484021,
456.63671875,
50.3
],
[
1727484021,
456.63671875,
56.2
],
[
1727484021,
456.63671875,
48.3
],
[
1727484021,
456.63671875,
57.8
],
[
1727484965,
482.03515625,
50.3
],
[
1727484965,
482.03515625,
54
],
[
1727484965,
482.03515625,
49
],
[
1727484965,
482.03515625,
48.6
],
[
1727485884,
487.25,
50.3
],
[
1727485884,
487.25,
50
],
[
1727485884,
487.25,
51.2
],
[
1727485884,
487.25,
38.2
],
[
1727486974,
495.96875,
50.3
],
[
1727486974,
495.96875,
54.3
],
[
1727486974,
495.96875,
48.8
],
[
1727486974,
495.96875,
55.6
],
[
1727488001,
484.0390625,
50.3
],
[
1727488001,
484.0390625,
53.2
],
[
1727488001,
484.0390625,
49
],
[
1727488001,
484.0390625,
54.8
],
[
1727489213,
470.0546875,
50.3
],
[
1727489213,
470.0546875,
50
],
[
1727489213,
470.0546875,
48.1
],
[
1727489213,
470.0546875,
56.8
],
[
1727490570,
553.25390625,
50.3
],
[
1727490570,
553.25390625,
38.2
],
[
1727490570,
553.25390625,
50.9
],
[
1727490570,
553.25390625,
48.6
],
[
1727492100,
473.94921875,
50.3
],
[
1727492100,
473.94921875,
56.2
],
[
1727492100,
473.94921875,
50.5
],
[
1727492100,
473.94921875,
39.4
],
[
1727493621,
576,
50.3
],
[
1727493621,
576,
51.1
],
[
1727493622,
576,
49.3
],
[
1727493622,
576,
56.5
],
[
1727495224,
499.15234375,
50.3
],
[
1727495224,
499.15234375,
53.2
],
[
1727495224,
499.15234375,
50.1
],
[
1727495224,
499.15234375,
57.8
],
[
1727497135,
511.734375,
50.3
],
[
1727497135,
511.734375,
42.9
],
[
1727497135,
511.734375,
51
],
[
1727497135,
511.734375,
40.6
],
[
1727498991,
540.421875,
50.3
],
[
1727498991,
540.421875,
45.2
],
[
1727498991,
540.421875,
50.3
],
[
1727498991,
540.421875,
53.3
],
[
1727501009,
596.2578125,
50.3
],
[
1727501009,
596.2578125,
38.2
],
[
1727501051,
596.265625,
49.7
],
[
1727501051,
596.265625,
53.2
]
];
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();
}
}
}, 300);
}
function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
if (searchInput) {
searchInput.addEventListener("input", colorize_table_entries);
}
}
function updatePreWidths() {
var width = window.innerWidth * 0.95;
var pres = document.getElementsByTagName('pre');
for (var i = 0; i < pres.length; i++) {
pres[i].style.width = width + 'px';
}
}
window.addEventListener('load', updatePreWidths);
window.addEventListener('resize', updatePreWidths);
$(document).ready(function() {
colorize_table_entries();
add_up_down_arrows_for_scrolling();
add_colorize_to_gridjs_table();
});
$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_reference_samples</th><th>recent_samples_proportion</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 50</td><td>0.225887</td><td>0.683044</td><td>0.174044 </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_reference_samples</td><td>range</td><td>50</td><td>500</td><td></td><td>int </td></tr><tr><td> recent_samples_proport…</td><td>range</td><td>0.1</td><td>1</td><td></td><td>float </td></tr><tr><td> threshold</td><td>range</td><td>0.5</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>499</td>
<td>2</td>
<td>501</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_reference_samples,recent_samples_proportion,threshold
0,0_0,COMPLETED,Sobol,0.192298074518629680262904457777,51,0.846809870004653908459602007497,0.522989526391029357910156250000
1,1_0,COMPLETED,Sobol,0.441360340085021229938888609468,336,0.697653441224247217178344726562,0.788498062733560822756828656566
2,2_0,COMPLETED,Sobol,0.441360340085021229938888609468,446,0.684619253221899315420273524069,0.757331812940537973943833094381
3,3_0,COMPLETED,Sobol,0.255563890972743235074915446603,157,0.138318423554301267452970591876,0.514619175437837861331047406566
4,4_0,COMPLETED,Sobol,0.317829457364341094738335868897,387,0.149033119343221193142667857501,0.640954717807471707757827061869
5,5_0,COMPLETED,Sobol,0.337834458614653665442517649353,313,0.647527315840125128332260828756,0.621891652047634191369240852509
6,6_0,COMPLETED,Sobol,0.235808952238059532646730076522,112,0.988534315023571252822875976562,0.557409877702593758996840733744
7,7_0,COMPLETED,Sobol,0.441360340085021229938888609468,493,0.719745303224772259298447352194,0.785397396981716178210319867503
8,8_0,COMPLETED,Sobol,0.301825456364090971561608967022,308,0.820041014999151274267319422506,0.576165532972663596567031163431
9,9_0,COMPLETED,Sobol,0.331832958239559938640184100223,134,0.771533321961760498730598101247,0.726162222120910971767671071575
10,10_0,COMPLETED,Sobol,0.441360340085021229938888609468,475,0.485890550911426521984992632497,0.773015015386045001299919476878
11,11_0,COMPLETED,Sobol,0.354588647161790393447233782354,435,0.848481718543916962893547406566,0.618179913703352235110344281566
12,12_0,COMPLETED,Sobol,0.398849712428107072703653557255,328,0.808806919492781162261962890625,0.726745853200554847717285156250
13,13_0,COMPLETED,Sobol,0.311827956989247256913699857250,271,0.822741483151912644800063389994,0.588666524365544341357292523753
14,14_0,COMPLETED,Sobol,0.382845711427856949526926655381,302,0.369282615371048450469970703125,0.736300043482333466116074305319
15,15_0,COMPLETED,Sobol,0.221805451362840688744881845196,134,0.758107978198677256997939366556,0.526157554425299212041977625631
16,16_0,COMPLETED,Sobol,0.330582645661415375215597123315,491,0.113086053542792802639738170001,0.726659548096358798296989789378
17,17_0,COMPLETED,Sobol,0.270317579394848683804752909055,196,0.439315837062895342413071375631,0.660423051379620984491225499369
18,18_0,COMPLETED,Sobol,0.328332083020755227664722042391,141,0.698239339608699105532707562816,0.719711340405047028667695485638
19,19_0,COMPLETED,Sobol,0.398099524881220356853361863614,408,0.634777214843779802322387695312,0.687387890648096799850463867188
20,20_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.649786356525840935738358439266,0.500000000000000000000000000000
21,21_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.967273983933888747976936883788,0.500000000000000000000000000000
22,22_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.435905393963842424653876150842,0.584768165975881881735176648363
23,23_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.100000000000000005551115123126,0.642098335819103471777680169907
24,24_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.590082620744604380291775669320,0.561276017488033018842941146431
25,25_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.494705493128585027662325046549,0.500000000000000000000000000000
26,26_0,COMPLETED,BoTorch,0.182295573893473394910813567549,50,0.815217720624371033899535632372,0.500000000000000000000000000000
27,27_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.294542399565861701127289506985,0.614045227525323755735087161156
28,28_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.707174621587320806348486712523,0.545661934315960883701279726665
29,29_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.504910918217320259238078961062,0.547939206613857088257191207958
30,30_0,COMPLETED,BoTorch,0.197049262315578843640651030000,50,0.100000000000000005551115123126,0.716606955615633145484366650635
31,31_0,COMPLETED,BoTorch,0.328082020505126248366423169500,500,0.100000000000000005551115123126,0.500000000000000000000000000000
32,32_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.801424948740082965237263579183,0.500000000000000000000000000000
33,33_0,COMPLETED,BoTorch,0.211802950737684403392790954967,62,0.100000000000000005551115123126,0.639000975254085723520347528392
34,34_0,COMPLETED,BoTorch,0.306326581645411377685661591386,301,0.100000000000000005551115123126,0.505783679816611431157014067139
35,35_0,COMPLETED,BoTorch,0.184796199049762410737685058848,63,0.785221702540274901771510940307,0.511785914490039739455085054942
36,36_0,COMPLETED,BoTorch,0.234808702175543837498139509989,138,0.210203860445026535774815101831,0.568738770679881877612160678837
37,37_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.575103843008828441440982714994,0.607312939874673718421149715141
38,38_0,COMPLETED,BoTorch,0.181545386346586679060521873907,50,0.541560314479246240892962305225,0.516242201276765677597779813368
39,39_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.495617115233334626367422970361,0.500222122687436820953621463559
40,40_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.174731662884123561951810188475,0.581006283316551352946532915666
41,41_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.282660611517684179361253882234,0.653607132618334030205176077288
42,42_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.316438638771501912216876917228,0.539639514115436425001348652586
43,43_0,COMPLETED,BoTorch,0.313578394598649667912582117424,355,0.285578737668460647114443418104,0.500000000000000000000000000000
44,44_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.100000000000000005551115123126,0.589501410438319384255123623007
45,45_0,COMPLETED,BoTorch,0.293573393348337097208400336967,330,0.115898434034316419327659275496,0.558868198800806847970079616061
46,46_0,COMPLETED,BoTorch,0.360590147536884231271869794000,500,1.000000000000000000000000000000,0.500000000000000000000000000000
47,47_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.254448464455158951391666732889,0.583638385686529925777676908183
48,48_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.270752924709132480884932192566,0.671179546915459779299339970748
49,49_0,COMPLETED,BoTorch,0.197299324831207822938949902891,50,0.100000000000000005551115123126,0.500000000000000000000000000000
50,50_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.362489349568308250049142316129,0.650826029095480262931516790559
51,51_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.294526477927624596997446815294,0.614044591533120964754743908998
52,52_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.330246392031487645546405929053,0.672266837193724930976657105930
53,53_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.100000000000000005551115123126,0.800000000000000044408920985006
54,54_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.409993153499464413336283996614,0.622525419524639356261275224824
55,55_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.313393335804594941329526136542,0.655736719754156149875257142412
56,56_0,COMPLETED,BoTorch,0.209052263065766408267620590777,77,0.132833659418543414965085958102,0.688763510625863717429240296042
57,57_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.725241134597713665854712417058,0.500000000000000000000000000000
58,58_0,COMPLETED,BoTorch,0.347836959239809950794608539582,421,0.999856461173234789541197642393,0.586713459805923154277706998982
59,59_0,COMPLETED,BoTorch,0.372843210802700664174835765152,401,0.804046850503410404087389906636,0.644692649652438887208916185045
60,60_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.333630155924918514465105090494,0.500000000000000000000000000000
61,61_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.296049607202518716420058808581,0.725711602497758900831570372247
62,62_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.337491190206078317537219390942,0.800000000000000044408920985006
63,63_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.398648480884710276761495606479,0.500000000000000000000000000000
64,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
65,65_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.272753877289133916939078972064,0.716172813772965710654716531280
66,66_0,COMPLETED,BoTorch,0.208552138034508671715627770027,58,0.684956001252192248074379676837,0.753771331510694309052666994830
67,67_0,COMPLETED,BoTorch,0.204801200300074981441866839305,51,0.611190772243851543343851062673,0.800000000000000044408920985006
68,68_0,COMPLETED,BoTorch,0.209552388097024255841915874043,53,0.100000000000000005551115123126,0.712665582702354361011032324313
69,69_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.285611669906581822075963827956,0.500000000000000000000000000000
70,70_0,COMPLETED,BoTorch,0.198549637409352386363536879799,50,0.226324615646654264677906098768,0.800000000000000044408920985006
71,71_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.217602826226807044562860937731,0.761623078839635869741186979809
72,72_0,COMPLETED,BoTorch,0.198549637409352386363536879799,50,0.230291653881715491225534719888,0.800000000000000044408920985006
73,73_0,COMPLETED,BoTorch,0.197049262315578843640651030000,50,0.215779663655551678935751169774,0.764173212611893637458138073271
74,74_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.236042605425510587657811356621,0.706741546248125684925867062702
75,75_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.243545967974960753110735822702,0.773452149388051513057007468888
76,76_0,COMPLETED,BoTorch,0.193548387096774243687491434684,50,0.331588944003657570824827871547,0.767481488311701376403561880579
77,77_0,COMPLETED,BoTorch,0.195548887221805411940067642718,50,0.238599870167312738677978245505,0.800000000000000044408920985006
78,78_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.576701430582529983581707710982,0.500000000000000000000000000000
79,79_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.870144445983915826303700669087,0.500000000000000000000000000000
80,80_0,COMPLETED,BoTorch,0.202550637659414833890991758381,50,0.219962278709577879753922502459,0.766475741776040386810109339422
81,81_0,COMPLETED,BoTorch,0.177544386096524120510764532810,50,0.260211064297423799729358506738,0.762475352532608785516288207873
82,82_0,COMPLETED,BoTorch,0.201550387596899249764703654364,50,0.457967922367365054547860836465,0.711599795342949703602641875477
83,83_0,COMPLETED,BoTorch,0.198049512378094538789241596533,78,0.606149050979434500519005268870,0.500000000000000000000000000000
84,84_0,COMPLETED,BoTorch,0.204051012753188265591575145663,73,0.840710867640089620778098833398,0.500000000000000000000000000000
85,85_0,COMPLETED,BoTorch,0.202800700175043813189290631271,50,1.000000000000000000000000000000,0.681179691348950955287477881939
86,86_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.524610066657929197120324715797,0.698874368841887250169975231984
87,87_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,1.000000000000000000000000000000,0.617969924669936387928714793816
88,88_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.809359404243581326277023890725,0.641141421489074714301636959135
89,89_0,COMPLETED,BoTorch,0.254063515878969692352029596805,50,1.000000000000000000000000000000,0.800000000000000044408920985006
90,90_0,COMPLETED,BoTorch,0.203050762690672681465287041647,50,0.923694716617771471867115451460,0.673243607140706634694993226731
91,91_0,COMPLETED,BoTorch,0.200300075018754686340116677457,50,0.465445991908797096492378386756,0.731278173159580546780489385128
92,92_0,COMPLETED,BoTorch,0.243810952738184538723942296201,59,0.757918059831655055447185986850,0.800000000000000044408920985006
93,93_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.713830843353167376896806217701,0.681280106608185609395889059670
94,94_0,COMPLETED,BoTorch,0.234058514628657121647847816348,140,0.100000000000000005551115123126,0.800000000000000044408920985006
95,95_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,1.000000000000000000000000000000,0.561627855545467458142638861318
96,96_0,COMPLETED,BoTorch,0.195798949737434391238366515609,50,0.891580900315491975405279845290,0.571316739836011278086402853660
97,97_0,COMPLETED,BoTorch,0.213303325831457835093374342250,50,0.875228599059007961180611800955,0.739302933692255415110139438184
98,98_0,COMPLETED,BoTorch,0.259314828707176814326373914810,70,0.700491556185313557492122527037,0.794699577367580678455283305084
99,99_0,COMPLETED,BoTorch,0.405101275318829667781983516761,96,0.974941381947132312824066957546,0.800000000000000044408920985006
100,100_0,COMPLETED,BoTorch,0.280070017504376100880847388908,67,0.723327388008552341069901103765,0.796089845443683330472595116589
101,101_0,COMPLETED,BoTorch,0.234058514628657121647847816348,51,0.879162311821145681101086211129,0.740182600837908766244765956799
102,102_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.916069315051276422678938615718,0.555086118939344230760468690278
103,103_0,COMPLETED,BoTorch,0.214803700925231266793957729533,51,0.875751637377343628010351039848,0.669302163177195597043578345620
104,104_0,COMPLETED,BoTorch,0.236059014753688400922726486897,50,0.994946241597996272609805146203,0.680189613791603031600629947206
105,105_0,COMPLETED,BoTorch,0.260815203800950246026957302092,151,0.100000000000000005551115123126,0.788292097117884615897764888359
106,106_0,COMPLETED,BoTorch,0.208552138034508671715627770027,50,0.999220421546680337421264539444,0.679667907432342288664983698254
107,107_0,COMPLETED,BoTorch,0.339334833708427097143101036636,91,0.956664940423261112023567420692,0.800000000000000044408920985006
108,108_0,COMPLETED,BoTorch,0.209802450612653124117912284419,50,0.922414646260579673686663682020,0.672635419184068439335533184931
109,109_0,COMPLETED,BoTorch,0.251062765691422828950862822239,183,1.000000000000000000000000000000,0.500000000000000000000000000000
110,110_0,COMPLETED,BoTorch,0.191547886971742964412612764136,74,0.312709664582845769942309743783,0.500000000000000000000000000000
111,111_0,COMPLETED,BoTorch,0.182295573893473394910813567549,69,0.339919454351074790121600699422,0.530240770350901335916660173098
112,112_0,COMPLETED,BoTorch,0.259564891222805682602370325185,189,0.768163996399901627398776327027,0.500000000000000000000000000000
113,113_0,COMPLETED,BoTorch,0.202800700175043813189290631271,83,0.411989884720331267509152439743,0.500000000000000000000000000000
114,114_0,COMPLETED,BoTorch,0.212803200800200098541381521500,90,0.447712762012801301914066698373,0.543391548752985786840952187049
115,115_0,COMPLETED,BoTorch,0.223305826456614120445465232478,101,0.761723982952132727675120804633,0.536140099639785594476393271179
116,116_0,COMPLETED,BoTorch,0.258064516129032250901786937902,160,0.722345897600692232742858323036,0.578349589953481735271623165318
117,117_0,COMPLETED,BoTorch,0.218804701175293825343715070630,119,0.658747765200158541532005074259,0.559590920872141350805861748086
118,118_0,COMPLETED,BoTorch,0.267316829207301820403586134489,168,1.000000000000000000000000000000,0.500000000000000000000000000000
119,119_0,COMPLETED,BoTorch,0.314078519629907515486877400690,290,0.829749525448565838914305459184,0.628510260044411683821863334742
120,120_0,COMPLETED,BoTorch,0.224556139034758683870052209386,133,0.791383059858784876361426086078,0.522311303899349654855654989660
121,121_0,COMPLETED,BoTorch,0.220055013753438388768302047538,136,0.767446790828263836203859682428,0.526950025026618695811464476719
122,122_0,COMPLETED,BoTorch,0.221805451362840688744881845196,129,0.863622893696706528388062906743,0.500000000000000000000000000000
123,123_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.444462596829201195269831714540,0.669562378535379920663217490073
124,124_0,COMPLETED,BoTorch,0.199799949987496838765821394190,50,0.375001470396787661698567717394,0.697921811022987670369843726803
125,125_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.532152815903539466724225803773
126,126_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.342489500538812718932746292921,0.562339307583924141731301915570
127,127_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.382471845681574440511951706867,0.533412953828685809654075455910
128,128_0,COMPLETED,BoTorch,0.197549387346836691214946313266,50,0.597031275612136091979209595593,0.645726178613784229654015689448
129,129_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.814038466345782585342760739877,0.554783035554184178472780786251
130,130_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.276577611581957982789248262634,0.527702402087004807107462056592
131,131_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.208628045236680687013475221647,0.500000000000000000000000000000
132,132_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.933584226740622469264963001478,0.516066932203674944013016556710
133,133_0,COMPLETED,BoTorch,0.196799199799949975364654619625,50,0.501592835173656603764413830504,0.660245471798773575500263177673
134,134_0,COMPLETED,BoTorch,0.208552138034508671715627770027,85,0.100000000000000005551115123126,0.500000000000000000000000000000
135,135_0,COMPLETED,BoTorch,0.198799699924981254639533290174,72,0.101309618180641367035654809570,0.500000000000000000000000000000
136,136_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.271486919042045471428536984604,0.543708178996996194243251920852
137,137_0,COMPLETED,BoTorch,0.201300325081270270466404781473,74,0.313174044153622710418005681277,0.500030092180918317446014498273
138,138_0,COMPLETED,BoTorch,0.181545386346586679060521873907,50,0.339529034587735145667153346949,0.586464119183446319638619570469
139,139_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.744981897558269934300767545210,0.589861972571526838038380446960
140,140_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.789967550691027420306511430681,0.500000000000000000000000000000
141,141_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.347210463303218530572991085137,0.525096890869235388699109989830
142,142_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.898407243415798584251774627774,0.500000000000000000000000000000
143,143_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.734552539388674374798426924826,0.525093338661215214280275631609
144,144_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.384193161747946043682588879165,0.732303288032380983452185319038
145,145_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,0.411452826072087329833948388114,0.737032918639650591607903606928
146,146_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.110318172301142908287019395175,0.549302848091453910228665336035
147,147_0,COMPLETED,BoTorch,0.289572393098274538658642995870,79,0.138377106355769219359075350440,0.500000000000000000000000000000
148,148_0,COMPLETED,BoTorch,0.217554388597149261919128093723,78,0.100000000000000005551115123126,0.800000000000000044408920985006
149,149_0,COMPLETED,BoTorch,0.255313828457114255776616573712,178,0.141901815922689661375599712301,0.766944282725105397346965219185
150,150_0,COMPLETED,BoTorch,0.184546136534133542461688648473,50,0.712381059947489569950107579643,0.559758928681191814114015414816
151,151_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.378016118008556722962509866193,0.600747959162706623992278309743
152,152_0,COMPLETED,BoTorch,0.233058264566141537521559712332,69,0.100000000000000005551115123126,0.548806625166556982797771979676
153,153_0,COMPLETED,BoTorch,0.194048512128031980239484255435,50,0.402299846505957070519343687920,0.551622774961467832177675063576
154,154_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.764587994581870766808151529403,0.500000000000000000000000000000
155,155_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.782609433422867817320423000638,0.562334009097488984885160334670
156,156_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.652692026049437323820257006446,0.614145673665651758987849007099
157,157_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.444117982232666630437734056613,0.500000000000000000000000000000
158,158_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.638763401423660392985937050980,0.540469899076802429149779527506
159,159_0,COMPLETED,BoTorch,0.204801200300074981441866839305,52,0.123156734460232578087790500376,0.769124391219168535016592613829
160,160_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.515399447155842627132926736522,0.630712249022509885421072794998
161,161_0,COMPLETED,BoTorch,0.271567891972993247229339885962,189,0.136239360535459652634671101623,0.759672294594316177551718283212
162,162_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.443982712270488910633048362797,0.556179231386568884154542047327
163,163_0,COMPLETED,BoTorch,0.193548387096774243687491434684,51,0.371281352516638074590105134121,0.753067925537570070915194264671
164,164_0,COMPLETED,BoTorch,0.231557889472368105820976325049,50,0.725470301804213790752839940978,0.800000000000000044408920985006
165,165_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.473619526029325665916758225649,0.568916698694828193438866037468
166,166_0,COMPLETED,BoTorch,0.183795948987246826611396954831,54,0.153475527832018704410543818994,0.759880605026953093172892295115
167,167_0,COMPLETED,BoTorch,0.193548387096774243687491434684,69,0.942643659473980544127869052318,0.500000000000000000000000000000
168,168_0,COMPLETED,BoTorch,0.186796699174793690012563729397,62,0.565549452604356295282173050509,0.513594987992307627777677225822
169,169_0,RUNNING,BoTorch,,50,0.854675063355610387105798508856,0.535227395441616793370087634685
170,170_0,COMPLETED,BoTorch,0.184546136534133542461688648473,50,0.450705734409799663175988371222,0.521758389460954763450217797072
171,171_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.538738242960678848092470616393,0.500000000000000000000000000000
172,172_0,COMPLETED,BoTorch,0.185046261565391390035983931739,50,0.447180085967830542870160570601,0.530597648941003585676412512839
173,173_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,0.365831722010560689284375257557,0.500000000000000000000000000000
174,174_0,COMPLETED,BoTorch,0.187296824206051537586859012663,69,0.785754237253416309982867460349,0.500000000000000000000000000000
175,175_0,COMPLETED,BoTorch,0.194548637159289827813779538701,50,0.787828499404044646503564308659,0.581829865957509184681839542463
176,176_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.718647515274157711040459162177,0.630063134111960909677918607485
177,177_0,COMPLETED,BoTorch,0.200550137534383554616113087832,57,0.773169248084723648162253084593,0.507871312495567162059728616441
178,178_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.545354099939708403255167468160
179,179_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.300806240200773900017594542078,0.568519538338803420707279201451
180,180_0,COMPLETED,BoTorch,0.182795698924731131462806388299,50,0.260717661945561229863699281850,0.610333764782839516271906177280
181,181_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.905915736546186844968531204358,0.538794370143921286242516544007
182,182_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.513352939950143416503181015287,0.572361886647337114730760276871
183,183_0,COMPLETED,BoTorch,0.195798949737434391238366515609,89,0.559657157485656586715094817919,0.500000000000000000000000000000
184,184_0,COMPLETED,BoTorch,0.184796199049762410737685058848,50,0.309698778801933527482503905048,0.580138437316689792311308337958
185,185_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.371997488283797750341364007909,0.525751137803067369880238857149
186,186_0,COMPLETED,BoTorch,0.216054013503375830218544706440,96,0.595096758457977492717816403456,0.500000000000000000000000000000
187,187_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.660829093507727227674308778660,0.523835460038788447434399131453
188,188_0,COMPLETED,BoTorch,0.176544136034008536384476428793,50,0.459858043667938587439891762187,0.533357833920752755219041318924
189,189_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.207010265383533287320005911170,0.645231071728858629477088015847
190,49_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.100000000000000005551115123126,0.500000000000000000000000000000
191,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000
192,192_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.209275044669737120273111941060,0.500020916525400038743498498661
193,193_0,RUNNING,BoTorch,,66,0.533548314493060504837274038437,0.500000000000000000000000000000
194,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
195,195_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.638944974967135093102399423515,0.593122413922630764560040006472
196,196_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.804641130474754273649296010262,0.534583185485693390681660730479
197,197_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.869252793407701118688635233411,0.528363696563006857154221052042
198,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000
199,199_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.522938953829648944804375787498
200,200_0,COMPLETED,BoTorch,0.199299824956239102213828573440,50,0.144601515659045998241083452740,0.687511703644821725589508787380
201,201_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.944350626967797146527061613597,0.535885857771406626248733573448
202,202_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.588625539313744172709164104162
203,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
204,204_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.349126220241388229847956381491,0.614787802214199596839705463935
205,205_0,COMPLETED,BoTorch,0.197049262315578843640651030000,60,1.000000000000000000000000000000,0.500000000000000000000000000000
206,206_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.932071692077272917487107406487,0.500000000000000000000000000000
207,207_0,COMPLETED,BoTorch,0.201800450112528118040700064739,70,0.370406397995742153739229252096,0.615334463479904281157928380708
208,208_0,COMPLETED,BoTorch,0.193048262065516396113196151418,62,0.567528113387534283162949577672,0.515292554221786991419662626868
209,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000
210,210_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.398097312292544525114124098764,0.535441114693999575813165847649
211,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000
212,212_0,COMPLETED,BoTorch,0.184796199049762410737685058848,63,0.787899502211990232503069364611,0.500000000000000000000000000000
213,213_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.322259501034363848859243262268,0.712605549247603664575478887855
214,214_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.597626392439718157056915970315,0.500000000000000000000000000000
215,215_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.935409427107838697956765372510,0.552094338693611352830714622542
216,216_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.410485744684218056832492038666,0.500000000000000000000000000000
217,217_0,COMPLETED,BoTorch,0.184796199049762410737685058848,50,0.730679799830542875405114955356,0.572106471424271734171895786858
218,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000
219,219_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.426000678805463661724672874698,0.647400715526813774758352337813
220,220_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.528513450921707983454211898788,0.707609356181271298424917404191
221,221_0,COMPLETED,BoTorch,0.228057014253563394845514267217,126,0.174748472473160831874849918677,0.555298130230690745179344958160
222,222_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.403894891117930199264662860514,0.569322727632765701599737440119
223,223_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.455198994688842617506452370435,0.608142242123440701639935923595
224,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
225,225_0,COMPLETED,BoTorch,0.209552388097024255841915874043,50,0.927292302712519389551459880749,0.583087565808178442949838427012
226,226_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.100000000000000005551115123126,0.531696611313915101781901739741
227,227_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,0.488326217606287471539872058202,0.731323166766490562196167957154
228,228_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.374637369352278093437291772716,0.623732302768671198478500627971
229,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000
230,230_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.544016875018127010754653838376
231,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
232,232_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.525353811641059986747848142841,0.590263963053190177099338598055
233,233_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.403349381369930815033342241804,0.511975446638067821503170762298
234,234_0,COMPLETED,BoTorch,0.194048512128031980239484255435,50,0.100000000000000005551115123126,0.583243071349429986760526389844
235,235_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.581108309320764515604196276399,0.527300206290547190945972033660
236,236_0,COMPLETED,BoTorch,0.197049262315578843640651030000,50,0.158372935996547642423237789444,0.541986307645733300653034802963
237,237_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.303184079167966691326085992841,0.638505679197377218336839632684
238,238_0,COMPLETED,BoTorch,0.199549887471867970489824983815,50,0.100000000000000005551115123126,0.752166613480570678262893125066
239,239_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.524627171916830392639496949414,0.536896210948017316155755906948
240,240_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.353971511700671981337507077114,0.549652822449042788299777839711
241,241_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.341635883616674740359542283841,0.569391932199675210313216666691
242,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
243,243_0,COMPLETED,BoTorch,0.181545386346586679060521873907,50,0.545855179339290841333820480941,0.545331624061694664185040437587
244,244_0,COMPLETED,BoTorch,0.198799699924981254639533290174,50,0.263209271483330953245172167954,0.720049752475542370611094611377
245,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
246,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
247,247_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.459392414904354451365975364752,0.514964904873884687930285508628
248,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
249,249_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.814103939532104692311520466319,0.512200298947206422717215446028
250,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
251,251_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.210240153371606147691963428770,0.735459111361490669445117873693
252,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
253,253_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,0.386903523945401284223066795676,0.500000000000000000000000000000
254,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
255,255_0,COMPLETED,BoTorch,0.180045011252813247359938486625,50,0.381863804186298616549777307227,0.513234315953521536002313041536
256,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
257,257_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.748964817966614959665605510963,0.580231798115811825411469726532
258,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000
259,259_0,COMPLETED,BoTorch,0.197549387346836691214946313266,66,0.487019784224768992331178196764,0.567335659632140387742538223392
260,260_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.976159623012681554321545718267,0.523053350430102370616225471167
261,261_0,COMPLETED,BoTorch,0.195548887221805411940067642718,60,0.999559271280566652428944962594,0.500076443550252602854300221225
262,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000
263,64_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,1.000000000000000000000000000000,0.500000000000000000000000000000
264,264_0,COMPLETED,BoTorch,0.180545136284070983911931307375,50,0.432763444941595687431856731564,0.500000000000000000000000000000
265,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000
266,266_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.453154905231889060246430744883,0.640787051532197948766622630501
267,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
268,268_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.638649294981019965966595464124,0.572550557352942579569798908778
269,269_0,COMPLETED,BoTorch,0.195548887221805411940067642718,50,0.584951192977362421920872748160,0.571255100651174352321959304390
270,270_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.342796882501694710754236439243,0.500000000000000000000000000000
271,271_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.339732338212254847409354852061,0.528814672920231321207040764421
272,272_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.640456918388409235376457218081,0.500000000000000000000000000000
273,273_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.469504822019437217939241691056,0.500000000000000000000000000000
274,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000
275,275_0,COMPLETED,BoTorch,0.181295323830957699762223001017,50,0.423156009991628501154536934337,0.679047851746629405056410178076
276,276_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.340562641922253273030207765260,0.521671913525359243202217385260
277,277_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.417507433042165310155269253301,0.800000000000000044408920985006
278,278_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.689656700009414924679163050314,0.500000000000000000000000000000
279,64_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,1.000000000000000000000000000000,0.500000000000000000000000000000
280,280_0,COMPLETED,BoTorch,0.182295573893473394910813567549,50,0.511539765795757328525894536142,0.588461947778365912498088619031
281,281_0,COMPLETED,BoTorch,0.196049012253063259514362925984,60,0.998541046681809429941267808317,0.500323702740395503951731370762
282,282_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.613031677369160865609387656150,0.663202174389217580241506766470
283,64_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,1.000000000000000000000000000000,0.500000000000000000000000000000
284,284_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.352444484561499016272989592835,0.800000000000000044408920985006
285,285_0,COMPLETED,BoTorch,0.194548637159289827813779538701,50,0.210061456478748326270888924228,0.593791048398628840132573714072
286,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
287,287_0,COMPLETED,BoTorch,0.191797949487371832688609174511,58,0.584198674697140152289875913993,0.500000000000000000000000000000
288,288_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.297292476833203866970478657095,0.678413861846507537656236763723
289,289_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.379607187756637376452317766962,0.712458416563205743088360577531
290,290_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.389361991054173461890286489506,0.781093601243838753234172145312
291,291_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.588540385740966121019823731331,0.691296998279221197591937198013
292,292_0,COMPLETED,BoTorch,0.200800200050012533914411960723,50,0.539420578070084477673162837164,0.736588885519427361003863552469
293,293_0,COMPLETED,BoTorch,0.181295323830957699762223001017,50,0.584663942695581417829941983655,0.514884386245281877592105956865
294,294_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.381760446164720046446916512650,0.500000000000000000000000000000
295,295_0,COMPLETED,BoTorch,0.203800950237559397315578735288,50,0.423806385124041562484364931152,0.755451717956675983245418137813
296,296_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.397477535149667327019074036798,0.672536109224622813407279409148
297,297_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.372870465920332572196116416308,0.521296847927623763219173724792
298,298_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.275529479300566149113649316860,0.800000000000000044408920985006
299,299_0,COMPLETED,BoTorch,0.184046011502875694887393365207,50,0.490275950557296669174434100569,0.681958557589167013723852051044
300,300_0,COMPLETED,BoTorch,0.212803200800200098541381521500,96,0.426368338138419344929275212053,0.558550867323062250058285371779
301,301_0,COMPLETED,BoTorch,0.181045261315328831486226590641,50,0.420866735770609512456985612516,0.702582226308535284786671581969
302,302_0,COMPLETED,BoTorch,0.203550887721930529039582324913,50,0.167868169071138301218226729361,0.800000000000000044408920985006
303,303_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.391379339642995538461889282189,0.762501222951289525653351120127
304,304_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.365300130674745426873073483875,0.531244682449988681050001559925
305,305_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.841977539560282095543186642317,0.500000000000000000000000000000
306,306_0,COMPLETED,BoTorch,0.195048762190547675388074821967,50,0.376741117470672071121384760772,0.500000000000000000000000000000
307,307_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.366027456593396305351006958517,0.552523008336156973285824278719
308,308_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.719396530052714866521057501814,0.500000000000000000000000000000
309,309_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.370611170221879615560567344801,0.657452627378011134062774090125
310,310_0,COMPLETED,BoTorch,0.182795698924731131462806388299,50,0.403239482669721871488377473725,0.533128338091727882463999321772
311,311_0,COMPLETED,BoTorch,0.196299074768692127790359336359,50,0.461021422525776936041097542329,0.745377958271499330145104522671
312,53_0,COMPLETED,BoTorch,0.198799699924981254639533290174,50,0.100000000000000005551115123126,0.800000000000000044408920985006
313,313_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.369152948684562720593760332122,0.500000000000000000000000000000
314,314_0,COMPLETED,BoTorch,0.198049512378094538789241596533,50,0.256556032096753705573632942105,0.796453643473606254232777246216
315,315_0,COMPLETED,BoTorch,0.197549387346836691214946313266,50,0.595949570122136340621921135607,0.658240684589882274480032720021
316,316_0,COMPLETED,BoTorch,0.206551637909477392440749099478,74,0.100015653221823777596632965015,0.799997458379825943097785057034
317,317_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.347928596046918836570682742604,0.500000000000000000000000000000
318,318_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.414327767460737628191225212504,0.613986597244317033883476142364
319,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
320,64_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,1.000000000000000000000000000000,0.500000000000000000000000000000
321,321_0,COMPLETED,BoTorch,0.200550137534383554616113087832,50,0.100000000000000019428902930940,0.724153951445219767890648654429
322,322_0,COMPLETED,BoTorch,0.199049762440610122915529700549,50,0.411020175934334153211580087373,0.660480038124953150457940864726
323,323_0,COMPLETED,BoTorch,0.267316829207301820403586134489,178,0.903184896401491577044851055689,0.539212374595242183161758475762
324,324_0,COMPLETED,BoTorch,0.196549137284321107088658209250,50,0.100000000000000005551115123126,0.508884676785355982708836108941
325,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
326,326_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.364672012215856433670069236541,0.566066162276287365706650689390
327,327_0,COMPLETED,BoTorch,0.198299574893723407065238006908,50,0.377847605923691043372514286602,0.582191249292318580010885398224
328,328_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.352889629663519222013690068707,0.500000000000000000000000000000
329,329_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.903806068121394523551259680971,0.519803977184655208176877749793
330,330_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.878260412234960652355653110135,0.510985882733827834201179030060
331,331_0,COMPLETED,BoTorch,0.194298574643660959537783128326,50,0.437450948215405976959857525799,0.540734699023231346615148140700
332,332_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.100000000000000005551115123126,0.624829600906597781850848605245
333,333_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.511426498791174966029871029605
334,334_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.457000639479605119674943125574,0.529961398872461297848701633484
335,335_0,COMPLETED,BoTorch,0.185046261565391390035983931739,50,0.342015040641369560958651163673,0.619846100780894748716320918902
336,336_0,COMPLETED,BoTorch,0.198549637409352386363536879799,50,0.177229760245530110207567986436,0.654747616843177171475076647766
337,337_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.542440388943294271584250054730,0.667358309913701708282474100997
338,338_0,COMPLETED,BoTorch,0.189047261815453837563438810321,50,0.686458813378676535599254293629,0.521953075780997366450719709974
339,339_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.318573618459392138291264018335,0.526994221643042992653249712021
340,340_0,COMPLETED,BoTorch,0.183295823955988979037101671565,50,0.280090734897204263198489115894,0.623968009772629339515503943403
341,341_0,COMPLETED,BoTorch,0.178294573643410836361056226451,50,0.350091209927966162673840244679,0.678171372878784373128269180597
342,342_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.530794738226223472565834526904
343,343_0,COMPLETED,BoTorch,0.188547136784196101011445989570,50,0.419514253238369994036816024163,0.616050522144240941813109202485
344,344_0,COMPLETED,BoTorch,0.185046261565391390035983931739,50,0.403706858603698259813086224312,0.767036639247117735251890735526
345,345_0,COMPLETED,BoTorch,0.179544886221555399785643203359,50,0.354987756710988033859166534967,0.651791281222186991151090751373
346,346_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.879144422166368122439905619103,0.552455221404233531856675654126
347,347_0,COMPLETED,BoTorch,0.199799949987496838765821394190,50,0.412796594755190149328427651199,0.737824672074026910451038929750
348,348_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.603906035585979661384214978170,0.500000000000000000000000000000
349,349_0,COMPLETED,BoTorch,0.202300575143785965614995348005,50,0.430324269839404527360215979570,0.785532366071003074203815685905
350,350_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.287559274459247837807396308563,0.500000000000000000000000000000
351,351_0,COMPLETED,BoTorch,0.213803450862715682667669625516,50,0.401452374942600109797297136538,0.800000000000000044408920985006
352,352_0,COMPLETED,BoTorch,0.181295323830957699762223001017,50,0.235489006210967521948163039269,0.694676045628319416280760378868
353,353_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.290881691380718232498026054600,0.716074155405533652185567916604
354,354_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.538445780258377526550361835689
355,64_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,1.000000000000000000000000000000,0.500000000000000000000000000000
356,356_0,COMPLETED,BoTorch,0.201300325081270270466404781473,50,0.343283551920363971809990744077,0.721784379326930292108954745345
357,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
358,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
359,359_0,COMPLETED,BoTorch,0.193548387096774243687491434684,50,0.326917882142228610753420525725,0.696904549479442247950089495134
360,360_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.345656108585776999930772035441,0.540133010225478882304628314159
361,361_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.962266955098403320434385932458,0.531648345455089321731634299795
362,362_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.606501225369156737876608076476,0.625805149268970239972986746579
363,363_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.338719621114231206338018864699,0.697794563881288798512514404138
364,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
365,365_0,COMPLETED,BoTorch,0.260565141285321377750960891717,177,0.913558493095204982026302786835,0.542254582043029453863880462450
366,366_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.653367863400310389110359210463,0.514590919665448787156947219046
367,367_0,COMPLETED,BoTorch,0.174043510877719409535302474978,50,0.225887353628974468788825902266,0.683044326531255019396837724344
368,368_0,COMPLETED,BoTorch,0.204051012753188265591575145663,77,0.653582313591051788925767596083,0.500000000000000000000000000000
369,369_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.289992807574675937054564656137,0.579687462579378420812759031833
370,370_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.311451275326714238644854049198,0.619830834377778527866098556842
371,371_0,COMPLETED,BoTorch,0.201800450112528118040700064739,82,0.560971421302151163068572259363,0.507510894059416517443139582610
372,372_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.262533071270815709929991044191,0.561041695764529890766425523907
373,373_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.342881767681622151577869317407,0.518937497802316571871017458761
374,374_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.767369234492963459004499782168,0.500000000000000000000000000000
375,375_0,COMPLETED,BoTorch,0.183545886471617958335400544456,50,0.331811174303643596772417367902,0.518686157220989096927610262355
376,376_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.555016721942769519770877195697,0.533769715739977890223144640913
377,377_0,COMPLETED,BoTorch,0.184296074018504674185692238098,50,0.302784021701473671228654893639,0.666130630674380497247000221250
378,378_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.643967551631823953428579443425,0.637865390648939989404198058764
379,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
380,380_0,COMPLETED,BoTorch,0.187796949237309274138851833413,50,0.274579381527525590023230961378,0.640460362014749540193747634476
381,381_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.369662933221642675540863365313,0.500000000000000000000000000000
382,382_0,COMPLETED,BoTorch,0.193798449612403111963487845060,50,0.404162075622059746571324012621,0.500000000000000000000000000000
383,383_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,0.913813340303445964529771572415,0.522981512018749539194573117129
384,384_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.736990802060846661447612859774,0.500000000000000000000000000000
385,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
386,386_0,COMPLETED,BoTorch,0.192548137034258548538900868152,50,0.546958791651856790494434790162,0.570494272878494523837389351684
387,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
388,388_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.517722560989907321093994596595
389,389_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.602658433296178075444515798154,0.535559954363865609039407900127
390,390_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.364615398519987032877054389246,0.500000000000000000000000000000
391,391_0,COMPLETED,BoTorch,0.182545636409102263186809977924,50,0.320106588629211707974775436014,0.523740931512669094516354562074
392,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000
393,393_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,0.537389382221507339032484651398,0.599404206477934575758581559057
394,394_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.316961759261636999429612160384,0.643098404717624916315799055155
395,49_0,COMPLETED,BoTorch,0.198299574893723407065238006908,50,0.100000000000000005551115123126,0.500000000000000000000000000000
396,396_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.554855736910768504444035897905
397,64_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,1.000000000000000000000000000000,0.500000000000000000000000000000
398,398_0,COMPLETED,BoTorch,0.190547636909227269264022197603,50,0.684727421774331546089342737105,0.564088203065787663348373826011
399,399_0,COMPLETED,BoTorch,0.181795448862215547336518284283,50,0.281708211391811735868628829849,0.636422174245226734967673110077
400,64_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,1.000000000000000000000000000000,0.500000000000000000000000000000
401,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
402,402_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.639482383433387813198578442098,0.550843955403799934167352603254
403,403_0,COMPLETED,BoTorch,0.190797699424856248562321070494,50,0.919810561818984018245259903779,0.539547316058045400843923289358
404,404_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.315199436466140237023125791893,0.627106753083499501855158086983
405,405_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.925269402693248088631605696719,0.500000000000000000000000000000
406,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
407,407_0,COMPLETED,BoTorch,0.180545136284070983911931307375,50,0.435483280788934279392776716122,0.500000000000000000000000000000
408,408_0,COMPLETED,BoTorch,0.184796199049762410737685058848,50,0.655736283414006138059448858257,0.549739044672193921670100280608
409,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
410,410_0,COMPLETED,BoTorch,0.184546136534133542461688648473,50,0.292663076194693783094180616899,0.545143764170563982496275912126
411,411_0,COMPLETED,BoTorch,0.193048262065516396113196151418,50,0.555311427881579056098360069882,0.706652153066541499626396216627
412,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
413,413_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.336846474582980448531088768505,0.553131371199966026530603357969
414,414_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.296678888646297933551210235237,0.534879236588653439454787985596
415,415_0,COMPLETED,BoTorch,0.215803950987746961942548296065,102,1.000000000000000000000000000000,0.500000000000000000000000000000
416,416_0,COMPLETED,BoTorch,0.190047511877969532712029376853,50,0.506057812643168736244092542620,0.605985501013661997937731484853
417,417_0,COMPLETED,BoTorch,0.183295823955988979037101671565,50,0.284193236125531101254892973884,0.630016041010267757727092430287
418,418_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.852084262859597596495575544395,0.500000000000000000000000000000
419,419_0,COMPLETED,BoTorch,0.185796449112278105886275625380,50,0.665362683418703748650102625106,0.514860941277073602684311026678
420,64_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,1.000000000000000000000000000000,0.500000000000000000000000000000
421,49_0,COMPLETED,BoTorch,0.199299824956239102213828573440,50,0.100000000000000005551115123126,0.500000000000000000000000000000
422,422_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.932998203013783000692171754054,0.515034670687837925484586776292
423,64_0,COMPLETED,BoTorch,0.191797949487371832688609174511,50,1.000000000000000000000000000000,0.500000000000000000000000000000
424,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
425,425_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.956375570628997029309914523765,0.539328398497139760436880351335
426,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
427,427_0,COMPLETED,BoTorch,0.188297074268567121713147116679,50,0.883234524260766984404824597732,0.500000000000000000000000000000
428,428_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.284791800160602459612846359960,0.653604316906256421759735530941
429,429_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.966655440363399676151345829567,0.536843173108926152714559520973
430,430_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.721840184503232173973685803503,0.533949440742214176403024339379
431,431_0,COMPLETED,BoTorch,0.188047011752938253437150706304,50,0.665030190472247539901218260638,0.544660753824110166476657468593
432,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
433,433_0,COMPLETED,BoTorch,0.186296574143535842438268446131,50,0.303477046633177538481618285005,0.632610330893489947179375576525
434,434_0,COMPLETED,BoTorch,0.180295073768442115635934897000,50,0.292991557122806500768064097429,0.646190293056261633175552105968
435,435_0,COMPLETED,BoTorch,0.194048512128031980239484255435,50,0.773564865035695770068002730113,0.519622155646338956813679033075
436,436_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.285844584119506195385440605605,0.674893993050649565468290802528
437,437_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.535440059969595871791625540936,0.627023791264731178429769897775
438,438_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,0.383976710520015696026518980943,0.701259212245332941293440853769
439,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
440,440_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.756563130826383045679506267334,0.531152226949441419456832136348
441,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
442,442_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.470280157739651372139633167535,0.661112269975318911363615370647
443,443_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.510382760155499881626894875808,0.500000000000000000000000000000
444,444_0,COMPLETED,BoTorch,0.180295073768442115635934897000,50,0.768922478541656628792111405346,0.561089333250431310240458060434
445,445_0,COMPLETED,BoTorch,0.191047761940485116838317480870,50,0.753794961323090428884086122707,0.580317280215367770068723984878
446,446_0,COMPLETED,BoTorch,0.183295823955988979037101671565,59,0.472359549232347242231355721742,0.500000000000000000000000000000
447,447_0,COMPLETED,BoTorch,0.186046511627906974162272035755,50,0.314415155074783458921672263386,0.518525412369845173365945356636
448,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
449,449_0,COMPLETED,BoTorch,0.197799449862465670513245186157,61,0.856010560733706515890162336291,0.500000000000000000000000000000
450,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
451,451_0,COMPLETED,BoTorch,0.192298074518629680262904457777,50,0.677300792992510447554366237455,0.615481753896617633792232027190
452,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
453,453_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.405722571524761210781662157387,0.500000000000000000000000000000
454,454_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,1.000000000000000000000000000000,0.535738889431352061087920901628
455,64_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,1.000000000000000000000000000000,0.500000000000000000000000000000
456,456_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.262554452654409853362693638701,0.560482743665254856679780459672
457,457_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,0.611605446699628019224803665566,0.637940740425373409294707016670
458,458_0,COMPLETED,BoTorch,0.195548887221805411940067642718,62,0.293328003518769697688384212597,0.696873446048822731135885533149
459,459_0,COMPLETED,BoTorch,0.193298324581145264389192561794,50,0.656250350699094320283677461703,0.597827216110904835488781827735
460,460_0,COMPLETED,BoTorch,0.200050012503125818064120267081,51,0.789909697844259017784906973247,0.623751981475111905162123093760
461,64_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,1.000000000000000000000000000000,0.500000000000000000000000000000
462,462_0,COMPLETED,BoTorch,0.192798199549887416814897278527,50,0.613005596433220278917985979206,0.663673793557769453599348707939
463,463_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.675867600351706454553379899153,0.595730314311224473655670408334
464,464_0,COMPLETED,BoTorch,0.184046011502875694887393365207,50,0.595077787873350083636125873454,0.620540414022890729484061012045
465,465_0,COMPLETED,BoTorch,0.182295573893473394910813567549,50,0.308373733589650533826187484010,0.655160544006075706846559114638
466,466_0,COMPLETED,BoTorch,0.185546386596649126587976752489,50,0.356089883996532408083623977291,0.500000000000000000000000000000
467,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
468,468_0,COMPLETED,BoTorch,0.187046761690422558288560139772,50,0.733778010300686789335600224149,0.601991070324807031610703234037
469,469_0,COMPLETED,BoTorch,0.189547386846711685137734093587,50,0.303811701107085108120031691215,0.654363024731915765563883269351
470,470_0,COMPLETED,BoTorch,0.178294573643410836361056226451,50,0.620019421106727808279401870095,0.500000000000000000000000000000
471,471_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.647787384996491466537804626569,0.534750533695551477642027293768
472,472_0,COMPLETED,BoTorch,0.196049012253063259514362925984,50,0.556754596162072012965893463843,0.527298861175770738896062539425
473,473_0,COMPLETED,BoTorch,0.182045511377844415612514694658,50,0.310698231018934456493241214048,0.650231218700586532932561567577
474,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
475,475_0,COMPLETED,BoTorch,0.191297824456113985114313891245,50,0.322191282313596727426840971020,0.500000000000000000000000000000
476,476_0,COMPLETED,BoTorch,0.183295823955988979037101671565,50,0.426531348462183657055390995083,0.500000000000000000000000000000
477,477_0,COMPLETED,BoTorch,0.185296324081020258311980342114,50,0.844830283784090196874672074046,0.511557933168035106064053252339
478,478_0,COMPLETED,BoTorch,0.189297324331082816861737683212,50,0.315442894481137381390567497874,0.544811600813915442031998281891
479,64_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,1.000000000000000000000000000000,0.500000000000000000000000000000
480,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
481,481_0,COMPLETED,BoTorch,0.186546636659164821736567319022,50,0.727997531753198146020622516517,0.514693242713986864877995230927
482,482_0,COMPLETED,BoTorch,0.189797449362340553413730503962,50,0.655599648519003763702528431168,0.500000000000000000000000000000
483,483_0,COMPLETED,BoTorch,0.181795448862215547336518284283,54,0.589170756138263973511470794620,0.539246557678844440353316258552
484,484_0,COMPLETED,BoTorch,0.188797199299824969287442399946,50,0.301517855210633067830627851436,0.627587004425708916421910998906
485,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
486,486_0,COMPLETED,BoTorch,0.191547886971742964412612764136,50,0.866910637299097119878865669307,0.521062131871933731375179377210
487,487_0,COMPLETED,BoTorch,0.189297324331082816861737683212,53,0.336449317292861982409135634953,0.555430628783738389309121430415
488,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
489,489_0,COMPLETED,BoTorch,0.196799199799949975364654619625,58,0.289774233275913783991484251601,0.674990903302228328897172104917
490,490_0,COMPLETED,BoTorch,0.186796699174793690012563729397,50,0.939260894699990078748896848992,0.500000000000000000000000000000
491,64_0,COMPLETED,BoTorch,0.187296824206051537586859012663,50,1.000000000000000000000000000000,0.500000000000000000000000000000
492,492_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,0.440319863895973551137785761966,0.500000000000000000000000000000
493,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
494,494_0,COMPLETED,BoTorch,0.193548387096774243687491434684,50,0.422640903285450453275018389832,0.500000000000000000000000000000
495,495_0,COMPLETED,BoTorch,0.182795698924731131462806388299,50,0.416895915044817821915046351933,0.500000000000000000000000000000
496,496_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,0.618032801086244476529429903167,0.604734491208909763493295486114
497,64_0,COMPLETED,BoTorch,0.183045761440360110761105261190,50,1.000000000000000000000000000000,0.500000000000000000000000000000
498,498_0,COMPLETED,BoTorch,0.183795948987246826611396954831,50,0.310349556061717657406973103207,0.643957295017805853021286566218
499,64_0,COMPLETED,BoTorch,0.187546886721680405862855423038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
500,64_0,COMPLETED,BoTorch,0.190297574393598400988025787228,50,1.000000000000000000000000000000,0.500000000000000000000000000000
</pre>
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<h1> CPU/RAM-Usage (main)</h1>
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<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727476458,473.08984375,49.8
1727476458,473.08984375,54.0
1727476458,473.08984375,49.7
1727476458,473.08984375,55.3
1727476458,473.08984375,37.5
1727476458,473.08984375,50.3
1727476458,473.08984375,40.6
1727476505,478.9609375,49.8
1727476505,478.9609375,54.3
1727476505,478.9609375,49.7
1727476505,478.9609375,39.4
1727476508,479.078125,49.9
1727476508,479.078125,54.3
1727476508,479.078125,47.6
1727476508,479.078125,58.7
1727476510,479.078125,49.9
1727476510,479.078125,39.4
1727476510,479.078125,54.2
1727476510,479.078125,42.4
1727476513,479.078125,49.9
1727476513,479.078125,48.7
1727476513,479.078125,49.0
1727476513,479.078125,57.8
1727476515,479.125,49.8
1727476515,479.125,53.1
1727476515,479.125,52.5
1727476515,479.125,40.6
1727476518,479.1796875,49.9
1727476518,479.1796875,39.4
1727476518,479.1796875,52.6
1727476518,479.1796875,44.4
1727476520,479.1796875,49.8
1727476520,479.1796875,54.3
1727476520,479.1796875,49.1
1727476520,479.1796875,55.6
1727476523,479.1796875,49.8
1727476523,479.1796875,56.5
1727476523,479.1796875,48.6
1727476523,479.1796875,58.1
1727476525,479.1796875,49.9
1727476525,479.1796875,54.3
1727476525,479.1796875,48.6
1727476525,479.1796875,56.5
1727476527,479.1796875,49.8
1727476527,479.1796875,54.3
1727476527,479.1796875,49.1
1727476527,479.1796875,40.0
1727476529,479.1796875,49.9
1727476529,479.1796875,46.2
1727476529,479.1796875,49.1
1727476529,479.1796875,55.8
1727476532,479.2578125,49.9
1727476532,479.2578125,55.3
1727476532,479.2578125,48.1
1727476532,479.2578125,57.8
1727476534,479.2578125,49.9
1727476534,479.2578125,47.4
1727476534,479.2578125,50.9
1727476534,479.2578125,40.6
1727476536,479.2578125,49.9
1727476536,479.2578125,57.4
1727476536,479.2578125,48.1
1727476536,479.2578125,45.9
1727476539,479.2890625,49.9
1727476539,479.2890625,39.4
1727476539,479.2890625,53.3
1727476539,479.2890625,38.7
1727476541,479.2890625,49.8
1727476541,479.2890625,52.1
1727476541,479.2890625,53.7
1727476541,479.2890625,38.7
1727476543,481.33984375,49.9
1727476543,481.33984375,56.2
1727476543,481.33984375,46.7
1727476543,481.33984375,56.8
1727476546,481.33984375,49.8
1727476546,481.33984375,55.3
1727476546,481.33984375,52.5
1727476546,481.33984375,39.4
1727476548,481.359375,49.9
1727476548,481.359375,44.7
1727476548,481.359375,52.0
1727476548,481.359375,40.6
1727476551,481.3671875,49.9
1727476551,481.3671875,55.6
1727476551,481.3671875,49.9
1727476551,481.3671875,40.6
1727476553,481.3671875,49.9
1727476553,481.3671875,37.5
1727476553,481.3671875,52.1
1727476553,481.3671875,41.9
1727476555,481.3671875,49.8
1727476555,481.3671875,55.6
1727476555,481.3671875,48.2
1727476555,481.3671875,51.2
1727476557,481.3671875,49.9
1727476557,481.3671875,38.2
1727476557,481.3671875,50.3
1727476557,481.3671875,58.7
1727476560,481.3671875,49.8
1727476560,481.3671875,39.4
1727476560,481.3671875,51.6
1727476560,481.3671875,55.6
1727476562,481.3671875,49.9
1727476562,481.3671875,36.4
1727476562,481.3671875,51.6
1727476562,481.3671875,58.7
1727476564,481.375,49.8
1727476564,481.375,45.9
1727476564,481.375,50.4
1727476564,481.375,56.8
1727476566,481.37890625,49.9
1727476566,481.37890625,39.4
1727476566,481.37890625,52.8
1727476566,481.37890625,40.6
1727476568,481.37890625,49.8
1727476568,481.37890625,38.2
1727476568,481.37890625,52.0
1727476568,481.37890625,40.6
1727476570,481.37890625,49.8
1727476570,481.37890625,55.6
1727476570,481.37890625,47.3
1727476570,481.37890625,55.6
1727476572,481.37890625,49.8
1727476572,481.37890625,53.2
1727476572,481.37890625,46.4
1727476572,481.37890625,56.5
1727476575,481.37890625,49.9
1727476575,481.37890625,41.2
1727476575,481.37890625,49.6
1727476575,481.37890625,56.3
1727476577,481.37890625,49.8
1727476577,481.37890625,55.3
1727476577,481.37890625,46.4
1727476577,481.37890625,56.5
1727476579,481.37890625,49.9
1727476579,481.37890625,40.0
1727476579,481.37890625,52.4
1727476579,481.37890625,40.6
1727476581,481.37890625,49.9
1727476581,481.37890625,37.1
1727476581,481.37890625,52.0
1727476581,481.37890625,48.8
1727476583,481.37890625,49.9
1727476583,481.37890625,54.3
1727476583,481.37890625,52.0
1727476583,481.37890625,37.1
1727476585,481.37890625,49.9
1727476585,481.37890625,55.3
1727476585,481.37890625,50.8
1727476585,481.37890625,42.4
1727476587,481.37890625,49.8
1727476587,481.37890625,54.3
1727476587,481.37890625,51.2
1727476587,481.37890625,40.6
1727476590,481.37890625,49.9
1727476590,481.37890625,40.0
1727476590,481.37890625,51.2
1727476590,481.37890625,56.8
1727476592,481.37890625,49.9
1727476592,481.37890625,56.3
1727476592,481.37890625,49.6
1727476592,481.37890625,44.1
1727476594,481.37890625,49.9
1727476594,481.37890625,50.0
1727476594,481.37890625,48.7
1727476594,481.37890625,55.6
1727476596,481.37890625,49.8
1727476596,481.37890625,53.3
1727476596,481.37890625,48.5
1727476596,481.37890625,45.5
1727476598,481.37890625,49.8
1727476598,481.37890625,38.2
1727476598,481.37890625,51.6
1727476598,481.37890625,58.7
1727476600,481.37890625,49.8
1727476600,481.37890625,54.3
1727476600,481.37890625,51.6
1727476600,481.37890625,39.4
1727476602,481.37890625,49.8
1727476602,481.37890625,54.3
1727476602,481.37890625,50.8
1727476602,481.37890625,45.7
1727476604,481.37890625,49.9
1727476604,481.37890625,52.1
1727476604,481.37890625,50.8
1727476604,481.37890625,42.4
1727476606,481.37890625,49.8
1727476606,481.37890625,50.0
1727476606,481.37890625,51.2
1727476606,481.37890625,38.7
1727476609,481.37890625,49.8
1727476609,481.37890625,55.3
1727476609,481.37890625,50.8
1727476609,481.37890625,39.4
1727476611,481.38671875,49.9
1727476611,481.38671875,41.7
1727476611,481.38671875,49.6
1727476611,481.38671875,54.8
1727476613,481.38671875,49.9
1727476613,481.38671875,39.4
1727476613,481.38671875,50.4
1727476613,481.38671875,56.8
1727476616,481.46484375,49.9
1727476616,481.46484375,39.4
1727476616,481.46484375,50.4
1727476616,481.46484375,56.5
1727476618,481.46484375,49.8
1727476618,481.46484375,55.3
1727476618,481.46484375,49.2
1727476618,481.46484375,40.6
1727476620,481.4765625,49.9
1727476620,481.4765625,48.8
1727476620,481.4765625,51.1
1727476620,481.4765625,41.2
1727476623,481.515625,49.9
1727476623,481.515625,55.3
1727476623,481.515625,50.0
1727476623,481.515625,48.7
1727476625,481.515625,49.9
1727476625,481.515625,38.2
1727476625,481.515625,52.9
1727476625,481.515625,47.2
1727476627,481.515625,49.9
1727476627,481.515625,38.2
1727476627,481.515625,53.1
1727476627,481.515625,37.5
1727476629,481.515625,49.8
1727476629,481.515625,54.3
1727476629,481.515625,50.4
1727476629,481.515625,40.6
1727476784,519.4921875,50.2
1727476784,519.4921875,53.2
1727476784,519.4921875,48.5
1727476784,519.4921875,56.8
1727476878,523.36328125,50.2
1727476878,523.36328125,56.5
1727476878,523.36328125,47.4
1727476878,523.36328125,56.8
1727477065,524.625,50.3
1727477065,524.625,52.1
1727477065,524.625,47.4
1727477065,524.625,58.7
1727477262,527.05078125,50.3
1727477262,527.05078125,46.2
1727477262,527.05078125,49.7
1727477262,527.05078125,58.7
1727477447,530.25,50.2
1727477447,530.25,40.0
1727477447,530.25,52.4
1727477447,530.25,37.1
1727477641,531.3828125,50.2
1727477641,531.3828125,43.2
1727477641,531.3828125,51.2
1727477641,531.3828125,42.4
1727477874,532.46875,50.2
1727477874,532.46875,39.4
1727477874,532.46875,50.6
1727477874,532.46875,54.3
1727478149,539.4453125,50.2
1727478149,539.4453125,56.2
1727478149,539.4453125,50.6
1727478149,539.4453125,40.6
1727478384,539.87890625,50.2
1727478384,539.87890625,55.3
1727478384,539.87890625,48.9
1727478384,539.87890625,55.6
1727478697,547.6640625,50.2
1727478697,547.6640625,40.0
1727478697,547.6640625,50.8
1727478697,547.6640625,50.0
1727479061,562.5703125,50.2
1727479061,562.5703125,39.4
1727479061,562.5703125,51.3
1727479061,562.5703125,42.4
1727479423,567.55859375,50.2
1727479423,567.55859375,53.2
1727479423,567.55859375,50.0
1727479423,567.55859375,55.6
1727479844,572.56640625,50.2
1727479844,572.56640625,38.2
1727479844,572.56640625,51.8
1727479844,572.56640625,41.2
1727480233,578.9921875,50.2
1727480233,578.9921875,54.3
1727480233,578.9921875,48.6
1727480233,578.9921875,57.8
1727480672,472.4140625,50.2
1727480672,472.4140625,51.3
1727480672,472.4140625,49.0
1727480672,472.4140625,56.8
1727481180,455.328125,50.2
1727481180,455.328125,38.2
1727481180,455.328125,51.4
1727481180,455.328125,40.6
1727481734,454.31640625,50.2
1727481734,454.31640625,54.2
1727481734,454.31640625,49.0
1727481734,454.31640625,54.8
1727482384,478.26171875,50.2
1727482384,478.26171875,54.5
1727482384,478.26171875,50.4
1727482384,478.26171875,41.2
1727483083,479.79296875,50.3
1727483083,479.79296875,37.1
1727483083,479.79296875,51.1
1727483083,479.79296875,41.2
1727484021,456.63671875,50.3
1727484021,456.63671875,56.2
1727484021,456.63671875,48.3
1727484021,456.63671875,57.8
1727484965,482.03515625,50.3
1727484965,482.03515625,54.0
1727484965,482.03515625,49.0
1727484965,482.03515625,48.6
1727485884,487.25,50.3
1727485884,487.25,50.0
1727485884,487.25,51.2
1727485884,487.25,38.2
1727486974,495.96875,50.3
1727486974,495.96875,54.3
1727486974,495.96875,48.8
1727486974,495.96875,55.6
1727488001,484.0390625,50.3
1727488001,484.0390625,53.2
1727488001,484.0390625,49.0
1727488001,484.0390625,54.8
1727489213,470.0546875,50.3
1727489213,470.0546875,50.0
1727489213,470.0546875,48.1
1727489213,470.0546875,56.8
1727490570,553.25390625,50.3
1727490570,553.25390625,38.2
1727490570,553.25390625,50.9
1727490570,553.25390625,48.6
1727492100,473.94921875,50.3
1727492100,473.94921875,56.2
1727492100,473.94921875,50.5
1727492100,473.94921875,39.4
1727493621,576.0,50.3
1727493621,576.0,51.1
1727493622,576.0,49.3
1727493622,576.0,56.5
1727495224,499.15234375,50.3
1727495224,499.15234375,53.2
1727495224,499.15234375,50.1
1727495224,499.15234375,57.8
1727497135,511.734375,50.3
1727497135,511.734375,42.9
1727497135,511.734375,51.0
1727497135,511.734375,40.6
1727498991,540.421875,50.3
1727498991,540.421875,45.2
1727498991,540.421875,50.3
1727498991,540.421875,53.3
1727501009,596.2578125,50.3
1727501009,596.2578125,38.2
1727501051,596.265625,49.7
1727501051,596.265625,53.2
</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>