| n_samples | const | max_depth | threshold | result |
|---|---|---|---|---|
| 108 | 0.358754 | 2 | 0.265565 | 0.278940 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| const | range | 0.1 | 1 | float | |
| max_depth | range | 2 | 4 | int | |
| threshold | range | 0.2 | 0.8 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 492 | 16 | 508 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,const,max_depth,threshold 0,0_0,COMPLETED,Sobol,0.296618570327128572294839159440,769,0.179557363688945759161441628748,4,0.684567558765411421362045985006 1,1_0,COMPLETED,Sobol,0.293038880934023526769749423693,200,0.793849396519362926483154296875,4,0.584290709905326499651323501894 2,2_0,COMPLETED,Sobol,0.298050446084370479482572591223,721,0.281833939626812912671027788747,3,0.548442964069545402239214126894 3,3_0,COMPLETED,Sobol,0.286925872893490474524469391326,119,0.481403946131467863622788172506,4,0.417066838219761870654167523753 4,4_0,COMPLETED,Sobol,0.294746117413812069862899534201,225,0.219617746211588388272062388751,2,0.776847092993557586382280533144 5,5_0,COMPLETED,Sobol,0.290946139442669893249160395499,134,0.765126441605389118194580078125,3,0.701942122541368007659912109375 6,6_0,COMPLETED,Sobol,0.292763520211477001886635207484,200,0.746354395803064063485976475931,4,0.704542430862784563316836283775 7,7_0,COMPLETED,Sobol,0.290009912986011642033190582879,207,0.507500544004142351006692024384,3,0.204876374453306198120117187500 8,8_0,COMPLETED,Sobol,0.300804053309835839336017215828,916,0.316926635522395416799668055319,4,0.705207260511815592352036219381 9,9_0,COMPLETED,Sobol,0.288412820795241775506667636364,135,0.634223212115466616900505414378,2,0.652044752985239117748506032513 10,10_0,COMPLETED,Sobol,0.292653375922458436342310506006,574,0.755943340808153174670280805003,2,0.270165017060935541692856531881 11,11_0,COMPLETED,Sobol,0.296122921026544805300773077761,475,0.533335997816175244601311078441,2,0.448894356191158361291115852509 12,12_0,COMPLETED,Sobol,0.297830157506333348393923188269,512,0.396652586106211013650124641572,4,0.599712880328297615051269531250 13,13_0,COMPLETED,Sobol,0.285769357858794981197547713236,142,0.115800998918712150231868918127,4,0.742901873216033070690400563763 14,14_0,COMPLETED,Sobol,0.294966405991849311973851399671,967,0.668990551866590954510627398122,3,0.707836289703846155418887065025 15,15_0,COMPLETED,Sobol,0.293479458090098010991653154633,220,0.559539464954286969167185361584,4,0.473165405727922983025734993134 16,16_0,COMPLETED,Sobol,0.298986672541028730698542403843,907,0.972348522208630994256850499369,2,0.336468357220292113574089398753 17,17_0,COMPLETED,Sobol,0.295462055292433078967917481350,718,0.660013105254620313644409179688,3,0.672342156805098123406594368134 18,18_0,COMPLETED,Sobol,0.300748981165326556563854865090,632,0.320575515180826164929328569997,4,0.320075014792382761541489344381 19,19_0,COMPLETED,Sobol,0.293314241656570051652863639902,204,0.419829713460058040475075813447,3,0.637583696842193736742387955019 20,20_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.178817477458164175718735577902,4,0.612318582597064509087658734643 21,21_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.166096812229695856011346677406,4,0.800000000000000044408920985006 22,22_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.384949810979415718570351145900,3,0.346852249515900434850834699319 23,23_0,COMPLETED,BoTorch,0.285824430003304374992012526491,122,0.100000000000000005551115123126,4,0.570140123873906334850403254677 24,24_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.625955711458969332738888624590,2,0.318947038178190145352175477456 25,25_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.270486862757708834692493837792,4,0.658530852418251155810935415502 26,26_0,COMPLETED,BoTorch,0.286154862870360182647289093438,100,0.100000000000000005551115123126,3,0.693723366458314338878210492112 27,27_0,COMPLETED,BoTorch,0.290064985130521035827655396133,143,0.100000000000000005551115123126,4,0.800000000000000044408920985006 28,28_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.579674771598672200489943406865,3,0.200000000000000011102230246252 29,29_0,COMPLETED,BoTorch,0.288467892939751058278829987103,100,0.100000000000000005551115123126,4,0.684444219083301330641688764445 30,30_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.285605756310100822314979041039,3,0.543310363468470391978826228296 31,31_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.183098479853907675218849249177,4,0.510293711603962640843690223846 32,32_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.791020412958522545210371390567,3,0.200000000000000011102230246252 33,33_0,COMPLETED,BoTorch,0.287641810772111483629487338476,148,0.100000000000000005551115123126,4,0.630613912475584736938571950304 34,34_0,COMPLETED,BoTorch,0.284998347835664689320367415348,100,0.209669280375856165177239631703,4,0.336619098613069600567371253419 35,35_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.354009725358397697725365560473,4,0.200000000000000011102230246252 36,36_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.860091372833587608504046784219,2,0.411238472239769281557641988911 37,37_0,COMPLETED,BoTorch,0.287476594338583524290697823744,100,0.100000000000000005551115123126,4,0.480384007833607129533959323453 38,38_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.662137609176870700622430376825,3,0.246987728661824501585897451150 39,39_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.233335951135688307589433065914,3,0.304177652883512728010373393772 40,40_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.422841435041076407763682709628,2,0.200000000000000011102230246252 41,41_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.909710362196232136255957811954,2,0.200000000000000011102230246252 42,42_0,COMPLETED,BoTorch,0.286980945037999757296631742065,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252 43,43_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.541617983654499979273566623306,3,0.409797974348826921087152186374 44,44_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.269760893808887347589120508928,2,0.346548974969344891761124927143 45,45_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.639194822828227038336024179443,2,0.200000000000000011102230246252 46,46_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.427933794673768019833914877381,2,0.438816486938387706473463367729 47,47_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.521871732680072253351966082846,2,0.200000000000000011102230246252 48,48_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.324347418889465166635943660367 49,49_0,COMPLETED,BoTorch,0.286925872893490474524469391326,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252 50,50_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252 51,51_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.427573606081281254454040663404,4,0.200000000000000011102230246252 52,52_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.291015047525964298813505592989,4,0.358822731145480855463603120370 53,53_0,COMPLETED,BoTorch,0.287421522194074241518535473006,100,0.100000000000000005551115123126,3,0.388374443291003790257320815726 54,54_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252 55,55_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.358173282486457189577322424157,3,0.200000000000000011102230246252 56,56_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,3,0.200000000000000011102230246252 57,57_0,COMPLETED,BoTorch,0.284943275691155406548205064610,142,1.000000000000000000000000000000,2,0.200000000000000011102230246252 58,58_0,COMPLETED,BoTorch,0.288963542240334825272896068782,123,0.812338561718944696110611403128,2,0.200000000000000011102230246252 59,59_0,COMPLETED,BoTorch,0.289404119396409309494799799722,131,1.000000000000000000000000000000,3,0.200000000000000011102230246252 60,60_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.284409846148573286495064849078,2,0.680337460491085677105616014160 61,61_0,COMPLETED,BoTorch,0.280372287696882938057285628020,136,0.195518013588302175254085568668,3,0.200000000000000011102230246252 62,62_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.320164226433103493718590470962,4,0.423908990863908097246337547404 63,63_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.912690057016043665427673658996,2,0.200000000000000011102230246252 64,64_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,4,0.382552280461466942540482705226 65,65_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.397955310164891606916626187740,4,0.800000000000000044408920985006 66,66_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.828593850442731483241232126602,2,0.200000000000000011102230246252 67,67_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.200000000000000011102230246252 68,68_0,COMPLETED,BoTorch,0.284392554246062356781976632192,133,0.993096025468156784477002929634,2,0.200000000000000011102230246252 69,69_0,COMPLETED,BoTorch,0.284337482101553074009814281453,141,0.179897476667295441732363769916,2,0.200000000000000011102230246252 70,70_0,COMPLETED,BoTorch,0.289238902962881350156010284991,138,0.100000000000000005551115123126,4,0.200000000000000011102230246252 71,71_0,COMPLETED,BoTorch,0.286209935014869465419451444177,144,0.356366974601144081979953170958,3,0.200000000000000011102230246252 72,72_0,COMPLETED,BoTorch,0.287641810772111483629487338476,134,0.100000000000000005551115123126,2,0.200000000000000011102230246252 73,73_0,COMPLETED,BoTorch,0.286815728604471908980144689849,142,0.100000000000000005551115123126,3,0.200000000000000011102230246252 74,74_0,COMPLETED,BoTorch,0.290009912986011642033190582879,150,0.410769052352913832670822102955,2,0.200000000000000011102230246252 75,75_0,COMPLETED,BoTorch,0.290450490142086126255094313819,132,0.100000000000000005551115123126,3,0.200000000000000011102230246252 76,76_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.297771436663977373537903758915,4,0.200000000000000011102230246252 77,77_0,COMPLETED,BoTorch,0.288853397951316259728571367305,144,0.100000000000000005551115123126,2,0.200000000000000011102230246252 78,78_0,COMPLETED,BoTorch,0.282960678488820338571940737893,135,0.100000000000000005551115123126,3,0.293579571476167477950269812936 79,79_0,COMPLETED,BoTorch,0.283566472078422782132633983565,141,0.352268393914146660250708009698,2,0.200000000000000011102230246252 80,80_0,COMPLETED,BoTorch,0.289349047251900026722637448984,138,0.110783829879258777229011911913,2,0.200000000000000011102230246252 81,81_0,COMPLETED,BoTorch,0.289128758673862784611685583513,138,0.344641565714975728340618843504,3,0.200000000000000011102230246252 82,82_0,COMPLETED,BoTorch,0.288633109373279017617619501834,128,0.104782806984311388509567564142,3,0.200000000000000011102230246252 83,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 84,84_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.398383198156441409309991286136 85,85_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.765357555447992443653504324175,4,0.200000000000000011102230246252 86,86_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.564967991994544727063498612551 87,87_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.691377220392203617471693632979,4,0.385564421944849078371930772846 88,88_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.349197712726325493193257898383,3,0.744925537295982431729157724476 89,89_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.313939683012984360743757861201 90,90_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.739387922178985190768685242801 91,91_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.520556345756132299307239463815,4,0.625751177418093051940672921774 92,92_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.899242156480864696099786215200,4,0.200000000000000011102230246252 93,93_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.281060179359338468962903334614,3,0.200000000000000011102230246252 94,94_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006 95,95_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.800000000000000044408920985006 96,96_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.367814266755175922618548156606,4,0.602559771548226041915086170775 97,97_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.785260500601631550310344209720 98,98_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.516999050797131820544905167480 99,99_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.390508661296323333900204488600 100,100_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.580252612203764339682265926967,2,0.800000000000000044408920985006 101,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 102,102_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.432247715942113308607019916963,3,0.519262095959112324194961729518 103,103_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.465262734069874728248805695330,2,0.622119548210290362888486015436 104,104_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.317161112471009198898741487938,2,0.579081237761454481471901090117 105,105_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.307502294693420386018090084690,3,0.417798898481892444500829242315 106,106_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.125425087096521364893320082956,2,0.800000000000000044408920985006 107,107_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.653496242017264639123652614217,4,0.200000000000000011102230246252 108,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 109,109_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.857098854977714541547584303771,3,0.372992394291094209179959761968 110,110_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.623484463904158792146859013883 111,111_0,COMPLETED,BoTorch,0.296508426038109895728211995447,287,1.000000000000000000000000000000,2,0.200000000000000011102230246252 112,112_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.788746489764244218534372521390,4,0.800000000000000044408920985006 113,113_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.646774774932152185513700715092,2,0.483402304101228252886102154662 114,114_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.346851953112389921329850039911,2,0.800000000000000044408920985006 115,115_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006 116,116_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.233383952882170481180068577487,2,0.531567435485934103311933540681 117,117_0,COMPLETED,BoTorch,0.287421522194074241518535473006,343,1.000000000000000000000000000000,2,0.200000000000000011102230246252 118,118_0,COMPLETED,BoTorch,0.291111355876197852587949910230,336,0.828081991070277134703303545393,2,0.200000000000000011102230246252 119,119_0,COMPLETED,BoTorch,0.300253331864742789569788783410,380,0.474033850860019478901108413993,2,0.200000000000000011102230246252 120,120_0,COMPLETED,BoTorch,0.292598303777949153570148155268,356,0.831229460367008288201873256185,2,0.204300670342650647626214777119 121,121_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.343643042985201430106201314629,3,0.800000000000000044408920985006 122,122_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.814039802987516769583464792959,4,0.581742537653108238160371001868 123,123_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.508178103698920025088625607168,4,0.434834805374892496843131084461 124,124_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.619112990334443225037830416113,4,0.800000000000000044408920985006 125,125_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.759275514527912087636707383353,2,0.800000000000000044408920985006 126,126_0,COMPLETED,BoTorch,0.289459191540918592266962150461,295,1.000000000000000000000000000000,2,0.247024782033900408562132611223 127,127_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.767062041687756490659921837505,4,0.800000000000000044408920985006 128,128_0,COMPLETED,BoTorch,0.283456327789404105566006819572,107,0.979066738859888596735459032061,3,0.226162409380527368307767233091 129,129_0,COMPLETED,BoTorch,0.290064985130521035827655396133,109,1.000000000000000000000000000000,3,0.200000000000000011102230246252 130,130_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,1.000000000000000000000000000000,2,0.576483043668023942274203363922 131,131_0,COMPLETED,BoTorch,0.280096926974336413174171411811,108,1.000000000000000000000000000000,4,0.200000000000000011102230246252 132,132_0,RUNNING,BoTorch,,108,0.827926161482887001952235550561,3,0.200000000000000011102230246252 133,133_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,2,0.200000000000000011102230246252 134,134_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,1.000000000000000000000000000000,4,0.586248925548114474537442220026 135,135_0,COMPLETED,BoTorch,0.283456327789404105566006819572,107,0.819430296144358805143781410152,3,0.526801248954669509849679798208 136,136_0,COMPLETED,BoTorch,0.289624407974446551605751665193,109,0.853667023177145045664815370401,3,0.378182157321190315357739564206 137,137_0,COMPLETED,BoTorch,0.281914307743143521811646223796,107,1.000000000000000000000000000000,4,0.799877619134160333658201125218 138,138_0,COMPLETED,BoTorch,0.289238902962881350156010284991,109,1.000000000000000000000000000000,4,0.200000000000000011102230246252 139,139_0,COMPLETED,BoTorch,0.284007049234497155332235251990,107,0.999968726225602622115218309773,2,0.200000000000000011102230246252 140,140_0,COMPLETED,BoTorch,0.283896904945478589787910550513,107,1.000000000000000000000000000000,4,0.200000000000000011102230246252 141,141_0,COMPLETED,BoTorch,0.292267870910893234892569125805,431,1.000000000000000000000000000000,2,0.800000000000000044408920985006 142,142_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.510864858331890769882477343344 143,143_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.569990745518046293405234337115 144,144_0,COMPLETED,BoTorch,0.297114219627712339288905241119,405,1.000000000000000000000000000000,4,0.800000000000000044408920985006 145,145_0,COMPLETED,BoTorch,0.294360612402246979435460616514,457,1.000000000000000000000000000000,3,0.800000000000000044408920985006 146,146_0,COMPLETED,BoTorch,0.289844696552483793716703530663,470,0.876927057534962917095811008039,2,0.800000000000000044408920985006 147,147_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.609179155357959523087174602551 148,148_0,COMPLETED,BoTorch,0.293864963101663212441394534835,418,0.866425851412028547038346459885,4,0.461375642329410640130049614527 149,149_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.327052477705765953785999045067 150,150_0,COMPLETED,BoTorch,0.295351911003414513423592779873,530,1.000000000000000000000000000000,2,0.800000000000000044408920985006 151,151_0,COMPLETED,BoTorch,0.295737416014979603851031697559,437,0.891500859174904625170654526300,3,0.599380105424832887450747875846 152,152_0,COMPLETED,BoTorch,0.299757682564159022575722701731,408,0.956882463491530721455546881771,3,0.739967263343734638070259279630 153,153_0,COMPLETED,BoTorch,0.293204097367551486108538938424,460,1.000000000000000000000000000000,2,0.800000000000000044408920985006 154,154_0,COMPLETED,BoTorch,0.300418548298270748908578298142,486,0.794464014810841057112611451885,3,0.800000000000000044408920985006 155,155_0,COMPLETED,BoTorch,0.293975107390681777985719236312,456,0.956895177724336298830110081326,3,0.703049755182721947122104211303 156,156_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.351694496799406119968978146062,2,0.200000000000000011102230246252 157,157_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.332249148327572685346353864588,2,0.800000000000000044408920985006 158,158_0,COMPLETED,BoTorch,0.287146161471527716635421256797,100,0.100000000000000005551115123126,2,0.467968542994602276774429583384 159,159_0,COMPLETED,BoTorch,0.295517127436942361740079832089,388,1.000000000000000000000000000000,4,0.200000000000000011102230246252 160,160_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.463222914027314525142742240860,2,0.800000000000000044408920985006 161,161_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.243029145210275188127013734629,2,0.457928619495715505394173305831 162,162_0,COMPLETED,BoTorch,0.288853397951316259728571367305,122,1.000000000000000000000000000000,3,0.630980500979821279372572462307 163,163_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.489519423569913791904895106200 164,164_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.285825988507109385317050964659 165,165_0,COMPLETED,BoTorch,0.289734552263465117150076366670,126,1.000000000000000000000000000000,4,0.441763672274622654079223593726 166,166_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.478360508781016213752934618242 167,167_0,RUNNING,BoTorch,,357,0.875385912798097742815173205599,4,0.226601519216319768901257702964 168,168_0,COMPLETED,BoTorch,0.296618570327128572294839159440,320,1.000000000000000000000000000000,4,0.800000000000000044408920985006 169,169_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.321729756357146678968916830854,3,0.683755908700369063879520581395 170,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 171,171_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.386537347469540937261456292617,3,0.452237361452681341233983403072 172,172_0,RUNNING,BoTorch,,100,0.442851076644879659838238694647,4,0.800000000000000044408920985006 173,173_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.749131453576931782833980832947,3,0.800000000000000044408920985006 174,174_0,COMPLETED,BoTorch,0.286154862870360182647289093438,100,0.100000000000000005551115123126,3,0.664759815216902749668292926799 175,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006 176,176_0,COMPLETED,BoTorch,0.288357748650732492734505285625,146,0.913304063965983736750331445364,4,0.200000000000000011102230246252 177,177_0,COMPLETED,BoTorch,0.303117083379226825989860572008,1000,0.100000000000000005551115123126,2,0.800000000000000044408920985006 178,178_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.819078586996064061942490752699,3,0.692991140352794854351259346004 179,179_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.680629625518879510792658038554 180,180_0,COMPLETED,BoTorch,0.297114219627712339288905241119,973,0.305107133977624012111107276723,2,0.432554944918137618259379451047 181,181_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.534444286896483711224448143184,3,0.656934345072449410452009033179 182,182_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.672670510253995712623975578026,3,0.800000000000000044408920985006 183,183_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.638551236345445638598050663859,4,0.200000000000000011102230246252 184,184_0,COMPLETED,BoTorch,0.290230201564048884144142448349,306,0.715467724796295279077185114147,4,0.216068289981710598413044976951 185,185_0,RUNNING,BoTorch,,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 186,186_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.586083897691065947022082127660,4,0.200000000000000011102230246252 187,187_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.510475990023492864899878895812,3,0.800000000000000044408920985006 188,188_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.795401824410743141235968778346,4,0.534324789819474177399172276637 189,189_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.339778087826879238164679009060,2,0.731206601191827898489350445743 190,190_0,COMPLETED,BoTorch,0.287476594338583524290697823744,100,0.100000000000000005551115123126,4,0.384868761607754572562356543131 191,191_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.411990047714558271785278975585,3,0.200000000000000011102230246252 192,192_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.494244745014250086434515196743,4,0.800000000000000044408920985006 193,193_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.492121881994626031442408020666,2,0.800000000000000044408920985006 194,194_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.539176992910849639528692023305,3,0.570562078682359752335173652682 195,195_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.415548916567664350374400328292,3,0.629518865148736117554051361367 196,196_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.375181757746088417881935583864,3,0.577394412181205685108409397799 197,197_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.604616181213158521146056045836,2,0.800000000000000044408920985006 198,185_0,COMPLETED,BoTorch,0.288357748650732492734505285625,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 199,199_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.537220651300486240486975475505,2,0.474532449837979597440096313221 200,200_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.588490529623626468236352593522,2,0.200000000000000011102230246252 201,201_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.100000000000000005551115123126,2,0.737202112089112127080170466797 202,202_0,COMPLETED,BoTorch,0.285604141425267132881060661020,100,0.140746335796367305626120014495,3,0.560868113737998830181652465399 203,203_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.487523414397231569239465898136,3,0.200000000000000011102230246252 204,204_0,COMPLETED,BoTorch,0.303282299812754674306347624224,1000,1.000000000000000000000000000000,4,0.200000000000000011102230246252 205,205_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.426118778389122376815123516280,4,0.692454935790626446845408281661 206,206_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.292291847043283425922055585033,4,0.200000000000000011102230246252 207,207_0,COMPLETED,BoTorch,0.283896904945478589787910550513,136,0.179146558813183642833166686614,2,0.514741080584647847651069696440 208,208_0,COMPLETED,BoTorch,0.284117193523515831898862415983,136,0.243861686375428515782814997692,4,0.414171844315302017935920275704 209,209_0,COMPLETED,BoTorch,0.284557770679590316120766146923,136,0.233879209865910042687175973697,2,0.200000000000000011102230246252 210,210_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.271336873343197226837730795523 211,211_0,COMPLETED,BoTorch,0.281473730587069037589742492855,136,0.100000000000000005551115123126,2,0.200000000000000011102230246252 212,212_0,COMPLETED,BoTorch,0.284667914968608881665090848401,142,0.587982981474156396473063068697,2,0.485059096243542320348041130273 213,213_0,COMPLETED,BoTorch,0.283786760656459913221283386520,136,0.100000000000000005551115123126,3,0.544227788723395944359140230517 214,214_0,COMPLETED,BoTorch,0.281969379887652804583808574534,136,0.304483693404447808283919130190,4,0.200000000000000011102230246252 215,215_0,COMPLETED,BoTorch,0.285549069280757739086595847766,136,0.247889120401724505349250193831,2,0.478325259102194366711557904637 216,216_0,COMPLETED,BoTorch,0.284117193523515831898862415983,136,0.295928000250786249480938749912,2,0.200000000000000011102230246252 217,217_0,COMPLETED,BoTorch,0.285769357858794981197547713236,142,0.710146769314897063907210394973,4,0.200000000000000011102230246252 218,218_0,COMPLETED,BoTorch,0.283456327789404105566006819572,136,0.100000000000000005551115123126,4,0.200000000000000011102230246252 219,219_0,COMPLETED,BoTorch,0.283786760656459913221283386520,136,0.100000000000000005551115123126,4,0.262983781682892103770399216955 220,220_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.335391238046071293865679763258,3,0.800000000000000044408920985006 221,221_0,COMPLETED,BoTorch,0.285989646436832223308499578707,136,0.577799123900583633250960247096,3,0.200000000000000011102230246252 222,222_0,COMPLETED,BoTorch,0.283951977089987872560072901251,136,0.693033489466276075852135818423,2,0.200000000000000011102230246252 223,223_0,COMPLETED,BoTorch,0.283786760656459913221283386520,144,0.755528120360757560192155324330,2,0.200000000000000011102230246252 224,224_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.434987241136139290986761807289,4,0.411229533725951279521382275561 225,225_0,COMPLETED,BoTorch,0.287862099350148725740439203946,137,0.801053149092940453002142930927,2,0.200000000000000011102230246252 226,226_0,RUNNING,BoTorch,,136,0.529006030573744179257289488305,3,0.200000000000000011102230246252 227,227_0,COMPLETED,BoTorch,0.285218636413701931431319280819,135,0.956088272559062923861006311199,2,0.200000000000000011102230246252 228,228_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.589865722877299658577499030798,3,0.800000000000000044408920985006 229,229_0,COMPLETED,BoTorch,0.287862099350148725740439203946,137,0.901949690655759250823564343591,4,0.626123362268657057683185485075 230,230_0,COMPLETED,BoTorch,0.284557770679590316120766146923,144,0.814709982695090206838983704074,3,0.662762920886289630395538097218 231,231_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.390367101025506646472251759405,2,0.800000000000000044408920985006 232,232_0,COMPLETED,BoTorch,0.287751955061130049173812039953,143,0.809467463388898855747299876384,2,0.567620844966029380884720012546 233,233_0,COMPLETED,BoTorch,0.287751955061130049173812039953,143,0.823817437307699962367735224689,2,0.526810516369289105753637159069 234,234_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.373997288752769829756061881199,2,0.691675549147858115262010869628 235,235_0,COMPLETED,BoTorch,0.288853397951316259728571367305,122,0.478995137977272000817663410999,4,0.800000000000000044408920985006 236,236_0,COMPLETED,BoTorch,0.288357748650732492734505285625,122,0.430292392699505366060463984468,4,0.200000000000000011102230246252 237,237_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.301729491111216829857255561365,4,0.799999884559750906731778741232 238,238_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.847659141739042443219886990846,2,0.233598064027858792757186279232 239,239_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.642478650847360777120798047690,4,0.284494317658181283814400330812 240,240_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.300318477474537326443737583759,3,0.200000000000000011102230246252 241,241_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.600855604676369670080759988195,4,0.799562720676648686080056904757 242,242_0,COMPLETED,BoTorch,0.291607005176781619582015991909,120,0.241192163602629461305326685761,4,0.460882465827727183516060449620 243,243_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.625201765660296504556470154057,4,0.580000296856238439779929194628 244,244_0,COMPLETED,BoTorch,0.284502698535080922326301333669,121,0.250726469999186518666789424969,4,0.277649162902254498241916280676 245,245_0,COMPLETED,BoTorch,0.290340345853067560710769612342,145,0.584013182605311653716739783704,2,0.654371870580812808881887576717 246,246_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.931056340582933628091666378168,2,0.258868904673573874131164984647 247,247_0,COMPLETED,BoTorch,0.286540367881925273074728011125,148,0.774944932668447661328059439256,4,0.624078542529035829034000926185 248,248_0,COMPLETED,BoTorch,0.283015750633329621344103088632,121,0.604080399945493118352146666439,2,0.331856693430154114921037944441 249,249_0,COMPLETED,BoTorch,0.294140323824209737324508751044,120,0.649205215668988810939765699004,3,0.499514475960462334125367078741 250,250_0,COMPLETED,BoTorch,0.293699746668135253102605020104,120,0.789731800701347741444635630614,4,0.473544897129477559971633127134 251,251_0,COMPLETED,BoTorch,0.290340345853067560710769612342,145,0.402646435366116883791676173132,2,0.629131922277261557141514458635 252,252_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.597872240548220079681129845994,3,0.528081120085982513856492914783 253,253_0,COMPLETED,BoTorch,0.294140323824209737324508751044,120,0.672576584692428536271791017498,3,0.683308627045199901139937992411 254,254_0,COMPLETED,BoTorch,0.286980945037999757296631742065,121,0.133740526382815305694862217933,2,0.509846726668204963672792473517 255,255_0,COMPLETED,BoTorch,0.290340345853067560710769612342,145,0.567677211129134273726037918095,3,0.485937662571209572082153727024 256,256_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.683660008266984253744169564015,3,0.612854411423088718535723273817 257,257_0,COMPLETED,BoTorch,0.292102654477365386576082073589,120,0.864354462490241726158046731143,3,0.200000000000000011102230246252 258,258_0,COMPLETED,BoTorch,0.291937438043837427237292558857,120,0.979275813867895084108283754176,2,0.783680271693660612797316389333 259,259_0,COMPLETED,BoTorch,0.285934574292322940536337227968,121,0.432765090076818181863416157285,2,0.229662417690795345182053210920 260,260_0,COMPLETED,BoTorch,0.293093953078532920564214236947,874,0.531017758697271413659279915009,4,0.629594424925744577947739344381 261,261_0,COMPLETED,BoTorch,0.288688181517788300389781852573,140,0.440898177942369406956402144715,2,0.200000000000000011102230246252 262,262_0,COMPLETED,BoTorch,0.290175129419539601371980097611,140,0.100000000000000005551115123126,3,0.463591401711738626545411534607 263,263_0,COMPLETED,BoTorch,0.281638947020596996928532007587,101,0.262769198773446510664086872566,3,0.200000000000000011102230246252 264,264_0,COMPLETED,BoTorch,0.299592466130631174259235649515,357,0.421760785798012238778653681948,4,0.209365742268198928854872065131 265,265_0,COMPLETED,BoTorch,0.280923009141975987823514060437,101,0.198831362116527743388871840580,2,0.624918515234376137357230618363 266,266_0,COMPLETED,BoTorch,0.287421522194074241518535473006,140,0.607947570853156760151136950299,4,0.728972688554274439454161438334 267,267_0,COMPLETED,BoTorch,0.291386716598744377471064126439,875,0.425346858823126283688509374770,2,0.652642573715641938214560013876 268,268_0,COMPLETED,BoTorch,0.288688181517788300389781852573,140,0.795045066476992245974031447986,2,0.425357450416481763788567604934 269,269_0,COMPLETED,BoTorch,0.289183830818372067383847934252,106,1.000000000000000000000000000000,4,0.800000000000000044408920985006 270,270_0,COMPLETED,BoTorch,0.288908470095825542500733718043,140,0.292513851310422290374901876930,3,0.563148556186527926570306590293 271,271_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.388197174359140473320906039589,2,0.704941586213047477471604906896 272,272_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.329210064200202379147697229200,3,0.800000000000000044408920985006 273,273_0,COMPLETED,BoTorch,0.281308514153541189273255440639,101,0.386362234327053966076448432432,2,0.280173226728228086379601791123 274,274_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,1.000000000000000000000000000000,3,0.485041066690058531030160793307 275,275_0,COMPLETED,BoTorch,0.287476594338583524290697823744,102,0.355057150298940582544560129463,3,0.609140286073968195701411332266 276,276_0,COMPLETED,BoTorch,0.293644674523625970330442669365,342,0.919054343321301314695404016675,4,0.775403220123862091384125960758 277,277_0,COMPLETED,BoTorch,0.281198369864522512706628276646,101,0.974348159053836759824207547354,4,0.202056550336747686724692130156 278,278_0,COMPLETED,BoTorch,0.283125894922348297910730252624,101,1.000000000000000000000000000000,4,0.800000000000000044408920985006 279,279_0,COMPLETED,BoTorch,0.281308514153541189273255440639,101,0.339845532767469649115810170770,2,0.281495683487864356564500667446 280,280_0,COMPLETED,BoTorch,0.286595440026434666869192824379,144,0.997969911446551005695937419659,4,0.200000000000000011102230246252 281,281_0,COMPLETED,BoTorch,0.281638947020596996928532007587,101,0.399121237526260497219254830270,4,0.503515445716535836595539876726 282,282_0,COMPLETED,BoTorch,0.281253442009031795478790627385,101,0.920902670376129939278087022103,2,0.200000000000000011102230246252 283,283_0,COMPLETED,BoTorch,0.286595440026434666869192824379,101,0.100000000000000005551115123126,2,0.200000000000000011102230246252 284,284_0,COMPLETED,BoTorch,0.281198369864522512706628276646,101,0.879623454044248243022252609080,3,0.200000000000000011102230246252 285,285_0,COMPLETED,BoTorch,0.293259169512060768880701289163,468,0.687105029177673021223426985671,4,0.678110727736390472841776499990 286,286_0,COMPLETED,BoTorch,0.285493997136248456314433497027,102,0.893699689683265297013292638439,3,0.200000000000000011102230246252 287,287_0,COMPLETED,BoTorch,0.292983808789514243997587072954,342,0.781674128555828628961421600252,3,0.716514345322489143441657688527 288,288_0,COMPLETED,BoTorch,0.281198369864522512706628276646,101,0.749549943653286976363858684635,4,0.200000000000000011102230246252 289,289_0,COMPLETED,BoTorch,0.282354884899218006033549954736,101,0.811437928530874708066278344631,4,0.512486565828633722219365154160 290,290_0,COMPLETED,BoTorch,0.286870800748981191752307040588,148,0.488089845058091387208776268380,2,0.200000000000000011102230246252 291,291_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.999999987274373114409797835833,2,0.432058622879172682385018333662 292,292_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.828790352078744185781999931351,4,0.271826076944031103099774782095 293,293_0,COMPLETED,BoTorch,0.293920035246172495213556885574,118,0.361378284795742188428846475290,3,0.444047269170259772952391585932 294,294_0,COMPLETED,BoTorch,0.286870800748981191752307040588,102,0.849791530698910890784247840202,2,0.200000000000000011102230246252 295,185_0,COMPLETED,BoTorch,0.288357748650732492734505285625,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006 296,296_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.496381081466202189744763018098,2,0.329259378537570923661803590221 297,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 298,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 299,299_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.905683892203302143286691716639,3,0.800000000000000044408920985006 300,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 301,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 302,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 303,303_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.859194970320032780364272184670,3,0.497546377451658305979265151109 304,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 305,305_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.271721598229404170954381925185 306,306_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.327585732137790464069126983304,2,0.501594037102137457750927751476 307,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 308,308_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.256614649902521307911484882425,2,0.800000000000000044408920985006 309,309_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.680149274786067681297652143257,4,0.800000000000000044408920985006 310,310_0,RUNNING,BoTorch,,789,1.000000000000000000000000000000,2,0.800000000000000044408920985006 311,311_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.326073792383983152021187379432,2,0.427072437793009296314039602294 312,312_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.277690048737403438749993256351,2,0.800000000000000044408920985006 313,313_0,COMPLETED,BoTorch,0.292488159488930477003520991275,789,0.461104125668460484988031566900,2,0.558490567294724238323055942601 314,314_0,RUNNING,BoTorch,,100,0.743565594613862179684815600922,3,0.513406265471625822272017103387 315,315_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.575770066483886622243915098807,4,0.351470789748313361400278154179 316,316_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.552969375833803900022189736774,4,0.800000000000000044408920985006 317,317_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.865293179314119642597802339878,2,0.749320964386531729317653116595 318,318_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.625509408452764703589821237983,4,0.574372690881568503940002301533 319,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 320,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 321,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252 322,322_0,COMPLETED,BoTorch,0.293424385945588728219490803895,132,0.765863142777596483945501404378,4,0.327794091786804631105667340307 323,323_0,COMPLETED,BoTorch,0.289183830818372067383847934252,131,0.448024007378751587538090461749,4,0.314787447358427563415261829505 324,324_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.547187679006647109680727680825,3,0.677814005062404079104965148872 325,325_0,COMPLETED,BoTorch,0.283015750633329621344103088632,121,0.810012754517705380052916552813,2,0.345828844946860280984424207418 326,326_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.295966175793617114475608786961 327,327_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,1.000000000000000000000000000000,3,0.356549088612972631118225308455 328,328_0,COMPLETED,BoTorch,0.290230201564048884144142448349,344,1.000000000000000000000000000000,2,0.200000000000000011102230246252 329,329_0,COMPLETED,BoTorch,0.294140323824209737324508751044,132,0.703031378684943253354333592142,2,0.232597859765709624735308125310 330,330_0,COMPLETED,BoTorch,0.292047582332855992781617260334,132,0.744700515254135830822690422792,3,0.732961588055752510939555577352 331,331_0,COMPLETED,BoTorch,0.290946139442669893249160395499,131,0.559255751765373454453822432697,4,0.496695505042695828468168883774 332,332_0,COMPLETED,BoTorch,0.286209935014869465419451444177,147,0.488486940999286023412651047693,4,0.617909211970323779539171482611 333,333_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.731249794269268171831299696350,3,0.575900539308130787752304513560 334,334_0,RUNNING,BoTorch,,108,0.659525106429545204811404346401,2,0.414965576351546738820275095350 335,335_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.860289181169299532747629655205,3,0.621403974573101036682487574581 336,336_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.686621725531830762179197336081,3,0.321331660625882808979270066629 337,337_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.775680077326724615183195510326,3,0.765095585774862696482045976154 338,338_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.755986415837068159717659909802,3,0.447955303988349928800971611054 339,339_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,1.000000000000000000000000000000,3,0.362316615319076218426630475733 340,340_0,COMPLETED,BoTorch,0.286209935014869465419451444177,147,0.584809881453425828290448862390,3,0.606091771328936435025980244973 341,341_0,COMPLETED,BoTorch,0.300363476153761466136415947403,542,0.567144298527622492400723785977,2,0.640739819164433455078722090548 342,342_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.308573508728000600598306846223 343,343_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.769709317415815719165550490288,2,0.500582777128644407227398005489 344,344_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.661037574141976280728272286069,3,0.746922623477147995529890067701 345,345_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.389721735973463490054768953996 346,346_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.935393795205001321590998486499,3,0.400905635298875240302152178629 347,347_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.945465634849333769196277899027,3,0.362413854238589905332190710396 348,348_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,4,0.281761424460074294540135042553 349,349_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.923970451027794847931318145129,4,0.359052428107143828395209084192 350,350_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.355222631954872691828484221332 351,351_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.346507955831221348130810611110,2,0.200000000000000011102230246252 352,352_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.543441167414440373661932426330,4,0.751495950193355577440001979994 353,353_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.356605573828709387917967887915 354,354_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.875923336801282759189746229822,3,0.335059727282235986223213330959 355,355_0,COMPLETED,BoTorch,0.297169291772221622061067591858,737,0.942588657760047299305483647913,2,0.800000000000000044408920985006 356,356_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.748011522755665847483896868653,2,0.371803986075063985783373254890 357,357_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.908630430001408639917315213097,4,0.800000000000000044408920985006 358,358_0,COMPLETED,BoTorch,0.301850424055512767118614192441,682,0.848769155119617479954285954591,2,0.311320259711274416325466063427 359,359_0,COMPLETED,BoTorch,0.298821456107500771359752889111,727,0.925147217394715060123644434498,2,0.793218865307228293559660414758 360,360_0,COMPLETED,BoTorch,0.300088115431214941253301731194,817,1.000000000000000000000000000000,2,0.635956305155788492733393013623 361,361_0,COMPLETED,BoTorch,0.297279436061240187605392293335,784,1.000000000000000000000000000000,2,0.632755333053425905731614875549 362,362_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.725682405508452088582771466463,3,0.420817281081158123257068837120 363,363_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.839084716881272507649214276171,3,0.200000000000000011102230246252 364,364_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.775706180071359940875197480636,3,0.287211329509643009672004154709 365,365_0,COMPLETED,BoTorch,0.285604141425267132881060661020,136,1.000000000000000000000000000000,2,0.516948449540925336620489360939 366,366_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.652715812399090822282232693397,3,0.538781503324197164062070442014 367,367_0,COMPLETED,BoTorch,0.289293975107390632928172635729,470,0.129883551836970423387640494184,2,0.671337513224865789496220713772 368,368_0,COMPLETED,BoTorch,0.286209935014869465419451444177,147,0.126312645333904599587171446728,2,0.468133144359302477699458222560 369,369_0,COMPLETED,BoTorch,0.297169291772221622061067591858,790,0.877323461494527778725682765071,4,0.665695876604833536305250163423 370,370_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.634159146482898283814222395449,4,0.614541299737245472201152551861 371,371_0,COMPLETED,BoTorch,0.297554796783786712488506509544,334,0.615588050997825320109768654220,4,0.418224938579480176592539919511 372,372_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.663585615859651589687473460799,3,0.520799148216694596236209235940 373,373_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.295901562927273709124875722409 374,374_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.941939112478402007511135707318,3,0.200000000000000011102230246252 375,375_0,COMPLETED,BoTorch,0.289899768696993076488865881402,296,0.606204066115995976460339988989,2,0.691636013888441092412051602878 376,376_0,COMPLETED,BoTorch,0.289789624407974399922238717409,126,0.360802492587028789117198357417,3,0.233371017567772343070942042687 377,377_0,COMPLETED,BoTorch,0.285163564269192648659156930080,149,0.821344353322974507491949225368,4,0.452785104327192988726835665148 378,378_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.572497688632799706098808201205,3,0.721650478231562031439239035535 379,379_0,COMPLETED,BoTorch,0.289789624407974399922238717409,333,0.119745143665789616216343915767,2,0.622858187223498505069585462479 380,380_0,COMPLETED,BoTorch,0.290725850864632651138208530028,297,0.572119152152188892301865053014,4,0.252409513597130374940036290354 381,381_0,COMPLETED,BoTorch,0.288027315783676574056926256162,125,0.149921055700249272746304995962,3,0.723538440277484928842000044824 382,382_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.433167780743998975800934658764,3,0.491160305973127153666979438640 383,383_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.439721476498541985478141214116,3,0.491035130099368999356812537371 384,384_0,COMPLETED,BoTorch,0.292818592355986395681100020738,612,0.319033482205122731478752484691,2,0.674374244734645023058305923769 385,385_0,COMPLETED,BoTorch,0.287862099350148725740439203946,125,0.715803694455670158625082422077,2,0.430496076691956996995713780052 386,386_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.435516437202817408014254851878,3,0.496263347247793917826896858969 387,387_0,COMPLETED,BoTorch,0.291276572309725700904436962446,298,0.688845290453992342705191731511,2,0.364852686838055273454983762349 388,388_0,COMPLETED,BoTorch,0.291441788743253660243226477178,300,0.345101170973003801289991088197,4,0.276905671347681570093612890560 389,389_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.433233479515099939582967181195,3,0.490719193429342059875608583752 390,390_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.430744038700940801156491488655,3,0.526015186082991692551047435700 391,391_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.441533948709074253180517644068,3,0.484229678034154786825382643656 392,392_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518574187077068926576828289399,3,0.497426713456130875634642052319 393,393_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518831807720019355656404513866,3,0.488809106777320367953620916524 394,394_0,COMPLETED,BoTorch,0.285163564269192648659156930080,149,0.527267021201936247898345300200,4,0.582806701664871140700086016295 395,395_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518983633241679398473422679672,3,0.488733273383903488173984897003 396,396_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.519608514125181275566944805178,3,0.478526030483827746753178189465 397,397_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518069265093745290329252384254,3,0.493814076142934044177934538311 398,398_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,1.000000000000000000000000000000,4,0.781309911625544861735193080676 399,399_0,COMPLETED,BoTorch,0.285328780702720607997946444812,149,0.374772770132353105765332657029,3,0.544431740861973212375346520275 400,400_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.479734628732583101573538897355,4,0.467246355394784429471144449053 401,401_0,COMPLETED,BoTorch,0.285053419980174083114832228603,152,0.449805442812826972165396455239,3,0.488114355320856729836265230915 402,402_0,COMPLETED,BoTorch,0.283456327789404105566006819572,107,0.394048777039265107902110685245,4,0.695924476738605246595170683577 403,403_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.485219641300954518392529735138,3,0.544576105291492584825618905597 404,404_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.396472581489590525372079810040,2,0.312809872518817477793362513694 405,405_0,COMPLETED,BoTorch,0.290230201564048884144142448349,209,0.340995737811665755589984883045,2,0.517365898852744754421451034432 406,406_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.449626310888963764966774760978,3,0.499956625221870676334390282136 407,407_0,RUNNING,BoTorch,,877,0.688107543483078010204678776063,2,0.374787519187230466766180825289 408,408_0,COMPLETED,BoTorch,0.285108492124683365886994579341,210,0.440532131638879942414632751024,2,0.352489795724450272196293099114 409,409_0,COMPLETED,BoTorch,0.280372287696882938057285628020,152,0.630873357158082792572884045512,2,0.781362083632988868586721764586 410,410_0,COMPLETED,BoTorch,0.287641810772111483629487338476,106,0.313451890262590571190060018125,3,0.319787260025966690868415298610 411,411_0,COMPLETED,BoTorch,0.281638947020596996928532007587,101,0.514849490137525123145678662695,4,0.200000000000000011102230246252 412,412_0,COMPLETED,BoTorch,0.292322943055402628687033939059,293,0.880406827522119961315638647648,4,0.219085635736898098890890196344 413,413_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.367007742885556043077599497337,3,0.307705319881438843498955293398 414,414_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.394715003853915336051727535960,3,0.360792435707713188275391757998 415,415_0,COMPLETED,BoTorch,0.281969379887652804583808574534,152,0.767826228657558451651254927128,3,0.800000000000000044408920985006 416,416_0,COMPLETED,BoTorch,0.282905606344311055799778387154,152,0.741742060254327983948030578176,2,0.386306686767372786661667305452 417,417_0,COMPLETED,BoTorch,0.282575173477255248144501820207,152,0.729632989677174714771012986603,3,0.711460125175196411717593036883 418,418_0,COMPLETED,BoTorch,0.284117193523515831898862415983,151,0.767322295534190623733650227223,2,0.699041715104094407706725178286 419,419_0,COMPLETED,BoTorch,0.288412820795241775506667636364,153,0.932993725062010059723149879574,2,0.295401011174526884062174758583 420,420_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.382481124443396658385552200343,3,0.251364065947366599473866699554 421,421_0,COMPLETED,BoTorch,0.286320079303888141986078608170,153,0.623579682631386189584077328618,2,0.800000000000000044408920985006 422,422_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.325212428692509880612249162368,3,0.295035188018432648071609492035 423,423_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.435902271436965249584716275422,2,0.425140366221556154080474243528 424,424_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.708554734070600211737200879725,3,0.380803442759677257534178806964 425,425_0,COMPLETED,BoTorch,0.301905496200022049890776543180,328,0.906370356001680699264966278861,4,0.279384287589389601613731883845 426,426_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.332146525054038521673760442354,2,0.200000000000000011102230246252 427,427_0,COMPLETED,BoTorch,0.280372287696882938057285628020,152,0.787160366003497591336213190516,2,0.800000000000000044408920985006 428,428_0,COMPLETED,BoTorch,0.298821456107500771359752889111,954,0.603724400141488315441051781818,3,0.567647453770046883647637514514 429,429_0,COMPLETED,BoTorch,0.280372287696882938057285628020,152,0.578157609151459750407298088248,2,0.800000000000000044408920985006 430,430_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.403169554708506350237939841463,2,0.413548320351045717302440607455 431,431_0,COMPLETED,BoTorch,0.293699746668135253102605020104,329,0.903813404754384319694793248345,3,0.420754969547359358728044753661 432,432_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.356121459760811998052076887689,2,0.200000000000000011102230246252 433,433_0,COMPLETED,BoTorch,0.293259169512060768880701289163,530,0.766525665003749856474257740047,2,0.743441145268714409510835139372 434,434_0,COMPLETED,BoTorch,0.292047582332855992781617260334,211,0.443240986517338120265208090132,3,0.774619749946934943451992694463 435,435_0,COMPLETED,BoTorch,0.291331644454235094698901775701,211,0.719116323162267812030279401370,2,0.560447850011962733773884792754 436,436_0,COMPLETED,BoTorch,0.284612842824099598892928497662,151,1.000000000000000000000000000000,4,0.800000000000000044408920985006 437,437_0,COMPLETED,BoTorch,0.291166428020707135360112260969,211,0.231768649091980916132627044135,3,0.739516115603226831254346507194 438,438_0,COMPLETED,BoTorch,0.290009912986011642033190582879,150,0.916428631837954088545927788800,4,0.800000000000000044408920985006 439,439_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.321346124557125800702550577626,2,0.200000000000000011102230246252 440,440_0,COMPLETED,BoTorch,0.291056283731688458793485096976,211,0.708514579968206881233072635951,2,0.252262766976825369980019786453 441,441_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.844083767357400871489403471060,3,0.200000000000000011102230246252 442,442_0,COMPLETED,BoTorch,0.300969269743363798674806730560,344,0.144361651712485050680356835073,2,0.497926161918553156215949684338 443,443_0,COMPLETED,BoTorch,0.291496860887762943015388827916,758,0.524418244049278836627081545885,4,0.409158553079630671334143698914 444,444_0,COMPLETED,BoTorch,0.287146161471527716635421256797,153,0.100000000000000005551115123126,3,0.345837338497483270938204213962 445,445_0,COMPLETED,BoTorch,0.296012776737526128734145913768,758,0.359302075589273184341720934754,3,0.429124784818826709997807711261 446,446_0,COMPLETED,BoTorch,0.295131622425377271312640914402,759,0.889168723673551930630765127717,3,0.226877840579513961882796024838 447,447_0,COMPLETED,BoTorch,0.292157726621874669348244424327,757,0.523109411953387448690477867785,3,0.693174437011801369834529396030 448,448_0,COMPLETED,BoTorch,0.293093953078532920564214236947,308,0.803662785608411422977326310502,2,0.365327645086008900854324110696 449,449_0,COMPLETED,BoTorch,0.292873664500495678453262371477,760,0.686277079930088462766946122429,3,0.768421117980781831491299271875 450,450_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.326292015389153422511014923657,2,0.244667543887781002709402855544 451,451_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.352134948343999720776764661423,2,0.200000026538165620593190396903 452,452_0,COMPLETED,BoTorch,0.291386716598744377471064126439,875,0.398656705080247975025997675402,2,0.595227643842916709360224558623 453,453_0,COMPLETED,BoTorch,0.295847560303998280417658861552,755,0.815226329266540927775963609747,2,0.267567233122452008409197787842 454,454_0,COMPLETED,BoTorch,0.290835995153651327704835694021,880,0.486687031503313272118305121694,2,0.689730807408696078297793974343 455,455_0,COMPLETED,BoTorch,0.296673642471637855067001510179,271,0.583569317878884996630972636922,4,0.475732176803040307522252305716 456,456_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.834799366393449648882096880698,2,0.200000000000000011102230246252 457,457_0,COMPLETED,BoTorch,0.293975107390681777985719236312,877,0.611222125121848924855783025123,3,0.256319443788552647767176040361 458,458_0,COMPLETED,BoTorch,0.282244740610199329466922790743,152,1.000000000000000000000000000000,2,0.414364079632176363077178393723 459,459_0,COMPLETED,BoTorch,0.283731688511950630449121035781,152,0.870118237838723329602430567320,3,0.527610337161072862066646393941 460,460_0,COMPLETED,BoTorch,0.294470756691265544979785317992,763,0.260663989417413977101034561201,2,0.325029009404230739832541985379 461,461_0,COMPLETED,BoTorch,0.299537393986121780464770836261,355,0.912197841685568500125214086438,3,0.393390004324668529633868274686 462,462_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.354636425477899641656165385939,2,0.232785827841728992781966667280 463,463_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.389275850439953163828477045172,3,0.800000000000000044408920985006 464,464_0,COMPLETED,BoTorch,0.286595440026434666869192824379,144,0.648911933567943632894525762822,4,0.200000000000000011102230246252 465,465_0,COMPLETED,BoTorch,0.292267870910893234892569125805,154,0.102175338991044917236195033183,4,0.509280790888575030095353213255 466,466_0,COMPLETED,BoTorch,0.291331644454235094698901775701,428,0.478054919998894489729934775823,2,0.492116795546302221442402924367 467,467_0,COMPLETED,BoTorch,0.299206961119065972809494269313,847,0.222400309239249444059893789927,3,0.738940869085474183464157249546 468,468_0,COMPLETED,BoTorch,0.284557770679590316120766146923,144,0.603902691293907278868857702037,3,0.539495441654894047900370424031 469,469_0,COMPLETED,BoTorch,0.284557770679590316120766146923,144,1.000000000000000000000000000000,2,0.400415356764157182034580273466 470,470_0,COMPLETED,BoTorch,0.297059147483202945494440427865,430,0.244180932004080086805686278240,2,0.644764901671394885518395767576 471,471_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.372937870391715997619996869616,2,0.301586821174566199754707440661 472,472_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.338647377398691773464634025004,2,0.304824100061134306560717277534 473,473_0,COMPLETED,BoTorch,0.302456217645115099657004975597,791,0.227246866468340164013639537188,4,0.630468744598329133843606086884 474,474_0,COMPLETED,BoTorch,0.290560634431104691799419015297,130,0.141726296058199585647940921262,2,0.200000000000000011102230246252 475,475_0,COMPLETED,BoTorch,0.293259169512060768880701289163,746,0.417473060078918933868408203125,2,0.347999875620007559362534266256 476,476_0,COMPLETED,BoTorch,0.289404119396409309494799799722,130,0.132864686979716539738660685543,2,0.619332605360162657959222087811 477,477_0,COMPLETED,BoTorch,0.286925872893490474524469391326,122,0.582041157328252567459969668562,2,0.800000000000000044408920985006 478,478_0,COMPLETED,BoTorch,0.284117193523515831898862415983,151,0.344533576689018294914035323018,4,0.740354627832054967839781056682 479,479_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.358565917263725109087602049840,2,0.266370752993803583397181000691 480,480_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.694569932735671136114774526504,3,0.369072789103007448119342370774 481,481_0,COMPLETED,BoTorch,0.284557770679590316120766146923,149,0.679778908431254103028607005399,2,0.800000000000000044408920985006 482,482_0,COMPLETED,BoTorch,0.290009912986011642033190582879,150,0.486632225422628295063987025060,3,0.800000000000000044408920985006 483,483_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.360021647530475163989649445284,2,0.270318045873222945196800992562 484,484_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.864081114007930528586598484253,3,0.200000000000000011102230246252 485,485_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.515382434748938522695027586451,2,0.682410669743261366626541075675 486,486_0,COMPLETED,BoTorch,0.282354884899218006033549954736,124,0.520851043524026113828995221411,3,0.348124749911709718830366000475 487,487_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.358753799485322266704656613001,2,0.265565293421060055756299789209 488,488_0,COMPLETED,BoTorch,0.288798325806806865934106554050,109,0.226968616613779677892992481247,4,0.467378042585722019985894348792 489,489_0,COMPLETED,BoTorch,0.281804163454124956267321522319,124,0.330911961083825478802111774712,2,0.200000000000000011102230246252 490,490_0,COMPLETED,BoTorch,0.287531666483092807062860174483,129,0.660334551166536498634229701565,3,0.325002795332200389299970311185 491,491_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.719003199490056976728169502167,4,0.610953444260461497883341053239 492,492_0,COMPLETED,BoTorch,0.280151999118845695946333762549,108,0.836967148725337062309392877069,4,0.302424228819288376524099248854 493,493_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.811915702420504370451226350269,2,0.527759982614542710877003628411 494,494_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.447715848485643386212018413062,4,0.800000000000000044408920985006 495,495_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.480813168011024361092609069601,4,0.409117532515098303314005079301 496,496_0,COMPLETED,BoTorch,0.281914307743143521811646223796,124,0.291095678639053889735777147507,4,0.200000000000000011102230246252 497,497_0,COMPLETED,BoTorch,0.298105518228879873277037404478,304,0.991188185111919195513507929718,3,0.420839485816412506302697238425 498,498_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.329057396417723690973389238934,2,0.800000000000000044408920985006 499,499_0,COMPLETED,BoTorch,0.280867936997466705051351709699,124,0.277815038770226063746804356924,3,0.630641617478971627797079690936 500,500_0,COMPLETED,BoTorch,0.291111355876197852587949910230,127,0.676593951933761816874834948976,3,0.536892264215374837021954590455 501,501_0,RUNNING,BoTorch,,882,0.346373691731111654767971685942,3,0.501674788648996283768610737752 502,502_0,RUNNING,BoTorch,,123,0.247200804469123930351415197038,3,0.306361870109804534934028197313 503,503_0,RUNNING,BoTorch,,123,0.161449547600200998820341169449,3,0.324505336929979126825429602832 504,504_0,RUNNING,BoTorch,,338,0.201950986296839107847489458436,4,0.223865624006641977805642795829 505,505_0,RUNNING,BoTorch,,124,0.316593452324789303986563027138,3,0.488648240362981212125959018522 506,506_0,RUNNING,BoTorch,,127,0.638611631674683932757830007176,3,0.800000000000000044408920985006 507,507_0,RUNNING,BoTorch,,882,0.661067298405356273960364887898,3,0.739490478039810383847907360177
start_time,end_time,run_time,program_string,n_samples,const,max_depth,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727287807,1727287817,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 225 const 0.2196177462115884 max_depth 2 threshold 0.7768470929935576,225,0.2196177462115884,2,0.7768470929935576,0.29474611741381207,0,None,i7182,7,0.0009954290120057275 1727287807,1727287818,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 119 const 0.48140394613146786 max_depth 4 threshold 0.41706683821976187,119,0.48140394613146786,4,0.41706683821976187,0.2869258728934905,0,None,i7182,8,0.0006268079605335617 1727287808,1727287821,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 200 const 0.7938493965193629 max_depth 4 threshold 0.5842907099053265,200,0.7938493965193629,4,0.5842907099053265,0.2930388809340235,0,None,i7182,10,0.0009227643768892809 1727287816,1727287826,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 134 const 0.7651264416053891 max_depth 3 threshold 0.701942122541368,134,0.7651264416053891,3,0.701942122541368,0.2909461394426699,0,None,i7182,7,0.0006510020664383772 1727287816,1727287827,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 207 const 0.5075005440041424 max_depth 3 threshold 0.2048763744533062,207,0.5075005440041424,3,0.2048763744533062,0.29000991298601164,0,None,i7182,8,0.0010361247653030122 1727287816,1727287829,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 200 const 0.7463543958030641 max_depth 4 threshold 0.7045424308627846,200,0.7463543958030641,4,0.7045424308627846,0.292763520211477,0,None,i7182,9,0.0009288835040569814 1727287837,1727287847,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.6342232121154666 max_depth 2 threshold 0.6520447529852391,135,0.6342232121154666,2,0.6520447529852391,0.2884128207952418,0,None,i7182,7,0.0006888127925193939 1727287837,1727287848,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 475 const 0.5333359978161752 max_depth 2 threshold 0.44889435619115836,475,0.5333359978161752,2,0.44889435619115836,0.2961229210265448,0,None,i7182,9,0.0020231766772366507 1727287837,1727287850,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 142 const 0.11580099891871215 max_depth 4 threshold 0.7429018732160331,142,0.11580099891871215,4,0.7429018732160331,0.285769357858795,0,None,i7182,10,0.0007745066672261299 1727287837,1727287852,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 574 const 0.7559433408081532 max_depth 2 threshold 0.27016501706093554,574,0.7559433408081532,2,0.27016501706093554,0.29265337592245844,0,None,i7182,12,0.002793993464772182 1727287837,1727287861,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 512 const 0.396652586106211 max_depth 4 threshold 0.5997128803282976,512,0.396652586106211,4,0.5997128803282976,0.29783015750633335,0,None,i7182,21,0.0021607717875122244 1727287837,1727287861,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 967 const 0.668990551866591 max_depth 3 threshold 0.7078362897038462,967,0.668990551866591,3,0.7078362897038462,0.2949664059918493,0,None,i7182,21,0.004399652433576873 1727287856,1727287867,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 204 const 0.41982971346005804 max_depth 3 threshold 0.6375836968421937,204,0.41982971346005804,3,0.6375836968421937,0.29331424165657005,0,None,i7182,8,0.0009374780963061617 1727287807,1727287874,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 721 const 0.2818339396268129 max_depth 3 threshold 0.5484429640695454,721,0.2818339396268129,3,0.5484429640695454,0.2980504460843705,0,None,i7182,63,0.003319348346333699 1727287855,1727287882,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 907 const 0.972348522208631 max_depth 2 threshold 0.3364683572202921,907,0.972348522208631,2,0.3364683572202921,0.29898667254102873,0,None,i7174,10,0.003952956150334715 1727287808,1727287883,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 769 const 0.17955736368894576 max_depth 4 threshold 0.6845675587654114,769,0.17955736368894576,4,0.6845675587654114,0.2966185703271286,0,None,i7182,72,0.003449518869719327 1727287835,1727287883,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 916 const 0.3169266355223954 max_depth 4 threshold 0.7052072605118156,916,0.3169266355223954,4,0.7052072605118156,0.30080405330983584,0,None,i7166,11,0.003751024953800592 1727287855,1727287884,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 220 const 0.559539464954287 max_depth 4 threshold 0.473165405727923,220,0.559539464954287,4,0.473165405727923,0.293479458090098,0,None,i7174,12,0.0010020443854620283 1727287855,1727287895,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 632 const 0.32057551518082616 max_depth 4 threshold 0.32007501479238276,632,0.32057551518082616,4,0.32007501479238276,0.30074898116532656,0,None,i7174,23,0.0024153069091939006 1727287855,1727287930,75,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 718 const 0.6600131052546203 max_depth 3 threshold 0.6723421568050981,718,0.6600131052546203,3,0.6723421568050981,0.2954620552924331,0,None,i7174,58,0.00355465660014619 1727288017,1727288027,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6259557114589693 max_depth 2 threshold 0.31894703817819015,100,0.6259557114589693,2,0.31894703817819015,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288017,1727288027,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3849498109794157 max_depth 3 threshold 0.34685224951590043,100,0.3849498109794157,3,0.34685224951590043,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288017,1727288028,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.16609681222969586 max_depth 4 threshold 0.8,100,0.16609681222969586,4,0.8,0.2853287807027206,0,None,i7182,8,0.0005446023179253702 1727288017,1727288028,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.27048686275770883 max_depth 4 threshold 0.6585308524182512,100,0.27048686275770883,4,0.6585308524182512,0.2853287807027206,0,None,i7182,8,0.0005446023179253702 1727288017,1727288028,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.17881747745816418 max_depth 4 threshold 0.6123185825970645,100,0.17881747745816418,4,0.6123185825970645,0.2848882035466461,0,None,i7182,8,0.0005494976196595311 1727288017,1727288029,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.6937233664583143,100,0.1,3,0.6937233664583143,0.2861548628703602,0,None,i7182,9,0.0005354236271738193 1727288017,1727288029,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.1 max_depth 4 threshold 0.5701401238739063,122,0.1,4,0.5701401238739063,0.2858244300033044,0,None,i7182,9,0.0006586330796045512 1727288018,1727288031,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.1 max_depth 4 threshold 0.8,143,0.1,4,0.8,0.29006498513052104,0,None,i7182,10,0.0007063221073574624 1727288037,1727288047,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7910204129585225 max_depth 3 threshold 0.2,100,0.7910204129585225,3,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288037,1727288047,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5796747715986722 max_depth 3 threshold 0.2,100,0.5796747715986722,3,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288037,1727288047,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2856057563101008 max_depth 3 threshold 0.5433103634684704,100,0.2856057563101008,3,0.5433103634684704,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288037,1727288048,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.18309847985390768 max_depth 4 threshold 0.5102937116039626,100,0.18309847985390768,4,0.5102937116039626,0.2848882035466461,0,None,i7182,8,0.0005494976196595311 1727288037,1727288048,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 148 const 0.1 max_depth 4 threshold 0.6306139124755847,148,0.1,4,0.6306139124755847,0.2876418107721115,0,None,i7182,8,0.0007692043790480276 1727288037,1727288051,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.6844442190833013,100,0.1,4,0.6844442190833013,0.28846789293975106,0,None,i7182,11,0.0005097232930694763 1727288048,1727288058,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3540097253583977 max_depth 4 threshold 0.2,100,0.3540097253583977,4,0.2,0.2848882035466461,0,None,i7182,8,0.0005494976196595311 1727288048,1727288058,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.20966928037585617 max_depth 4 threshold 0.3366190986130696,100,0.20966928037585617,4,0.3366190986130696,0.2849983478356647,0,None,i7182,8,0.0005482737942259915 1727288056,1727288066,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6621376091768707 max_depth 3 threshold 0.2469877286618245,100,0.6621376091768707,3,0.2469877286618245,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288056,1727288066,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2333359511356883 max_depth 3 threshold 0.30417765288351273,100,0.2333359511356883,3,0.30417765288351273,0.2848882035466461,0,None,i7182,7,0.0005494976196595311 1727288065,1727288079,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8600913728335876 max_depth 2 threshold 0.4112384722397693,100,0.8600913728335876,2,0.4112384722397693,0.28521863641370193,0,None,i7177,7,0.000545826143358911 1727288065,1727288084,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.48038400783360713,100,0.1,4,0.48038400783360713,0.2874765943385835,0,None,i7177,12,0.0005207377219713378 1727288108,1727288118,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4228414350410764 max_depth 2 threshold 0.2,100,0.4228414350410764,2,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288117,1727288126,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9097103621962321 max_depth 2 threshold 0.2,100,0.9097103621962321,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288117,1727288127,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5416179836545 max_depth 3 threshold 0.4097979743488269,100,0.5416179836545,3,0.4097979743488269,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288117,1727288128,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.2,100,0.1,2,0.2,0.28698094503799976,0,None,i7182,8,0.0005262449364222685 1727288137,1727288147,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.32434741888946517,100,1,2,0.32434741888946517,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288137,1727288147,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.26976089380888735 max_depth 2 threshold 0.3465489749693449,100,0.26976089380888735,2,0.3465489749693449,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288137,1727288147,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5218717326800723 max_depth 2 threshold 0.2,100,0.5218717326800723,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288137,1727288147,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.427933794673768 max_depth 2 threshold 0.4388164869383877,100,0.427933794673768,2,0.4388164869383877,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288137,1727288147,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.639194822828227 max_depth 2 threshold 0.2,100,0.639194822828227,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288137,1727288152,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.2,100,0.1,4,0.2,0.2869258728934905,0,None,i7182,12,0.0005268568491390383 1727288157,1727288168,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.42757360608128125 max_depth 4 threshold 0.2,100,0.42757360608128125,4,0.2,0.2854389249917392,0,None,i7182,8,0.0005433784924918306 1727288158,1727288169,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2910150475259643 max_depth 4 threshold 0.35882273114548086,100,0.2910150475259643,4,0.35882273114548086,0.2854389249917392,0,None,i7182,8,0.0005433784924918306 1727288157,1727288170,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.3883744432910038,100,0.1,3,0.3883744432910038,0.28742152219407424,0,None,i7182,10,0.0005213496346881076 1727288157,1727288170,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.2,100,0.1,3,0.2,0.2848882035466461,0,None,i7182,10,0.0005494976196595311 1727288228,1727288238,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2,100,1,4,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288237,1727288247,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.2,100,1,3,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288237,1727288247,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3581732824864572 max_depth 3 threshold 0.2,100,0.3581732824864572,3,0.2,0.2854389249917392,0,None,i7182,7,0.0005433784924918306 1727288257,1727288267,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2844098461485733 max_depth 2 threshold 0.6803374604910857,100,0.2844098461485733,2,0.6803374604910857,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288257,1727288268,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 123 const 0.8123385617189447 max_depth 2 threshold 0.2,123,0.8123385617189447,2,0.2,0.2889635422403348,0,None,i7182,7,0.0006216362613105356 1727288257,1727288268,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 142 const 1 max_depth 2 threshold 0.2,142,1,2,0.2,0.2849432756911554,0,None,i7182,7,0.0007876190825854883 1727288257,1727288269,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 131 const 1 max_depth 3 threshold 0.2,131,1,3,0.2,0.2894041193964093,0,None,i7182,8,0.0006544805579366936 1727288258,1727288269,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3201642264331035 max_depth 4 threshold 0.4239089908639081,100,0.3201642264331035,4,0.4239089908639081,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288257,1727288269,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.19551801358830218 max_depth 3 threshold 0.2,136,0.19551801358830218,3,0.2,0.28037228769688294,0,None,i7182,8,0.000821075609047852 1727288277,1727288287,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.9126900570160437 max_depth 2 threshold 0.2,141,0.9126900570160437,2,0.2,0.2852737085582112,0,None,i7182,7,0.000770149520872343 1727288277,1727288287,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8285938504427315 max_depth 2 threshold 0.2,100,0.8285938504427315,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288277,1727288288,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.2,108,1,3,0.2,0.2790505562286596,0,None,i7182,7,0.0006608657341116854 1727288277,1727288288,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.38255228046146694,100,1,4,0.38255228046146694,0.2853287807027206,0,None,i7182,8,0.0005446023179253702 1727288278,1727288289,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3979553101648916 max_depth 4 threshold 0.8,100,0.3979553101648916,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288288,1727288298,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 133 const 0.9930960254681568 max_depth 2 threshold 0.2,133,0.9930960254681568,2,0.2,0.28439255424606236,0,None,i7182,7,0.0007378047595291001 1727288397,1727288407,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.3563669746011441 max_depth 3 threshold 0.2,144,0.3563669746011441,3,0.2,0.28620993501486947,0,None,i7182,7,0.0007675133790344715 1727288397,1727288407,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.17989747666729544 max_depth 2 threshold 0.2,141,0.17989747666729544,2,0.2,0.2843374821015531,0,None,i7182,7,0.0007847780592576264 1727288397,1727288410,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 138 const 0.1 max_depth 4 threshold 0.2,138,0.1,4,0.2,0.28923890296288135,0,None,i7182,10,0.0006972980758639972 1727288408,1727288418,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 150 const 0.41076905235291383 max_depth 2 threshold 0.2,150,0.41076905235291383,2,0.2,0.29000991298601164,0,None,i7182,7,0.0007425560818004921 1727288408,1727288420,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 142 const 0.1 max_depth 3 threshold 0.2,142,0.1,3,0.2,0.2868157286044719,0,None,i7182,8,0.0007578976077709406 1727288409,1727288420,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 134 const 0.1 max_depth 2 threshold 0.2,134,0.1,2,0.2,0.2876418107721115,0,None,i7182,8,0.0007003204048049206 1727288409,1727288422,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.1 max_depth 3 threshold 0.2,132,0.1,3,0.2,0.2904504901420861,0,None,i7182,10,0.0006487174669405153 1727288417,1727288429,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.2977714366639774 max_depth 4 threshold 0.2,141,0.2977714366639774,4,0.2,0.2852737085582112,0,None,i7182,9,0.000770149520872343 1727288437,1727288447,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 138 const 0.11078382987925878 max_depth 2 threshold 0.2,138,0.11078382987925878,2,0.2,0.2893490472519,0,None,i7182,7,0.0006956035483406329 1727288437,1727288448,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.35226839391414666 max_depth 2 threshold 0.2,141,0.35226839391414666,2,0.2,0.2835664720784228,0,None,i7182,7,0.0007968250908690373 1727288437,1727288448,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 138 const 0.34464156571497573 max_depth 3 threshold 0.2,138,0.34464156571497573,3,0.2,0.2891287586738628,0,None,i7182,7,0.0006989926033873598 1727288437,1727288448,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.1 max_depth 2 threshold 0.2,144,0.1,2,0.2,0.28885339795131626,0,None,i7182,8,0.0007372561281084663 1727288438,1727288451,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.1 max_depth 3 threshold 0.2935795714761675,135,0.1,3,0.2935795714761675,0.28296067848882034,0,None,i7182,10,0.0007701880508241914 1727288457,1727288468,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.10478280698431139 max_depth 3 threshold 0.2,128,0.10478280698431139,3,0.2,0.288633109373279,0,None,i7182,8,0.0006561452645823165 1727288517,1727288527,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288528,1727288538,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.3983831981564414,100,1,3,0.3983831981564414,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288528,1727288539,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7653575554479924 max_depth 4 threshold 0.2,100,0.7653575554479924,4,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288537,1727288547,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.5649679919945447,100,1,2,0.5649679919945447,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288537,1727288547,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6913772203922036 max_depth 4 threshold 0.3855644219448491,100,0.6913772203922036,4,0.3855644219448491,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288557,1727288567,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.7393879221789852,100,1,2,0.7393879221789852,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288557,1727288567,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.31393968301298436,100,1,3,0.31393968301298436,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288557,1727288567,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3491977127263255 max_depth 3 threshold 0.7449255372959824,100,0.3491977127263255,3,0.7449255372959824,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288558,1727288569,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5205563457561323 max_depth 4 threshold 0.625751177418093,100,0.5205563457561323,4,0.625751177418093,0.2853287807027206,0,None,i7182,8,0.0005446023179253702 1727288559,1727288569,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8992421564808647 max_depth 4 threshold 0.2,100,0.8992421564808647,4,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288577,1727288587,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.8,100,1,3,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288577,1727288587,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.8,100,0.1,2,0.8,0.2854389249917392,0,None,i7182,7,0.0005433784924918306 1727288577,1727288587,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.28106017935933847 max_depth 3 threshold 0.2,100,0.28106017935933847,3,0.2,0.2848882035466461,0,None,i7182,7,0.0005494976196595311 1727288589,1727288599,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3678142667551759 max_depth 4 threshold 0.602559771548226,100,0.3678142667551759,4,0.602559771548226,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288677,1727288687,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.39050866129632333,100,1,2,0.39050866129632333,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288677,1727288688,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.5169990507971318,100,1,4,0.5169990507971318,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288677,1727288688,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.7852605006016316,100,1,4,0.7852605006016316,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288679,1727288689,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5802526122037643 max_depth 2 threshold 0.8,100,0.5802526122037643,2,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288697,1727288707,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.46526273406987473 max_depth 2 threshold 0.6221195482102904,100,0.46526273406987473,2,0.6221195482102904,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288697,1727288707,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4322477159421133 max_depth 3 threshold 0.5192620959591123,100,0.4322477159421133,3,0.5192620959591123,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288697,1727288707,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288709,1727288719,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3171611124710092 max_depth 2 threshold 0.5790812377614545,100,0.3171611124710092,2,0.5790812377614545,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288709,1727288719,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3075022946934204 max_depth 3 threshold 0.41779889848189244,100,0.3075022946934204,3,0.41779889848189244,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288710,1727288720,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.12542508709652136 max_depth 2 threshold 0.8,100,0.12542508709652136,2,0.8,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288717,1727288727,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6534962420172646 max_depth 4 threshold 0.2,100,0.6534962420172646,4,0.2,0.2854389249917392,0,None,i7182,7,0.0005433784924918306 1727288737,1727288747,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8570988549777145 max_depth 3 threshold 0.3729923942910942,100,0.8570988549777145,3,0.3729923942910942,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288737,1727288747,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.6234844639041588,100,1,3,0.6234844639041588,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288737,1727288747,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288737,1727288749,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 287 const 1 max_depth 2 threshold 0.2,287,1,2,0.2,0.2965084260381099,0,None,i7182,9,0.0012275758663203636 1727288757,1727288767,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7887464897642442 max_depth 4 threshold 0.8,100,0.7887464897642442,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288877,1727288887,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.8,100,1,2,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288877,1727288887,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3468519531123899 max_depth 2 threshold 0.8,100,0.3468519531123899,2,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288877,1727288887,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6467747749321522 max_depth 2 threshold 0.48340230410122825,100,0.6467747749321522,2,0.48340230410122825,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288889,1727288899,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.23338395288217048 max_depth 2 threshold 0.5315674354859341,100,0.23338395288217048,2,0.5315674354859341,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727288917,1727288927,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.34364304298520143 max_depth 3 threshold 0.8,100,0.34364304298520143,3,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288917,1727288927,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8140398029875168 max_depth 4 threshold 0.5817425376531082,100,0.8140398029875168,4,0.5817425376531082,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288897,1727288936,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 336 const 0.8280819910702771 max_depth 2 threshold 0.2,336,0.8280819910702771,2,0.2,0.29111135587619785,0,None,i7182,36,0.0018104967507434715 1727288889,1727288942,53,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 343 const 1 max_depth 2 threshold 0.2,343,1,2,0.2,0.28742152219407424,0,None,i7182,50,0.002049641552172475 1727288937,1727288947,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7592755145279121 max_depth 2 threshold 0.8,100,0.7592755145279121,2,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288937,1727288947,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6191129903344432 max_depth 4 threshold 0.8,100,0.6191129903344432,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288937,1727288952,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 295 const 1 max_depth 2 threshold 0.2470247820339004,295,1,2,0.2470247820339004,0.2894591915409186,0,None,i7182,12,0.0014961265925028445 1727288938,1727288953,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.50817810369892 max_depth 4 threshold 0.4348348053748925,100,0.50817810369892,4,0.4348348053748925,0.2853287807027206,0,None,i7182,11,0.0005446023179253702 1727288949,1727288959,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7670620416877565 max_depth 4 threshold 0.8,100,0.7670620416877565,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727288917,1727288968,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 380 const 0.4740338508600195 max_depth 2 threshold 0.2,380,0.4740338508600195,2,0.2,0.3002533318647428,0,None,i7182,47,0.0016338069537761132 1727288917,1727288986,69,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 356 const 0.8312294603670083 max_depth 2 threshold 0.20430067034265065,356,0.8312294603670083,2,0.20430067034265065,0.29259830377794915,0,None,i7182,66,0.001998332100766286 1727289058,1727289073,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.9790667388598886 max_depth 3 threshold 0.22616240938052737,107,0.9790667388598886,3,0.22616240938052737,0.2834563277894041,0,None,i7182,10,0.0006057935896023788 1727289069,1727289079,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 2 threshold 0.5764830436680239,108,1,2,0.5764830436680239,0.28026214340786426,0,None,i7182,7,0.0006464420772163917 1727289069,1727289079,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 109 const 1 max_depth 3 threshold 0.2,109,1,3,0.2,0.29006498513052104,0,None,i7182,7,0.000533469929945577 1727289078,1727289093,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 4 threshold 0.2,108,1,4,0.2,0.2800969269743364,0,None,i7182,11,0.0006484089395202947 1727289097,1727289107,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 2 threshold 0.2,108,1,2,0.2,0.2790505562286596,0,None,i7182,7,0.0006608657341116854 1727289097,1727289107,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 4 threshold 0.5862489255481145,108,1,4,0.5862489255481145,0.28026214340786426,0,None,i7182,7,0.0006464420772163917 1727289097,1727289108,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.8194302961443588 max_depth 3 threshold 0.5268012489546695,107,0.8194302961443588,3,0.5268012489546695,0.2834563277894041,0,None,i7182,7,0.0006057935896023788 1727289098,1727289112,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.827926161482887 max_depth 3 threshold 0.2,108,0.827926161482887,3,0.2,0.2789404119396409,0,None,i7182,10,0.0006621769756476219 1727289117,1727289127,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 107 const 1 max_depth 4 threshold 0.7998776191341603,107,1,4,0.7998776191341603,0.2819143077431435,0,None,i7182,7,0.0006241509711054809 1727289117,1727289128,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 109 const 1 max_depth 4 threshold 0.2,109,1,4,0.2,0.28923890296288135,0,None,i7182,8,0.0005434227271460551 1727289119,1727289132,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 109 const 0.853667023177145 max_depth 3 threshold 0.3781821573211903,109,0.853667023177145,3,0.3781821573211903,0.28962440797444655,0,None,i7182,9,0.0005387780884524984 1727289130,1727289139,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.9999687262256026 max_depth 2 threshold 0.2,107,0.9999687262256026,2,0.2,0.28400704923449716,0,None,i7182,7,0.0005992373819226996 1727289129,1727289140,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 107 const 1 max_depth 4 threshold 0.2,107,1,4,0.2,0.2838969049454786,0,None,i7182,7,0.000600548623458635 1727289318,1727289340,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 431 const 1 max_depth 2 threshold 0.8,431,1,2,0.8,0.29226787091089323,0,None,i7182,18,0.0021147703491573968 1727289338,1727289348,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.5108648583318908,100,1,3,0.5108648583318908,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289338,1727289349,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.5699907455180463,100,1,4,0.5699907455180463,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289358,1727289368,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.6091791553579595,100,1,4,0.6091791553579595,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289358,1727289369,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 470 const 0.8769270575349629 max_depth 2 threshold 0.8,470,0.8769270575349629,2,0.8,0.2898446965524838,0,None,i7182,8,0.0023536095442924935 1727289370,1727289380,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.32705247770576595,108,1,3,0.32705247770576595,0.2790505562286596,0,None,i7182,7,0.0006608657341116854 1727289377,1727289389,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 530 const 1 max_depth 2 threshold 0.8,530,1,2,0.8,0.2953519110034145,0,None,i7182,8,0.0023065509935662736 1727289359,1727289397,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 457 const 1 max_depth 3 threshold 0.8,457,1,3,0.8,0.294360612402247,0,None,i7182,34,0.00211592976272601 1727289398,1727289412,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 460 const 1 max_depth 2 threshold 0.8,460,1,2,0.8,0.2932040973675515,0,None,i7182,12,0.002176798975078404 1727289417,1727289427,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3516944967994061 max_depth 2 threshold 0.2,100,0.3516944967994061,2,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727289418,1727289436,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 486 const 0.7944640148108411 max_depth 3 threshold 0.8,486,0.7944640148108411,3,0.8,0.30041854829827075,0,None,i7182,14,0.0018969294219872454 1727289398,1727289445,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 437 const 0.8915008591749046 max_depth 3 threshold 0.5993801054248329,437,0.8915008591749046,3,0.5993801054248329,0.2957374160149796,0,None,i7182,44,0.0020434664146874505 1727289417,1727289447,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 456 const 0.9568951777243363 max_depth 3 threshold 0.703049755182722,456,0.9568951777243363,3,0.703049755182722,0.2939751073906818,0,None,i7182,26,0.00213621950017681 1727289398,1727289457,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 408 const 0.9568824634915307 max_depth 3 threshold 0.7399672633437346,408,0.9568824634915307,3,0.7399672633437346,0.299757682564159,0,None,i7182,57,0.0017402797664941073 1727289338,1727289484,146,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 405 const 1 max_depth 4 threshold 0.8,405,1,4,0.8,0.29711421962771234,0,None,i7182,142,0.0018724529133164413 1727289370,1727289488,118,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 418 const 0.8664258514120285 max_depth 4 threshold 0.46137564232941064,418,0.8664258514120285,4,0.46137564232941064,0.2938649631016632,0,None,i7182,115,0.002034915739618898 1727289521,1727289534,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3322491483275727 max_depth 2 threshold 0.8,100,0.3322491483275727,2,0.8,0.28521863641370193,0,None,i7182,10,0.000545826143358911 1727289538,1727289548,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.4679685429946023,100,0.1,2,0.4679685429946023,0.2871461614715277,0,None,i7182,8,0.0005250211109887276 1727289551,1727289564,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4632229140273145 max_depth 2 threshold 0.8,100,0.4632229140273145,2,0.8,0.28521863641370193,0,None,i7182,10,0.000545826143358911 1727289558,1727289568,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2430291452102752 max_depth 2 threshold 0.4579286194957155,100,0.2430291452102752,2,0.4579286194957155,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727289558,1727289568,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 122 const 1 max_depth 3 threshold 0.6309805009798213,122,1,3,0.6309805009798213,0.28885339795131626,0,None,i7182,7,0.0006177010803070933 1727289578,1727289588,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2858259885071094,100,1,2,0.2858259885071094,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289581,1727289592,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 126 const 1 max_depth 4 threshold 0.44176367227462265,126,1,4,0.44176367227462265,0.2897345522634651,0,None,i7182,8,0.0006226211893135562 1727289579,1727289593,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.4895194235699138,100,1,4,0.4895194235699138,0.28521863641370193,0,None,i7182,11,0.000545826143358911 1727289599,1727289612,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.4783605087810162,100,1,2,0.4783605087810162,0.28521863641370193,0,None,i7182,9,0.000545826143358911 1727289598,1727289670,72,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 320 const 1 max_depth 4 threshold 0.8,320,1,4,0.8,0.2966185703271286,0,None,i7182,69,0.0014053595395152813 1727289719,1727289733,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3217297563571467 max_depth 3 threshold 0.6837559087003691,100,0.3217297563571467,3,0.6837559087003691,0.28521863641370193,0,None,i7182,10,0.000545826143358911 1727289730,1727289741,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289739,1727289752,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.38653734746954094 max_depth 3 threshold 0.45223736145268134,100,0.38653734746954094,3,0.45223736145268134,0.28521863641370193,0,None,i7182,9,0.000545826143358911 1727289758,1727289768,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7491314535769318 max_depth 3 threshold 0.8,100,0.7491314535769318,3,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289758,1727289769,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 3 threshold 0.6647598152169027,100,0.1,3,0.6647598152169027,0.2861548628703602,0,None,i7182,8,0.0005354236271738193 1727289759,1727289773,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.44285107664487966 max_depth 4 threshold 0.8,100,0.44285107664487966,4,0.8,0.28521863641370193,0,None,i7182,10,0.000545826143358911 1727289778,1727289787,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 1000 const 0.1 max_depth 2 threshold 0.8,1000,0.1,2,0.8,0.3031170833792268,0,None,i7182,6,0.0034940216127571488 1727289778,1727289788,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 146 const 0.9133040639659837 max_depth 4 threshold 0.2,146,0.9133040639659837,4,0.2,0.2883577486507325,0,None,i7182,7,0.0007452504716662689 1727289779,1727289793,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.8,100,1,4,0.8,0.28521863641370193,0,None,i7182,11,0.000545826143358911 1727289799,1727289814,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 973 const 0.305107133977624 max_depth 2 threshold 0.4325549449181376,973,0.305107133977624,2,0.4325549449181376,0.29711421962771234,0,None,i7182,12,0.004161006474036537 1727289808,1727289824,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.6806296255188795,100,1,2,0.6806296255188795,0.28521863641370193,0,None,i7007,7,0.000545826143358911 1727289808,1727289824,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8190785869960641 max_depth 3 threshold 0.6929911403527949,100,0.8190785869960641,3,0.6929911403527949,0.28521863641370193,0,None,i7007,7,0.000545826143358911 1727289818,1727289828,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6726705102539957 max_depth 3 threshold 0.8,100,0.6726705102539957,3,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727289818,1727289828,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6385512363454456 max_depth 4 threshold 0.2,100,0.6385512363454456,4,0.2,0.2854389249917392,0,None,i7182,7,0.0005433784924918306 1727289819,1727289833,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5344442868964837 max_depth 3 threshold 0.6569343450724494,100,0.5344442868964837,3,0.6569343450724494,0.28521863641370193,0,None,i7182,10,0.000545826143358911 1727289538,1727289848,310,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 388 const 1 max_depth 4 threshold 0.2,388,1,4,0.2,0.29551712743694236,0,None,i7182,307,0.0021569923266145314 1727289958,1727289971,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5104759900234929 max_depth 3 threshold 0.8,100,0.5104759900234929,3,0.8,0.28521863641370193,0,None,i7183,8,0.000545826143358911 1727289958,1727289972,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7954018244107431 max_depth 4 threshold 0.5343247898194742,100,0.7954018244107431,4,0.5343247898194742,0.28521863641370193,0,None,i7183,9,0.000545826143358911 1727289958,1727289972,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.586083897691066 max_depth 4 threshold 0.2,100,0.586083897691066,4,0.2,0.2854389249917392,0,None,i7183,9,0.0005433784924918306 1727289956,1727289975,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.2883577486507325,0,None,i7183,12,0.0005109471185030159 1727289971,1727289983,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.33977808782687924 max_depth 2 threshold 0.7312066011918279,100,0.33977808782687924,2,0.7312066011918279,0.28521863641370193,0,None,i7183,8,0.000545826143358911 1727289971,1727289988,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.3848687616077546,100,0.1,4,0.3848687616077546,0.2874765943385835,0,None,i7183,13,0.0005207377219713378 1727289978,1727289989,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.41199004771455827 max_depth 3 threshold 0.2,100,0.41199004771455827,3,0.2,0.2853287807027206,0,None,i7183,7,0.0005446023179253702 1727289598,1727289991,393,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 357 const 0.8753859127980977 max_depth 4 threshold 0.22660151921631977,357,0.8753859127980977,4,0.22660151921631977,0.2923780151999119,0,None,i7182,390,0.002343625705229403 1727289998,1727290009,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.49212188199462603 max_depth 2 threshold 0.8,100,0.49212188199462603,2,0.8,0.28521863641370193,0,None,i7183,7,0.000545826143358911 1727289998,1727290009,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4942447450142501 max_depth 4 threshold 0.8,100,0.4942447450142501,4,0.8,0.28521863641370193,0,None,i7183,8,0.000545826143358911 1727290018,1727290029,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5391769929108496 max_depth 3 threshold 0.5705620786823598,100,0.5391769929108496,3,0.5705620786823598,0.28521863641370193,0,None,i7183,8,0.000545826143358911 1727289956,1727290071,115,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 306 const 0.7154677247962953 max_depth 4 threshold 0.2160682899817106,306,0.7154677247962953,4,0.2160682899817106,0.2902302015640489,0,None,i7183,108,0.001641965789999714 1727290138,1727290148,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.41554891656766435 max_depth 3 threshold 0.6295188651487361,100,0.41554891656766435,3,0.6295188651487361,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727290138,1727290148,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.3751817577460884 max_depth 3 threshold 0.5773944121812057,100,0.3751817577460884,3,0.5773944121812057,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727290151,1727290161,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6046161812131585 max_depth 2 threshold 0.8,100,0.6046161812131585,2,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727290152,1727290165,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.2883577486507325,0,None,i7182,10,0.0005109471185030159 1727290178,1727290188,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5884905296236265 max_depth 2 threshold 0.2,100,0.5884905296236265,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727290178,1727290188,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 2 threshold 0.7372021120891121,100,0.1,2,0.7372021120891121,0.2854389249917392,0,None,i7182,7,0.0005433784924918306 1727290178,1727290188,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5372206513004862 max_depth 2 threshold 0.4745324498379796,100,0.5372206513004862,2,0.4745324498379796,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727290198,1727290208,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.48752341439723157 max_depth 3 threshold 0.2,100,0.48752341439723157,3,0.2,0.2853287807027206,0,None,i7182,7,0.0005446023179253702 1727290198,1727290208,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1407463357963673 max_depth 3 threshold 0.5608681137379988,100,0.1407463357963673,3,0.5608681137379988,0.28560414142526713,0,None,i7182,7,0.0005415427543415199 1727290218,1727290228,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2922918470432834 max_depth 4 threshold 0.2,100,0.2922918470432834,4,0.2,0.2848882035466461,0,None,i7182,7,0.0005494976196595311 1727290229,1727290243,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4261187783891224 max_depth 4 threshold 0.6924549357906264,100,0.4261187783891224,4,0.6924549357906264,0.28521863641370193,0,None,i7132,7,0.000545826143358911 1727290378,1727290388,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.17914655881318364 max_depth 2 threshold 0.5147410805846478,136,0.17914655881318364,2,0.5147410805846478,0.2838969049454786,0,None,i7182,7,0.0007676723174024633 1727290229,1727290393,164,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 1000 const 1 max_depth 4 threshold 0.2,1000,1,4,0.2,0.3032822998127547,0,None,i7132,157,0.0039101222601608115 1727290391,1727290402,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.23387920986591004 max_depth 2 threshold 0.2,136,0.23387920986591004,2,0.2,0.2845577706795903,0,None,i7182,7,0.0007576592002189522 1727290391,1727290404,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.24386168637542852 max_depth 4 threshold 0.414171844315302,136,0.24386168637542852,4,0.414171844315302,0.28411719352351583,0,None,i7182,10,0.0007643346116746263 1727290398,1727290408,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.2713368733431972,108,1,3,0.2713368733431972,0.2790505562286596,0,None,i7182,7,0.0006608657341116854 1727290418,1727290428,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 142 const 0.5879829814741564 max_depth 2 threshold 0.4850590962435423,142,0.5879829814741564,2,0.4850590962435423,0.2846679149686089,0,None,i7182,7,0.0007919898877052744 1727290418,1727290429,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.1 max_depth 2 threshold 0.2,136,0.1,2,0.2,0.28147373058706904,0,None,i7182,8,0.0008043870804086686 1727290438,1727290449,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.1 max_depth 3 threshold 0.5442277887233959,136,0.1,3,0.5442277887233959,0.2837867606564599,0,None,i7182,8,0.0007693411702663826 1727290438,1727290451,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.3044836934044478 max_depth 4 threshold 0.2,136,0.3044836934044478,4,0.2,0.2819693798876528,0,None,i7182,10,0.0007968772425210358 1727290451,1727290461,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.2478891204017245 max_depth 2 threshold 0.47832525910219437,136,0.2478891204017245,2,0.47832525910219437,0.28554906928075774,0,None,i7182,7,0.0007426395244436883 1727290451,1727290462,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.29592800025078625 max_depth 2 threshold 0.2,136,0.29592800025078625,2,0.2,0.28411719352351583,0,None,i7182,7,0.0007643346116746263 1727290458,1727290469,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 142 const 0.7101467693148971 max_depth 4 threshold 0.2,142,0.7101467693148971,4,0.2,0.285769357858795,0,None,i7182,8,0.0007745066672261299 1727290478,1727290493,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.1 max_depth 4 threshold 0.2629837816828921,136,0.1,4,0.2629837816828921,0.2837867606564599,0,None,i7182,11,0.0007693411702663826 1727290478,1727290493,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.1 max_depth 4 threshold 0.2,136,0.1,4,0.2,0.2834563277894041,0,None,i7182,12,0.0007743477288581372 1727290676,1727290692,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.6930334894662761 max_depth 2 threshold 0.2,136,0.6930334894662761,2,0.2,0.2839519770899879,0,None,i7184,7,0.0007668378909705044 1727290679,1727290693,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.7555281203607576 max_depth 2 threshold 0.2,144,0.7555281203607576,2,0.2,0.2837867606564599,0,None,i7184,7,0.0008059764640885914 1727290676,1727290693,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.3353912380460713 max_depth 3 threshold 0.8,141,0.3353912380460713,3,0.8,0.2852737085582112,0,None,i7184,8,0.000770149520872343 1727290679,1727290693,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.4349872411361393 max_depth 4 threshold 0.4112295337259513,121,0.4349872411361393,4,0.4112295337259513,0.2819143077431435,0,None,i7184,8,0.0006990490876381387 1727290676,1727290694,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.5777991239005836 max_depth 3 threshold 0.2,136,0.5777991239005836,3,0.2,0.2859896464368322,0,None,i7184,9,0.0007359641129880143 1727290692,1727290702,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.8010531490929405 max_depth 2 threshold 0.2,137,0.8010531490929405,2,0.2,0.2878620993501487,0,None,i7184,7,0.0007075936143014006 1727290692,1727290704,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 0.5290060305737442 max_depth 3 threshold 0.2,136,0.5290060305737442,3,0.2,0.2845577706795903,0,None,i7184,9,0.0007576592002189522 1727290699,1727290709,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 135 const 0.9560882725590629 max_depth 2 threshold 0.2,135,0.9560882725590629,2,0.2,0.28521863641370193,0,None,i7184,7,0.0007364871862737199 1727290718,1727290728,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.5898657228772997 max_depth 3 threshold 0.8,141,0.5898657228772997,3,0.8,0.2852737085582112,0,None,i7184,7,0.000770149520872343 1727290738,1727290748,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.8147099826950902 max_depth 3 threshold 0.6627629208862896,144,0.8147099826950902,3,0.6627629208862896,0.2845577706795903,0,None,i7184,7,0.0007937382097531881 1727290738,1727290748,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 137 const 0.9019496906557593 max_depth 4 threshold 0.6261233622686571,137,0.9019496906557593,4,0.6261233622686571,0.2878620993501487,0,None,i7184,7,0.0007075936143014006 1727290752,1727290762,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 0.39036710102550665 max_depth 2 threshold 0.8,141,0.39036710102550665,2,0.8,0.2852737085582112,0,None,i7183,7,0.000770149520872343 1727290752,1727290763,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.8094674633888989 max_depth 2 threshold 0.5676208449660294,143,0.8094674633888989,2,0.5676208449660294,0.28775195506113005,0,None,i7183,7,0.0007430368703636685 1727290759,1727290768,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 143 const 0.8238174373077 max_depth 2 threshold 0.5268105163692891,143,0.8238174373077,2,0.5268105163692891,0.28775195506113005,0,None,i7184,6,0.0007430368703636685 1727290919,1727290929,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.37399728875276983 max_depth 2 threshold 0.6916755491478581,121,0.37399728875276983,2,0.6916755491478581,0.2819143077431435,0,None,i7184,7,0.0006990490876381387 1727290932,1727290943,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.478995137977272 max_depth 4 threshold 0.8,122,0.478995137977272,4,0.8,0.28885339795131626,0,None,i7184,8,0.0006177010803070933 1727290979,1727290992,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.3003184774745373 max_depth 3 threshold 0.2,121,0.3003184774745373,3,0.2,0.2819143077431435,0,None,i7186,7,0.0006990490876381387 1727290979,1727290992,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.6424786508473608 max_depth 4 threshold 0.2844943176581813,121,0.6424786508473608,4,0.2844943176581813,0.2819143077431435,0,None,i7186,7,0.0006990490876381387 1727290977,1727290992,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8476591417390424 max_depth 2 threshold 0.2335980640278588,108,0.8476591417390424,2,0.2335980640278588,0.2790505562286596,0,None,i7186,7,0.0006608657341116854 1727290977,1727290993,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.30172949111121683 max_depth 4 threshold 0.7999998845597509,121,0.30172949111121683,4,0.7999998845597509,0.2819143077431435,0,None,i7186,8,0.0006990490876381387 1727290977,1727290994,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.43029239269950537 max_depth 4 threshold 0.2,122,0.43029239269950537,4,0.2,0.2883577486507325,0,None,i7186,9,0.0006243990438284956 1727290992,1727291003,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.6008556046763697 max_depth 4 threshold 0.7995627206766487,121,0.6008556046763697,4,0.7995627206766487,0.2819143077431435,0,None,i7186,7,0.0006990490876381387 1727290992,1727291003,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.24119216360262946 max_depth 4 threshold 0.4608824658277272,120,0.24119216360262946,4,0.4608824658277272,0.2916070051767816,0,None,i7186,8,0.0005698131218562974 1727290999,1727291010,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.6252017656602965 max_depth 4 threshold 0.5800002968562384,121,0.6252017656602965,4,0.5800002968562384,0.2819143077431435,0,None,i7186,8,0.0006990490876381387 1727291019,1727291030,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.2507264699991865 max_depth 4 threshold 0.2776491629022545,121,0.2507264699991865,4,0.2776491629022545,0.2845026985350809,0,None,i7186,8,0.0006645372104123067 1727291040,1727291050,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 145 const 0.5840131826053117 max_depth 2 threshold 0.6543718705808128,145,0.5840131826053117,2,0.6543718705808128,0.29034034585306756,0,None,i7186,7,0.0007097200558538141 1727291233,1727291244,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.9310563405829336 max_depth 2 threshold 0.2588689046735739,108,0.9310563405829336,2,0.2588689046735739,0.2792708448066967,0,None,i7186,8,0.0006582432510398148 1727291239,1727291251,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 148 const 0.7749449326684477 max_depth 4 threshold 0.6240785425290358,148,0.7749449326684477,4,0.6240785425290358,0.2865403678819253,0,None,i7186,8,0.0007872608198707524 1727291259,1727291270,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.6040803999454931 max_depth 2 threshold 0.3318566934301541,121,0.6040803999454931,2,0.3318566934301541,0.2830157506333296,0,None,i7186,8,0.0006843631824356574 1727291259,1727291271,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.6492052156689888 max_depth 3 threshold 0.49951447596046233,120,0.6492052156689888,3,0.49951447596046233,0.29414032382420974,0,None,i7186,8,0.0005360355398905892 1727291279,1727291290,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 145 const 0.4026464353661169 max_depth 2 threshold 0.6291319222772616,145,0.4026464353661169,2,0.6291319222772616,0.29034034585306756,0,None,i7186,8,0.0007097200558538141 1727291279,1727291293,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.7897318007013477 max_depth 4 threshold 0.47354489712947756,120,0.7897318007013477,4,0.47354489712947756,0.29369974666813525,0,None,i7186,10,0.0005448470830120789 1727291293,1727291303,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.5978722405482201 max_depth 3 threshold 0.5280811200859825,121,0.5978722405482201,3,0.5280811200859825,0.2819143077431435,0,None,i7186,7,0.0006990490876381387 1727291293,1727291303,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.6725765846924285 max_depth 3 threshold 0.6833086270451999,120,0.6725765846924285,3,0.6833086270451999,0.29414032382420974,0,None,i7186,7,0.0005360355398905892 1727291319,1727291330,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 145 const 0.5676772111291343 max_depth 3 threshold 0.48593766257120957,145,0.5676772111291343,3,0.48593766257120957,0.29034034585306756,0,None,i7186,8,0.0007097200558538141 1727291319,1727291331,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.1337405263828153 max_depth 2 threshold 0.509846726668205,121,0.1337405263828153,2,0.509846726668205,0.28698094503799976,0,None,i7186,8,0.0006430044980546137 1727291339,1727291351,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.6836600082669843 max_depth 3 threshold 0.6128544114230887,101,0.6836600082669843,3,0.6128544114230887,0.28329111135587615,0,None,i7186,8,0.0005736166062935706 1727291339,1727291351,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.8643544624902417 max_depth 3 threshold 0.2,120,0.8643544624902417,3,0.2,0.2921026544773654,0,None,i7186,9,0.0005661416455556771 1727291353,1727291362,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 120 const 0.9792758138678951 max_depth 2 threshold 0.7836802716936606,120,0.9792758138678951,2,0.7836802716936606,0.2919374380438374,0,None,i7186,7,0.0005654073502955533 1727291519,1727291530,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.4327650900768182 max_depth 2 threshold 0.22966241769079535,121,0.4327650900768182,2,0.22966241769079535,0.28593457429232294,0,None,i7186,8,0.0006541677705902835 1727291533,1727291544,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.4408981779423694 max_depth 2 threshold 0.2,140,0.4408981779423694,2,0.2,0.2886881815177883,0,None,i7186,7,0.000716798380878951 1727291533,1727291560,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 874 const 0.5310177586972714 max_depth 4 threshold 0.6295944249257446,874,0.5310177586972714,4,0.6295944249257446,0.2930939530785329,0,None,i7186,23,0.004146932481550824 1727291559,1727291571,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.2627691987734465 max_depth 3 threshold 0.2,101,0.2627691987734465,3,0.2,0.281638947020597,0,None,i7186,9,0.0005921802505101902 1727291559,1727291572,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.1 max_depth 3 threshold 0.4635914017117386,140,0.1,3,0.4635914017117386,0.2901751294195396,0,None,i7186,9,0.000693564819914087 1727291579,1727291591,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.19883136211652774 max_depth 2 threshold 0.6249185152343761,101,0.19883136211652774,2,0.6249185152343761,0.280923009141976,0,None,i7186,8,0.0006002244963373925 1727291593,1727291604,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 875 const 0.4253468588231263 max_depth 2 threshold 0.6526425737156419,875,0.4253468588231263,2,0.6526425737156419,0.2913867165987444,0,None,i7186,7,0.004317656129529679 1727291593,1727291605,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.6079475708531568 max_depth 4 threshold 0.7289726885542744,140,0.6079475708531568,4,0.7289726885542744,0.28742152219407424,0,None,i7186,8,0.0007365899328119832 1727291619,1727291630,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.7950450664769922 max_depth 2 threshold 0.42535745041648176,140,0.7950450664769922,2,0.42535745041648176,0.2886881815177883,0,None,i7186,7,0.000716798380878951 1727291619,1727291630,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 106 const 1 max_depth 4 threshold 0.8,106,1,4,0.8,0.28918383081837207,0,None,i7186,8,0.0005338758479490483 1727291639,1727291651,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 140 const 0.2925138513104223 max_depth 3 threshold 0.5631485561865279,140,0.2925138513104223,3,0.5631485561865279,0.28890847009582554,0,None,i7186,8,0.0007133563718471191 1727291799,1727291809,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.3881971743591405 max_depth 2 threshold 0.7049415862130475,101,0.3881971743591405,2,0.7049415862130475,0.28329111135587615,0,None,i7182,7,0.0005736166062935706 1727291819,1727291829,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.3292100642002024 max_depth 3 threshold 0.8,101,0.3292100642002024,3,0.8,0.28329111135587615,0,None,i7182,7,0.0005736166062935706 1727291563,1727291835,272,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 357 const 0.42176078579801224 max_depth 4 threshold 0.20936574226819893,357,0.42176078579801224,4,0.20936574226819893,0.2995924661306312,0,None,i7182,269,0.0017485405881704995 1727291833,1727291843,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.48504106669005853,108,1,3,0.48504106669005853,0.2792708448066967,0,None,i7182,7,0.0006582432510398148 1727291833,1727291844,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.38636223432705397 max_depth 2 threshold 0.2801732267282281,101,0.38636223432705397,2,0.2801732267282281,0.2813085141535412,0,None,i7182,7,0.0005958929793535139 1727291859,1727291870,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.3550571502989406 max_depth 3 threshold 0.6091402860739682,102,0.3550571502989406,3,0.6091402860739682,0.2874765943385835,0,None,i7183,8,0.0005325726701979591 1727291879,1727291891,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 1 max_depth 4 threshold 0.8,101,1,4,0.8,0.2831258949223483,0,None,i7183,8,0.0005754729707152318 1727291880,1727291892,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.9743481590538368 max_depth 4 threshold 0.2020565503367477,101,0.9743481590538368,4,0.2020565503367477,0.2811983698645225,0,None,i7183,9,0.0005971305556346226 1727291894,1727291905,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.33984553276746965 max_depth 2 threshold 0.28149568348786436,101,0.33984553276746965,2,0.28149568348786436,0.2813085141535412,0,None,i7183,8,0.0005958929793535139 1727291899,1727291911,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.997969911446551 max_depth 4 threshold 0.2,144,0.997969911446551,4,0.2,0.28659544002643467,0,None,i7183,8,0.0007613942518667698 1727291919,1727291931,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.3991212375262605 max_depth 4 threshold 0.5035154457165358,101,0.3991212375262605,4,0.5035154457165358,0.281638947020597,0,None,i7183,9,0.0005921802505101902 1727291859,1727291970,111,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 342 const 0.9190543433213013 max_depth 4 threshold 0.7754032201238621,342,0.9190543433213013,4,0.7754032201238621,0.29364467452362597,0,None,i7183,108,0.0017049418071006333 1727292119,1727292129,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.9209026703761299 max_depth 2 threshold 0.2,101,0.9209026703761299,2,0.2,0.2812534420090318,0,None,i7182,7,0.0005965117674940688 1727292119,1727292131,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.1 max_depth 2 threshold 0.2,101,0.1,2,0.2,0.28659544002643467,0,None,i7182,9,0.000538964470422545 1727292134,1727292144,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.8796234540442482 max_depth 3 threshold 0.2,101,0.8796234540442482,3,0.2,0.2811983698645225,0,None,i7182,7,0.0005971305556346226 1727292139,1727292167,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 468 const 0.687105029177673 max_depth 4 threshold 0.6781107277363905,468,0.687105029177673,4,0.6781107277363905,0.29325916951206077,0,None,i7182,25,0.002173900441156863 1727292159,1727292169,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.8936996896832653 max_depth 3 threshold 0.2,102,0.8936996896832653,3,0.2,0.28549399713624846,0,None,i7182,7,0.0005551021838608576 1727292180,1727292191,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.8114379285308747 max_depth 4 threshold 0.5124865658286337,101,0.8114379285308747,4,0.5124865658286337,0.282354884899218,0,None,i7184,8,0.0005841360046829878 1727292180,1727292191,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.749549943653287 max_depth 4 threshold 0.2,101,0.749549943653287,4,0.2,0.2811983698645225,0,None,i7184,8,0.0005971305556346226 1727292194,1727292203,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 148 const 0.4880898450580914 max_depth 2 threshold 0.2,148,0.4880898450580914,2,0.2,0.2868708007489812,0,None,i7184,6,0.000781843887623934 1727292199,1727292209,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.9999999872743731 max_depth 2 threshold 0.4320586228791727,101,0.9999999872743731,2,0.4320586228791727,0.28329111135587615,0,None,i7184,7,0.0005736166062935706 1727292219,1727292229,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.8287903520787442 max_depth 4 threshold 0.2718260769440311,121,0.8287903520787442,4,0.2718260769440311,0.2819143077431435,0,None,i7184,7,0.0006990490876381387 1727292224,1727292234,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 118 const 0.3613782847957422 max_depth 3 threshold 0.4440472691702598,118,0.3613782847957422,3,0.4440472691702598,0.2939200352461725,0,None,i7184,7,0.0005347795085245878 1727292159,1727292242,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 342 const 0.7816741285558286 max_depth 3 threshold 0.7165143453224891,342,0.7816741285558286,3,0.7165143453224891,0.29298380878951424,0,None,i7182,80,0.001807803004544649 1727292240,1727292249,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 102 const 0.8497915306989109 max_depth 2 threshold 0.2,102,0.8497915306989109,2,0.2,0.2868708007489812,0,None,i7186,7,0.000539456688261622 1727292460,1727292473,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.1 max_depth 4 threshold 0.8,100,0.1,4,0.8,0.2883577486507325,0,None,i7182,10,0.0005109471185030159 1727292464,1727292474,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.4963810814662022 max_depth 2 threshold 0.3292593785375709,100,0.4963810814662022,2,0.3292593785375709,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292480,1727292489,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292494,1727292504,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292494,1727292504,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9056838922033021 max_depth 3 threshold 0.8,100,0.9056838922033021,3,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292520,1727292529,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292524,1727292534,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292540,1727292550,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292540,1727292550,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8591949703200328 max_depth 3 threshold 0.4975463774516583,100,0.8591949703200328,3,0.4975463774516583,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292554,1727292564,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292580,1727292590,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.32758573213779046 max_depth 2 threshold 0.5015940371021375,100,0.32758573213779046,2,0.5015940371021375,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292581,1727292591,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.27172159822940417,100,1,3,0.27172159822940417,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292600,1727292609,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292855,1727292865,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.2566146499025213 max_depth 2 threshold 0.8,100,0.2566146499025213,2,0.8,0.28521863641370193,0,None,i7182,7,0.000545826143358911 1727292881,1727292893,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6801492747860677 max_depth 4 threshold 0.8,100,0.6801492747860677,4,0.8,0.28521863641370193,0,None,i7186,8,0.000545826143358911 1727292885,1727292896,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.32607379238398315 max_depth 2 threshold 0.4270724377930093,100,0.32607379238398315,2,0.4270724377930093,0.28521863641370193,0,None,i7186,8,0.000545826143358911 1727292881,1727292906,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 789 const 1 max_depth 2 threshold 0.8,789,1,2,0.8,0.29898667254102873,0,None,i7186,21,0.003234236850273858 1727292900,1727292911,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.27769004873740344 max_depth 2 threshold 0.8,100,0.27769004873740344,2,0.8,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727292915,1727292929,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 789 const 0.4611041256684605 max_depth 2 threshold 0.5584905672947242,789,0.4611041256684605,2,0.5584905672947242,0.2924881594889305,0,None,i7186,10,0.003825010764100972 1727292920,1727292931,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7435655946138622 max_depth 3 threshold 0.5134062654716258,100,0.7435655946138622,3,0.5134062654716258,0.28521863641370193,0,None,i7186,8,0.000545826143358911 1727292940,1727292952,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5757700664838866 max_depth 4 threshold 0.35147078974831336,100,0.5757700664838866,4,0.35147078974831336,0.2853287807027206,0,None,i7186,8,0.0005446023179253702 1727292945,1727292956,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5529693758338039 max_depth 4 threshold 0.8,100,0.5529693758338039,4,0.8,0.28521863641370193,0,None,i7186,8,0.000545826143358911 1727292960,1727292971,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8652931793141196 max_depth 2 threshold 0.7493209643865317,100,0.8652931793141196,2,0.7493209643865317,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727292975,1727292986,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6255094084527647 max_depth 4 threshold 0.5743726908815685,100,0.6255094084527647,4,0.5743726908815685,0.2853287807027206,0,None,i7186,8,0.0005446023179253702 1727292980,1727292991,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727293000,1727293010,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727293005,1727293015,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.2,100,1,2,0.2,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727293200,1727293210,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.7658631427775965 max_depth 4 threshold 0.32779409178680463,132,0.7658631427775965,4,0.32779409178680463,0.29342438594558873,0,None,i7182,7,0.0006049837051243005 1727293215,1727293228,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.4480240073787516 max_depth 4 threshold 0.31478744735842756,131,0.4480240073787516,4,0.31478744735842756,0.28918383081837207,0,None,i7182,9,0.0006576731460241898 1727293240,1727293250,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.8100127545177054 max_depth 2 threshold 0.3458288449468603,121,0.8100127545177054,2,0.3458288449468603,0.2830157506333296,0,None,i7182,7,0.0006843631824356574 1727293240,1727293250,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5471876790066471 max_depth 3 threshold 0.6778140050624041,108,0.5471876790066471,3,0.6778140050624041,0.2792708448066967,0,None,i7182,7,0.0006582432510398148 1727293260,1727293270,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.35654908861297263,108,1,3,0.35654908861297263,0.2792708448066967,0,None,i7182,7,0.0006582432510398148 1727293260,1727293270,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.2959661757936171,108,1,3,0.2959661757936171,0.2790505562286596,0,None,i7182,7,0.0006608657341116854 1727293300,1727293311,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.7030313786849433 max_depth 2 threshold 0.23259785976570962,132,0.7030313786849433,2,0.23259785976570962,0.29414032382420974,0,None,i7186,8,0.0005944552069092857 1727293300,1727293311,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 132 const 0.7447005152541358 max_depth 3 threshold 0.7329615880557525,132,0.7447005152541358,3,0.7329615880557525,0.292047582332856,0,None,i7186,8,0.0006252308170762525 1727293275,1727293331,56,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 344 const 1 max_depth 2 threshold 0.2,344,1,2,0.2,0.2902302015640489,0,None,i7182,53,0.0019275250578257514 1727293320,1727293332,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.488486940999286 max_depth 4 threshold 0.6179092119703238,147,0.488486940999286,4,0.6179092119703238,0.28620993501486947,0,None,i7186,8,0.0007926777521175688 1727293320,1727293333,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 131 const 0.5592557517653735 max_depth 4 threshold 0.49669550504269583,131,0.5592557517653735,4,0.49669550504269583,0.2909461394426699,0,None,i7186,9,0.0006321324413242214 1727293516,1727293527,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.7312497942692682 max_depth 3 threshold 0.5759005393081308,108,0.7312497942692682,3,0.5759005393081308,0.2792708448066967,0,None,i7183,7,0.0006582432510398148 1727293521,1727293532,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.6595251064295452 max_depth 2 threshold 0.41496557635154674,108,0.6595251064295452,2,0.41496557635154674,0.2792708448066967,0,None,i7184,8,0.0006582432510398148 1727293541,1727293552,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8602891811692995 max_depth 3 threshold 0.621403974573101,108,0.8602891811692995,3,0.621403974573101,0.28026214340786426,0,None,i7184,8,0.0006464420772163917 1727293546,1727293557,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.6866217255318308 max_depth 3 threshold 0.3213316606258828,108,0.6866217255318308,3,0.3213316606258828,0.2790505562286596,0,None,i7184,8,0.0006608657341116854 1727293561,1727293572,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.7756800773267246 max_depth 3 threshold 0.7650955857748627,108,0.7756800773267246,3,0.7650955857748627,0.28026214340786426,0,None,i7184,8,0.0006464420772163917 1727293576,1727293587,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.7559864158370682 max_depth 3 threshold 0.44795530398834993,108,0.7559864158370682,3,0.44795530398834993,0.2790505562286596,0,None,i7184,8,0.0006608657341116854 1727293581,1727293592,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.3623166153190762,108,1,3,0.3623166153190762,0.2792708448066967,0,None,i7184,8,0.0006582432510398148 1727293601,1727293612,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.5848098814534258 max_depth 3 threshold 0.6060917713289364,147,0.5848098814534258,3,0.6060917713289364,0.28620993501486947,0,None,i7184,7,0.0007926777521175688 1727293621,1727293631,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 542 const 0.5671442985276225 max_depth 2 threshold 0.6407398191644335,542,0.5671442985276225,2,0.6407398191644335,0.30036347615376147,0,None,i7184,7,0.0021374876087674813 1727293621,1727293632,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.3085735087280006,108,1,3,0.3085735087280006,0.2790505562286596,0,None,i7184,8,0.0006608657341116854 1727293907,1727293917,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7697093174158157 max_depth 2 threshold 0.5005827771286444,100,0.7697093174158157,2,0.5005827771286444,0.28521863641370193,0,None,i7183,7,0.000545826143358911 1727293921,1727293932,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.6610375741419763 max_depth 3 threshold 0.746922623477148,100,0.6610375741419763,3,0.746922623477148,0.28521863641370193,0,None,i7183,7,0.000545826143358911 1727293937,1727293947,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.3897217359734635,100,1,3,0.3897217359734635,0.28521863641370193,0,None,i7183,7,0.000545826143358911 1727293961,1727293970,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9454656348493338 max_depth 3 threshold 0.3624138542385899,100,0.9454656348493338,3,0.3624138542385899,0.28521863641370193,0,None,i7183,6,0.000545826143358911 1727293961,1727293970,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9353937952050013 max_depth 3 threshold 0.40090563529887524,100,0.9353937952050013,3,0.40090563529887524,0.28521863641370193,0,None,i7183,7,0.000545826143358911 1727293981,1727293993,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9239704510277948 max_depth 4 threshold 0.35905242810714383,100,0.9239704510277948,4,0.35905242810714383,0.2853287807027206,0,None,i7184,8,0.0005446023179253702 1727293981,1727293993,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 4 threshold 0.2817614244600743,100,1,4,0.2817614244600743,0.2853287807027206,0,None,i7184,8,0.0005446023179253702 1727293997,1727294008,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 3 threshold 0.3552226319548727,100,1,3,0.3552226319548727,0.28521863641370193,0,None,i7184,8,0.000545826143358911 1727294021,1727294032,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.34650795583122135 max_depth 2 threshold 0.2,100,0.34650795583122135,2,0.2,0.2853287807027206,0,None,i7184,8,0.0005446023179253702 1727294027,1727294038,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.5434411674144404 max_depth 4 threshold 0.7514959501933556,100,0.5434411674144404,4,0.7514959501933556,0.28521863641370193,0,None,i7184,8,0.000545826143358911 1727294327,1727294337,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 1 max_depth 2 threshold 0.3566055738287094,100,1,2,0.3566055738287094,0.28521863641370193,0,None,i7186,6,0.000545826143358911 1727294357,1727294367,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.8759233368012828 max_depth 3 threshold 0.335059727282236,100,0.8759233368012828,3,0.335059727282236,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727294357,1727294385,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 737 const 0.9425886577600473 max_depth 2 threshold 0.8,737,0.9425886577600473,2,0.8,0.2971692917722216,0,None,i7186,25,0.0033844336080265077 1727294381,1727294391,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.9086304300014086 max_depth 4 threshold 0.8,100,0.9086304300014086,4,0.8,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727294381,1727294391,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7480115227556658 max_depth 2 threshold 0.371803986075064,100,0.7480115227556658,2,0.371803986075064,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727294401,1727294434,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 682 const 0.8487691551196175 max_depth 2 threshold 0.3113202597112744,682,0.8487691551196175,2,0.3113202597112744,0.30185042405551277,0,None,i7186,29,0.0027260711532107 1727294422,1727294434,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 817 const 1 max_depth 2 threshold 0.6359563051557885,817,1,2,0.6359563051557885,0.30008811543121494,0,None,i7186,10,0.0031341056784387478 1727294417,1727294448,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 727 const 0.9251472173947151 max_depth 2 threshold 0.7932188653072283,727,0.9251472173947151,2,0.7932188653072283,0.29882145610750077,0,None,i7186,27,0.002978485148878366 1727294441,1727294451,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 100 const 0.7256824055084521 max_depth 3 threshold 0.4208172810811581,100,0.7256824055084521,3,0.4208172810811581,0.28521863641370193,0,None,i7186,7,0.000545826143358911 1727294441,1727294468,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 784 const 1 max_depth 2 threshold 0.6327553330534259,784,1,2,0.6327553330534259,0.2972794360612402,0,None,i7186,23,0.003389440166618271 1727294741,1727294753,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8390847168812725 max_depth 3 threshold 0.2,108,0.8390847168812725,3,0.2,0.2789404119396409,0,None,i7184,7,0.0006621769756476219 1727294761,1727294771,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 136 const 1 max_depth 2 threshold 0.5169484495409253,136,1,2,0.5169484495409253,0.28560414142526713,0,None,i7186,6,0.0007418050980117278 1727294778,1727294789,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.6527158123990908 max_depth 3 threshold 0.5387815033241972,108,0.6527158123990908,3,0.5387815033241972,0.2792708448066967,0,None,i7186,7,0.0006582432510398148 1727294765,1727294807,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.7757061800713599 max_depth 3 threshold 0.287211329509643,108,0.7757061800713599,3,0.287211329509643,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727294801,1727294811,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 147 const 0.1263126453339046 max_depth 2 threshold 0.4681331443593025,147,0.1263126453339046,2,0.4681331443593025,0.28620993501486947,0,None,i7186,6,0.0007926777521175688 1727294801,1727294811,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 470 const 0.12988355183697042 max_depth 2 threshold 0.6713375132248658,470,0.12988355183697042,2,0.6713375132248658,0.28929397510739063,0,None,i7186,7,0.0023825948835079227 1727294838,1727294848,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.6341591464828983 max_depth 4 threshold 0.6145412997372455,108,0.6341591464828983,4,0.6145412997372455,0.28026214340786426,0,None,i7186,7,0.0006464420772163917 1727294821,1727294877,56,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 790 const 0.8773234614945278 max_depth 4 threshold 0.6656958766048335,790,0.8773234614945278,4,0.6656958766048335,0.2971692917722216,0,None,i7186,53,0.003399453283801777 1727294877,1727294887,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 1 max_depth 3 threshold 0.2959015629272737,108,1,3,0.2959015629272737,0.2790505562286596,0,None,i7186,7,0.0006608657341116854 1727294877,1727294888,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.6635856158596516 max_depth 3 threshold 0.5207991482166946,108,0.6635856158596516,3,0.5207991482166946,0.2790505562286596,0,None,i7186,8,0.0006608657341116854 1727294841,1727295043,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 334 const 0.6155880509978253 max_depth 4 threshold 0.4182249385794802,334,0.6155880509978253,4,0.4182249385794802,0.2975547967837867,0,None,i7186,198,0.0016090643960980197 1727295061,1727295071,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.941939112478402 max_depth 3 threshold 0.2,108,0.941939112478402,3,0.2,0.2790505562286596,0,None,i7182,7,0.0006608657341116854 1727295101,1727295111,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 126 const 0.3608024925870288 max_depth 3 threshold 0.23337101756777234,126,0.3608024925870288,3,0.23337101756777234,0.2897896244079744,0,None,i7182,7,0.000620326516625668 1727295101,1727295113,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 296 const 0.606204066115996 max_depth 2 threshold 0.6916360138884411,296,0.606204066115996,2,0.6916360138884411,0.2898997686969931,0,None,i7182,9,0.001488783639901603 1727295108,1727295120,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.8213443533229745 max_depth 4 threshold 0.452785104327193,149,0.8213443533229745,4,0.452785104327193,0.28516356426919265,0,None,i7182,9,0.000823328560414142 1727295121,1727295131,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5724976886327997 max_depth 3 threshold 0.721650478231562,108,0.5724976886327997,3,0.721650478231562,0.28026214340786426,0,None,i7182,7,0.0006464420772163917 1727295138,1727295166,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 333 const 0.11974514366578962 max_depth 2 threshold 0.6228581872234985,333,0.11974514366578962,2,0.6228581872234985,0.2897896244079744,0,None,i7182,25,0.0017909461394426706 1727295161,1727295171,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.14992105570024927 max_depth 3 threshold 0.7235384402774849,125,0.14992105570024927,3,0.7235384402774849,0.2880273157836766,0,None,i7182,7,0.0006432732435045466 1727295161,1727295186,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 297 const 0.5721191521521889 max_depth 4 threshold 0.2524095135971304,297,0.5721191521521889,4,0.2524095135971304,0.29072585086463265,0,None,i7182,22,0.0014612475676469504 1727295439,1727295449,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.433167780743999 max_depth 3 threshold 0.49116030597312715,108,0.433167780743999,3,0.49116030597312715,0.2789404119396409,0,None,i7182,7,0.0006621769756476219 1727295442,1727295452,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.439721476498542 max_depth 3 threshold 0.491035130099369,108,0.439721476498542,3,0.491035130099369,0.2789404119396409,0,None,i7182,7,0.0006621769756476219 1727295462,1727295475,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 612 const 0.31903348220512273 max_depth 2 threshold 0.674374244734645,612,0.31903348220512273,2,0.674374244734645,0.2928185923559864,0,None,i7186,10,0.002981763252718198 1727295482,1727295493,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 125 const 0.7158036944556702 max_depth 2 threshold 0.430496076691957,125,0.7158036944556702,2,0.430496076691957,0.2878620993501487,0,None,i7186,8,0.0006455679161924333 1727295482,1727295493,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.4355164372028174 max_depth 3 threshold 0.4962633472477939,108,0.4355164372028174,3,0.4962633472477939,0.2789404119396409,0,None,i7186,8,0.0006621769756476219 1727295499,1727295514,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 298 const 0.6888452904539923 max_depth 2 threshold 0.3648526868380553,298,0.6888452904539923,2,0.3648526868380553,0.2912765723097257,0,None,i7186,11,0.0014428901861438489 1727295529,1727295540,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.43323347951509994 max_depth 3 threshold 0.49071919342934206,108,0.43323347951509994,3,0.49071919342934206,0.2789404119396409,0,None,i7186,8,0.0006621769756476219 1727295522,1727295545,23,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 300 const 0.3451011709730038 max_depth 4 threshold 0.27690567134768157,300,0.3451011709730038,4,0.27690567134768157,0.29144178874325366,0,None,i7186,20,0.001437382971692917 1727295542,1727295552,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.4307440387009408 max_depth 3 threshold 0.5260151860829917,108,0.4307440387009408,3,0.5260151860829917,0.2790505562286596,0,None,i7186,7,0.0006608657341116854 1727295559,1727295570,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.44153394870907425 max_depth 3 threshold 0.4842296780341548,108,0.44153394870907425,3,0.4842296780341548,0.2789404119396409,0,None,i7186,7,0.0006621769756476219 1727295902,1727295912,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5185741870770689 max_depth 3 threshold 0.4974267134561309,108,0.5185741870770689,3,0.4974267134561309,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727295920,1727295929,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5188318077200194 max_depth 3 threshold 0.48880910677732037,108,0.5188318077200194,3,0.48880910677732037,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727295943,1727295954,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5189836332416794 max_depth 3 threshold 0.4887332733839035,108,0.5189836332416794,3,0.4887332733839035,0.2790505562286596,0,None,i7186,8,0.0006608657341116854 1727295943,1727295956,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.5272670212019362 max_depth 4 threshold 0.5828067016648711,149,0.5272670212019362,4,0.5828067016648711,0.28516356426919265,0,None,i7186,10,0.000823328560414142 1727295962,1727295973,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5196085141251813 max_depth 3 threshold 0.47852603048382775,108,0.5196085141251813,3,0.47852603048382775,0.2790505562286596,0,None,i7186,7,0.0006608657341116854 1727295980,1727295990,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.5180692650937453 max_depth 3 threshold 0.49381407614293404,108,0.5180692650937453,3,0.49381407614293404,0.2790505562286596,0,None,i7186,7,0.0006608657341116854 1727295982,1727295992,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 141 const 1 max_depth 4 threshold 0.7813099116255449,141,1,4,0.7813099116255449,0.2852737085582112,0,None,i7186,7,0.000770149520872343 1727296002,1727296013,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.3747727701323531 max_depth 3 threshold 0.5444317408619732,149,0.3747727701323531,3,0.5444317408619732,0.2853287807027206,0,None,i7186,7,0.000820574953188676 1727296023,1727296033,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.4797346287325831 max_depth 4 threshold 0.46724635539478443,108,0.4797346287325831,4,0.46724635539478443,0.28026214340786426,0,None,i7186,7,0.0006464420772163917 1727296040,1727296053,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.44980544281282697 max_depth 3 threshold 0.48811435532085673,152,0.44980544281282697,3,0.48811435532085673,0.2850534199801741,0,None,i7186,9,0.0008391501341333404 1727296042,1727296053,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 107 const 0.3940487770392651 max_depth 4 threshold 0.6959244767386052,107,0.3940487770392651,4,0.6959244767386052,0.2834563277894041,0,None,i7186,7,0.0006057935896023788 1727296343,1727296352,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.4852196413009545 max_depth 3 threshold 0.5445761052914926,108,0.4852196413009545,3,0.5445761052914926,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727296362,1727296372,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3964725814895905 max_depth 2 threshold 0.3128098725188175,108,0.3964725814895905,2,0.3128098725188175,0.2790505562286596,0,None,i7185,6,0.0006608657341116854 1727296383,1727296392,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 209 const 0.34099573781166576 max_depth 2 threshold 0.5173658988527448,209,0.34099573781166576,2,0.5173658988527448,0.2902302015640489,0,None,i7185,7,0.0010310017751160997 1727296401,1727296410,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.44962631088896376 max_depth 3 threshold 0.4999566252218707,108,0.44962631088896376,3,0.4999566252218707,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727296423,1727296432,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 210 const 0.44053213163887994 max_depth 2 threshold 0.35248979572445027,210,0.44053213163887994,2,0.35248979572445027,0.28510849212468337,0,None,i7185,6,0.0011501112969618094 1727296423,1727296434,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 877 const 0.688107543483078 max_depth 2 threshold 0.37478751918723047,877,0.688107543483078,2,0.37478751918723047,0.2919374380438374,0,None,i7185,9,0.004262583985020374 1727296444,1727296453,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.6308733571580828 max_depth 2 threshold 0.7813620836329889,152,0.6308733571580828,2,0.7813620836329889,0.28037228769688294,0,None,i7185,6,0.0009184913592738683 1727296461,1727296472,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 101 const 0.5148494901375251 max_depth 4 threshold 0.2,101,0.5148494901375251,4,0.2,0.281638947020597,0,None,i7186,8,0.0005921802505101902 1727296461,1727296474,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 106 const 0.31345189026259057 max_depth 3 threshold 0.3197872600259667,106,0.31345189026259057,3,0.3197872600259667,0.2876418107721115,0,None,i7186,9,0.0005494256299281464 1727296483,1727296532,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 293 const 0.88040682752212 max_depth 4 threshold 0.2190856357368981,293,0.88040682752212,4,0.2190856357368981,0.29232294305540263,0,None,i7186,45,0.0014080111612879513 1727296851,1727296861,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.36700774288555604 max_depth 3 threshold 0.30770531988143884,108,0.36700774288555604,3,0.30770531988143884,0.2793259169512061,0,None,i7185,7,0.0006575876302718457 1727296881,1727296891,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.39471500385391534 max_depth 3 threshold 0.3607924357077132,108,0.39471500385391534,3,0.3607924357077132,0.2793259169512061,0,None,i7185,7,0.0006575876302718457 1727296881,1727296891,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.7678262286575585 max_depth 3 threshold 0.8,152,0.7678262286575585,3,0.8,0.2819693798876528,0,None,i7185,7,0.0008914220001082773 1727296903,1727296912,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.741742060254328 max_depth 2 threshold 0.3863066867673728,152,0.741742060254328,2,0.3863066867673728,0.28290560634431106,0,None,i7185,7,0.0008755537550801714 1727296912,1727296922,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.7296329896771747 max_depth 3 threshold 0.7114601251751964,152,0.7296329896771747,3,0.7114601251751964,0.28257517347725525,0,None,i7185,7,0.0008811543121489139 1727296923,1727296932,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 151 const 0.7673222955341906 max_depth 2 threshold 0.6990417151040944,151,0.7673222955341906,2,0.6990417151040944,0.28411719352351583,0,None,i7185,6,0.0008407680728420889 1727296941,1727296951,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 153 const 0.9329937250620101 max_depth 2 threshold 0.2954010111745269,153,0.9329937250620101,2,0.2954010111745269,0.2884128207952418,0,None,i7185,6,0.0007822111372677863 1727296963,1727296972,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.38248112444339666 max_depth 3 threshold 0.2513640659473666,108,0.38248112444339666,3,0.2513640659473666,0.2793259169512061,0,None,i7185,7,0.0006575876302718457 1727296983,1727296992,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 153 const 0.6235796826313862 max_depth 2 threshold 0.8,153,0.6235796826313862,2,0.8,0.28632007930388814,0,None,i7185,6,0.000817681332036492 1727297002,1727297012,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3252124286925099 max_depth 3 threshold 0.29503518801843265,108,0.3252124286925099,3,0.29503518801843265,0.2793259169512061,0,None,i7186,7,0.0006575876302718457 1727297362,1727297372,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.43590227143696525 max_depth 2 threshold 0.42514036622155615,108,0.43590227143696525,2,0.42514036622155615,0.2790505562286596,0,None,i7185,6,0.0006608657341116854 1727297383,1727297393,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.7085547340706002 max_depth 3 threshold 0.38080344275967726,108,0.7085547340706002,3,0.38080344275967726,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727297403,1727297412,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3321465250540385 max_depth 2 threshold 0.2,108,0.3321465250540385,2,0.2,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727297422,1727297431,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.7871603660034976 max_depth 2 threshold 0.8,152,0.7871603660034976,2,0.8,0.28037228769688294,0,None,i7185,6,0.0009184913592738683 1727297452,1727297463,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.5781576091514598 max_depth 2 threshold 0.8,152,0.5781576091514598,2,0.8,0.28037228769688294,0,None,i7186,7,0.0009184913592738683 1727297464,1727297475,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.40316955470850635 max_depth 2 threshold 0.4135483203510457,108,0.40316955470850635,2,0.4135483203510457,0.2790505562286596,0,None,i7186,7,0.0006608657341116854 1727297443,1727297479,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 954 const 0.6037244001414883 max_depth 3 threshold 0.5676474537700469,954,0.6037244001414883,3,0.5676474537700469,0.29882145610750077,0,None,i7186,30,0.003971313531837822 1727297504,1727297515,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.356121459760812 max_depth 2 threshold 0.2,108,0.356121459760812,2,0.2,0.2789404119396409,0,None,i7186,8,0.0006621769756476219 1727297482,1727297533,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 329 const 0.9038134047543843 max_depth 3 threshold 0.42075496954735936,329,0.9038134047543843,3,0.42075496954735936,0.29369974666813525,0,None,i7185,48,0.0016345412490362365 1727297392,1727297541,149,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 328 const 0.9063703560016807 max_depth 4 threshold 0.2793842875893896,328,0.9063703560016807,4,0.2793842875893896,0.30190549620002205,0,None,i7185,146,0.0013607409039174633 1727297813,1727297824,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 530 const 0.7665256650037499 max_depth 2 threshold 0.7434411452687144,530,0.7665256650037499,2,0.7434411452687144,0.29325916951206077,0,None,i7185,8,0.002429653434234141 1727297843,1727297853,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 211 const 0.7191163231622678 max_depth 2 threshold 0.5604478500119627,211,0.7191163231622678,2,0.5604478500119627,0.2913316444542351,0,None,i7185,7,0.0010293246057096685 1727297843,1727297853,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 211 const 0.4432409865173381 max_depth 3 threshold 0.7746197499469349,211,0.4432409865173381,3,0.7746197499469349,0.292047582332856,0,None,i7185,7,0.0010122784657425042 1727297863,1727297873,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 151 const 1 max_depth 4 threshold 0.8,151,1,4,0.8,0.2846128428240996,0,None,i7185,7,0.0008325072511656928 1727297884,1727297896,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 211 const 0.23176864909198092 max_depth 3 threshold 0.7395161156032268,211,0.23176864909198092,3,0.7395161156032268,0.29116642802070714,0,None,i7185,9,0.001033258330317477 1727297903,1727297912,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 150 const 0.9164286318379541 max_depth 4 threshold 0.8,150,0.9164286318379541,4,0.8,0.29000991298601164,0,None,i7185,6,0.0007425560818004921 1727297903,1727297912,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3213461245571258 max_depth 2 threshold 0.2,108,0.3213461245571258,2,0.2,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727297924,1727297934,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 211 const 0.7085145799682069 max_depth 2 threshold 0.25226276697682537,211,0.7085145799682069,2,0.25226276697682537,0.29105628373168846,0,None,i7185,7,0.00103588081338935 1727298325,1727298334,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8440837673574009 max_depth 3 threshold 0.2,108,0.8440837673574009,3,0.2,0.2789404119396409,0,None,i7185,7,0.0006621769756476219 1727298344,1727298369,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 344 const 0.14436165171248505 max_depth 2 threshold 0.49792616191855316,344,0.14436165171248505,2,0.49792616191855316,0.3009692697433638,0,None,i7185,22,0.0013437603260270947 1727298364,1727298374,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 153 const 0.1 max_depth 3 threshold 0.34583733849748327,153,0.1,3,0.34583733849748327,0.2871461614715277,0,None,i7185,7,0.0008036799393646348 1727298344,1727298427,83,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 758 const 0.5244182440492788 max_depth 4 threshold 0.40915855307963067,758,0.5244182440492788,4,0.40915855307963067,0.29149686088776294,0,None,i7185,80,0.003915128818752566 1727298384,1727298429,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 758 const 0.3593020755892732 max_depth 3 threshold 0.4291247848188267,758,0.3593020755892732,3,0.4291247848188267,0.29601277673752613,0,None,i7185,43,0.00350459101422864 1727298424,1727298446,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 308 const 0.8036627856084114 max_depth 2 threshold 0.3653276450860089,308,0.8036627856084114,2,0.3653276450860089,0.2930939530785329,0,None,i7185,19,0.0014810473148395803 1727298404,1727298453,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 759 const 0.8891687236735519 max_depth 3 threshold 0.22687784057951396,759,0.8891687236735519,3,0.22687784057951396,0.29513162242537727,0,None,i7185,46,0.0035846959516967177 1727298414,1727298463,49,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 757 const 0.5231094119533874 max_depth 3 threshold 0.6931744370118014,757,0.5231094119533874,3,0.6931744370118014,0.29215772662187467,0,None,i7185,47,0.0038550501156514997 1727298464,1727298473,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3262920153891534 max_depth 2 threshold 0.244667543887781,108,0.3262920153891534,2,0.244667543887781,0.2789404119396409,0,None,i7185,7,0.0006621769756476219 1727298444,1727298492,48,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 760 const 0.6862770799300885 max_depth 3 threshold 0.7684211179807818,760,0.6862770799300885,3,0.7684211179807818,0.2928736645004957,0,None,i7185,45,0.003789964853958681 1727298864,1727298876,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3521349483439997 max_depth 2 threshold 0.20000002653816562,108,0.3521349483439997,2,0.20000002653816562,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727298884,1727298893,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 875 const 0.398656705080248 max_depth 2 threshold 0.5952276438429167,875,0.398656705080248,2,0.5952276438429167,0.2913867165987444,0,None,i7185,6,0.004317656129529679 1727298904,1727298931,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 755 const 0.8152263292665409 max_depth 2 threshold 0.267567233122452,755,0.8152263292665409,2,0.267567233122452,0.2958475603039983,0,None,i7185,24,0.003519610690003899 1727298924,1727298934,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 880 const 0.48668703150331327 max_depth 2 threshold 0.6897308074086961,880,0.48668703150331327,2,0.6897308074086961,0.2908359951536513,0,None,i7185,7,0.0043727282740389836 1727298944,1727298954,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8347993663934496 max_depth 2 threshold 0.2,108,0.8347993663934496,2,0.2,0.2790505562286596,0,None,i7185,6,0.0006608657341116854 1727298944,1727298959,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 271 const 0.583569317878885 max_depth 4 threshold 0.4757321768030403,271,0.583569317878885,4,0.4757321768030403,0.29667364247163786,0,None,i7185,11,0.001148170770375858 1727298964,1727298977,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 877 const 0.6112221251218489 max_depth 3 threshold 0.25631944378855265,877,0.6112221251218489,3,0.25631944378855265,0.2939751073906818,0,None,i7185,10,0.004058817050335939 1727298984,1727298994,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 1 max_depth 2 threshold 0.41436407963217636,152,1,2,0.41436407963217636,0.28224474061019933,0,None,i7185,7,0.0008867548692176583 1727299004,1727299014,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 152 const 0.8701182378387233 max_depth 3 threshold 0.5276103371610729,152,0.8701182378387233,3,0.5276103371610729,0.28373168851195063,0,None,i7185,7,0.0008615523624083142 1727299405,1727299420,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 763 const 0.260663989417414 max_depth 2 threshold 0.32502900940423074,763,0.260663989417414,2,0.32502900940423074,0.29447075669126554,0,None,i7185,12,0.0036447746547977836 1727299425,1727299434,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.35463642547789964 max_depth 2 threshold 0.232785827841729,108,0.35463642547789964,2,0.232785827841729,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727299435,1727299444,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 121 const 0.38927585043995316 max_depth 3 threshold 0.8,121,0.38927585043995316,3,0.8,0.2819143077431435,0,None,i7185,6,0.0006990490876381387 1727299465,1727299475,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.6489119335679436 max_depth 4 threshold 0.2,144,0.6489119335679436,4,0.2,0.28659544002643467,0,None,i7185,7,0.0007613942518667698 1727299485,1727299495,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 154 const 0.10217533899104492 max_depth 4 threshold 0.509280790888575,154,0.10217533899104492,4,0.509280790888575,0.29226787091089323,0,None,i7185,7,0.0007292311548818609 1727299485,1727299502,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 428 const 0.4780549199988945 max_depth 2 threshold 0.4921167955463022,428,0.4780549199988945,2,0.4921167955463022,0.2913316444542351,0,None,i7185,14,0.0021615816719903038 1727299505,1727299528,23,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 847 const 0.22240030923924944 max_depth 3 threshold 0.7389408690854742,847,0.22240030923924944,3,0.7389408690854742,0.299206961119066,0,None,i7185,20,0.0035356316774975195 1727299525,1727299534,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 0.6039026912939073 max_depth 3 threshold 0.539495441654894,144,0.6039026912939073,3,0.539495441654894,0.2845577706795903,0,None,i7185,7,0.0007937382097531881 1727299545,1727299554,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 144 const 1 max_depth 2 threshold 0.4004153567641572,144,1,2,0.4004153567641572,0.2845577706795903,0,None,i7185,6,0.0007937382097531881 1727299425,1727299571,146,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 355 const 0.9121978416855685 max_depth 3 threshold 0.39339000432466853,355,0.9121978416855685,3,0.39339000432466853,0.2995373939861218,0,None,i7185,143,0.0017512941953959694 1727299945,1727299960,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 430 const 0.2441809320040801 max_depth 2 threshold 0.6447649016713949,430,0.2441809320040801,2,0.6447649016713949,0.29705914748320295,0,None,i7185,12,0.0018752065205419111 1727299965,1727299974,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.372937870391716 max_depth 2 threshold 0.3015868211745662,108,0.372937870391716,2,0.3015868211745662,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727299976,1727299985,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3386473773986918 max_depth 2 threshold 0.3048241000611343,108,0.3386473773986918,2,0.3048241000611343,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727300025,1727300035,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.14172629605819959 max_depth 2 threshold 0.2,130,0.14172629605819959,2,0.2,0.2905606344311047,0,None,i7185,7,0.0006377194704773402 1727300025,1727300044,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 746 const 0.41747306007891893 max_depth 2 threshold 0.34799987562000756,746,0.41747306007891893,2,0.34799987562000756,0.29325916951206077,0,None,i7185,16,0.0037549189438164 1727300005,1727300048,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 791 const 0.22724686646834016 max_depth 4 threshold 0.6304687445983291,791,0.22724686646834016,4,0.6304687445983291,0.3024562176451151,0,None,i7185,40,0.002918823658993279 1727300045,1727300055,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 130 const 0.13286468697971654 max_depth 2 threshold 0.6193326053601627,130,0.13286468697971654,2,0.6193326053601627,0.2894041193964093,0,None,i7185,7,0.0006544805579366936 1727300065,1727300074,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 122 const 0.5820411573282526 max_depth 2 threshold 0.8,122,0.5820411573282526,2,0.8,0.2869258728934905,0,None,i7185,6,0.000643748716223658 1727300086,1727300096,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 151 const 0.3445335766890183 max_depth 4 threshold 0.740354627832055,151,0.3445335766890183,4,0.740354627832055,0.28411719352351583,0,None,i7185,7,0.0008407680728420889 1727300505,1727300515,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.3585659172637251 max_depth 2 threshold 0.2663707529938036,108,0.3585659172637251,2,0.2663707529938036,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727300525,1727300535,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.6945699327356711 max_depth 3 threshold 0.36907278910300745,108,0.6945699327356711,3,0.36907278910300745,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727300546,1727300555,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 149 const 0.6797789084312541 max_depth 2 threshold 0.8,149,0.6797789084312541,2,0.8,0.2845577706795903,0,None,i7185,6,0.0008334251202408475 1727300566,1727300575,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 150 const 0.4866322254226283 max_depth 3 threshold 0.8,150,0.4866322254226283,3,0.8,0.29000991298601164,0,None,i7185,6,0.0007425560818004921 1727300577,1727300586,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.36002164753047516 max_depth 2 threshold 0.27031804587322295,108,0.36002164753047516,2,0.27031804587322295,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727300586,1727300595,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8640811140079305 max_depth 3 threshold 0.2,108,0.8640811140079305,3,0.2,0.2790505562286596,0,None,i7185,7,0.0006608657341116854 1727300606,1727300615,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.5153824347489385 max_depth 2 threshold 0.6824106697432614,128,0.5153824347489385,2,0.6824106697432614,0.2831809670668576,0,None,i7185,6,0.0007340330118169084 1727300626,1727300636,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.5208510435240261 max_depth 3 threshold 0.3481247499117097,124,0.5208510435240261,3,0.3481247499117097,0.282354884899218,0,None,i7185,7,0.000715183465682509 1727300646,1727300655,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.35875379948532227 max_depth 2 threshold 0.26556529342106006,108,0.35875379948532227,2,0.26556529342106006,0.2789404119396409,0,None,i7185,6,0.0006621769756476219 1727300666,1727300676,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 109 const 0.22696861661377968 max_depth 4 threshold 0.467378042585722,109,0.22696861661377968,4,0.467378042585722,0.28879832580680687,0,None,i7185,7,0.0005487308856529766 1727301238,1727301248,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.3309119610838255 max_depth 2 threshold 0.2,124,0.3309119610838255,2,0.2,0.28180416345412496,0,None,i7185,7,0.0007227275950673454 1727301266,1727301277,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 129 const 0.6603345511665365 max_depth 3 threshold 0.3250027953322004,129,0.6603345511665365,3,0.3250027953322004,0.2875316664830928,0,None,i7185,7,0.000671880163013548 1727301286,1727301296,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.719003199490057 max_depth 4 threshold 0.6109534442604615,128,0.719003199490057,4,0.6109534442604615,0.2831809670668576,0,None,i7185,7,0.0007340330118169084 1727301326,1727301336,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 108 const 0.8369671487253371 max_depth 4 threshold 0.3024242288192884,108,0.8369671487253371,4,0.3024242288192884,0.2801519991188457,0,None,i7185,7,0.000647753318752327 1727301346,1727301355,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.8119157024205044 max_depth 2 threshold 0.5277599826145427,128,0.8119157024205044,2,0.5277599826145427,0.2831809670668576,0,None,i7185,6,0.0007340330118169084 1727301358,1727301368,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.4477158484856434 max_depth 4 threshold 0.8,128,0.4477158484856434,4,0.8,0.2831809670668576,0,None,i7185,7,0.0007340330118169084 1727301386,1727301396,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.48081316801102436 max_depth 4 threshold 0.4091175325150983,128,0.48081316801102436,4,0.4091175325150983,0.2831809670668576,0,None,i7185,7,0.0007340330118169084 1727301418,1727301429,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.2910956786390539 max_depth 4 threshold 0.2,124,0.2910956786390539,4,0.2,0.2819143077431435,0,None,i7185,7,0.0007182011174364438 1727301466,1727301475,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 128 const 0.3290573964177237 max_depth 2 threshold 0.8,128,0.3290573964177237,2,0.8,0.2831809670668576,0,None,i7185,6,0.0007340330118169084 1727301446,1727301482,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 304 const 0.9911881851119192 max_depth 3 threshold 0.4208394858164125,304,0.9911881851119192,3,0.4208394858164125,0.2981055182288799,0,None,i7185,32,0.001302062845184332 1727302047,1727302056,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 124 const 0.27781503877022606 max_depth 3 threshold 0.6306416174789716,124,0.27781503877022606,3,0.6306416174789716,0.2808679369974667,0,None,i7185,7,0.0007325349632676332 1727302079,1727302089,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 127 const 0.6765939519337618 max_depth 3 threshold 0.5368922642153748,127,0.6765939519337618,3,0.5368922642153748,0.29111135587619785,0,None,i7185,7,0.000611998901659765
timestamp,ram_usage_mb,cpu_usage_percent 1727287795,475.01953125,34.5 1727287795,475.01953125,34.8 1727287795,475.1953125,35.3 1727287795,475.1953125,30.3 1727287795,475.1953125,27.6 1727287795,475.1953125,34.9 1727287795,475.1953125,46.2 1727287838,483.36328125,36.3 1727287838,483.36328125,33.3 1727287838,483.36328125,37.7 1727287838,483.36328125,32.3 1727287841,483.3671875,37.8 1727287841,483.3671875,28.1 1727287841,483.3671875,39.2 1727287841,483.3671875,44.2 1727287843,483.3671875,37.7 1727287843,483.3671875,40.5 1727287843,483.3671875,36.7 1727287843,483.3671875,44.7 1727287845,483.3671875,37.7 1727287845,483.3671875,30.3 1727287845,483.3671875,39.0 1727287845,483.3671875,30.0 1727287847,483.3671875,37.7 1727287847,483.3671875,41.0 1727287847,483.3671875,34.8 1727287847,483.3671875,46.3 1727287849,483.37109375,37.4 1727287849,483.37109375,43.9 1727287849,483.37109375,33.0 1727287849,483.37109375,43.6 1727287852,483.375,36.8 1727287852,483.375,28.1 1727287852,483.375,37.0 1727287852,483.375,30.0 1727287854,483.375,36.3 1727287854,483.375,44.2 1727287854,483.375,36.5 1727287854,483.375,29.0 1727287856,483.375,36.6 1727287856,483.375,30.8 1727287856,483.375,39.3 1727287856,483.375,36.4 1727287858,483.375,36.7 1727287858,483.375,46.3 1727287858,483.375,36.6 1727287858,483.375,30.0 1727287859,483.375,36.6 1727287859,483.375,29.0 1727287859,483.375,36.6 1727287859,483.375,29.0 1727287861,483.375,36.6 1727287861,483.375,28.1 1727287861,483.375,36.3 1727287861,483.375,33.3 1727287864,483.4609375,35.8 1727287864,483.4609375,29.0 1727287864,483.4609375,35.3 1727287864,483.4609375,33.3 1727287866,483.4609375,35.8 1727287866,483.4609375,45.2 1727287866,483.4609375,36.0 1727287866,483.4609375,29.0 1727287868,483.4609375,35.8 1727287868,483.4609375,42.5 1727287868,483.4609375,35.0 1727287868,483.4609375,31.2 1727287870,483.4609375,35.4 1727287870,483.4609375,43.9 1727287870,483.4609375,34.6 1727287870,483.4609375,28.1 1727287872,483.4609375,35.4 1727287872,483.4609375,27.3 1727287872,483.4609375,38.8 1727287872,483.4609375,30.3 1727287874,483.4609375,35.4 1727287874,483.4609375,28.1 1727287874,483.4609375,35.6 1727287874,483.4609375,46.2 1727287876,483.5234375,35.2 1727287876,483.5234375,28.1 1727287876,483.5234375,35.2 1727287876,483.5234375,41.0 1727287878,483.5234375,35.0 1727287878,483.5234375,28.1 1727287878,483.5234375,34.3 1727287878,483.5234375,46.3 1727287880,483.5234375,35.0 1727287880,483.5234375,28.1 1727287880,483.5234375,35.8 1727287880,483.5234375,44.7 1727287881,483.5234375,35.0 1727287881,483.5234375,43.9 1727287881,483.5234375,34.0 1727287881,483.5234375,29.0 1727287883,483.5234375,35.0 1727287883,483.5234375,39.1 1727287883,483.5234375,34.0 1727287883,483.5234375,29.0 1727287886,483.5234375,34.8 1727287886,483.5234375,30.3 1727287886,483.5234375,37.8 1727287886,483.5234375,30.0 1727287888,483.5234375,34.6 1727287888,483.5234375,32.4 1727287888,483.5234375,33.6 1727287888,483.5234375,42.5 1727287890,483.5234375,34.6 1727287890,483.5234375,26.7 1727287890,483.5234375,34.3 1727287890,483.5234375,32.3 1727287892,483.5234375,34.6 1727287892,483.5234375,45.2 1727287892,483.5234375,33.3 1727287892,483.5234375,29.0 1727287893,483.5234375,34.6 1727287893,483.5234375,28.1 1727287893,483.5234375,38.4 1727287893,483.5234375,31.2 1727287895,483.578125,34.6 1727287895,483.578125,30.3 1727287895,483.578125,33.7 1727287895,483.578125,29.0 1727287897,483.578125,34.6 1727287897,483.578125,42.5 1727287897,483.578125,34.0 1727287897,483.578125,31.3 1727287899,483.578125,34.6 1727287899,483.578125,41.0 1727287899,483.578125,33.7 1727287899,483.578125,27.6 1727287901,483.578125,34.6 1727287901,483.578125,40.0 1727287901,483.578125,33.7 1727287901,483.578125,31.4 1727287903,483.59375,34.6 1727287903,483.59375,28.6 1727287903,483.59375,38.1 1727287903,483.59375,26.7 1727287905,483.59375,34.6 1727287905,483.59375,40.0 1727287905,483.59375,34.0 1727287905,483.59375,29.0 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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 (2024-09-25 20:10:06,768) - Starting with JobEnvironment(job_id=2204757, hostname=i7182, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-25 20:10:06,770) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/BayesianNonparametricDetectionMethod_NOAAWeather/0/single_runs/2204757/2204757_submitted.pkl
Parameters: {"n_samples": 769, "const": 0.17955736368894576, "max_depth": 4, "threshold": 0.6845675587654114}
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 NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 769 const 0.17955736368894576 max_depth 4 threshold 0.6845675587654114
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: 1727287748.0
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Hostname: i7182
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: 1727287748.0
Last modification: 1727277242.0
Hostname: i7182
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 BayesianNonparametricDetectionMethod n_samples 769 const 0.17955736368894576 max_depth 4 threshold 0.6845675587654114
stdout:
RESULT: 0.2966185703271286
OO-Info: runtime: 72
OO-Info: lpd: 0.003449518869719327
Result: 0.2966185703271286
EXIT_CODE: 0
submitit INFO (2024-09-25 20:11:23,955) - Job completed successfully
submitit INFO (2024-09-25 20:11:23,957) - Exiting after successful completion
Job-Infos
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text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
scroll-behavior: smooth;
text-wrap: balance;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-feature-settings: "ss02", "liga", "onum";
}
.marked_text {
background-color: yellow;
}
.time_picker_container {
font-variant: small-caps;
width: 100%;
}
.time_picker_container > input {
width: 50px;
}
#loader {
display: grid;
justify-content: center;
align-items: center;
height: 100%;
}
.no_linebreak {
line-break: auto;
}
.dark_code_bg {
background-color: #363636;
color: white;
}
.code_bg {
background-color: #C0C0C0;
}
#commands {
line-break: anywhere;
}
.color_red {
color: red;
}
.color_orange {
color: orange;
}
table > tbody > tr:nth-child(odd) {
background-color: #fafafa;
}
table > tbody > tr:nth-child(even) {
background-color: #ddd;
}
table {
border-collapse: collapse;
margin: 25px 0;
min-width: 200px;
}
th {
background-color: #4eae46;
color: #ffffff;
text-align: left;
border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
min-height: 80px;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 60px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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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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[
1727302095,
514.515625,
28.9
]
];
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));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_method"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
el.textContent.includes("Sobol") ? "orange" :
el.textContent.includes("SAASBO") ? "pink" :
el.textContent.includes("Uniform") ? "lightblue" :
el.textContent.includes("Legacy_GPEI") ? "Sienna" :
el.textContent.includes("BO_MIXED") ? "Aqua" :
el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
el.textContent.includes("BoTorch") ? "yellow" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_trial_status () {
document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
el.textContent.includes("FAILED") ? "red" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_run_time() {
let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
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let min = Math.min(...values);
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cells.forEach(el => {
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el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
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function _colorize_table_entries_by_results() {
result_names.forEach((name, index) => {
let minMax = result_min_max[index];
let selector_query = `[data-column-id="${name}"]`;
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
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let logMin = Math.min(...logValues);
let logMax = Math.max(...logValues);
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cells.forEach(el => {
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let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
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function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
let cells = [...document.querySelectorAll(selector_query)];
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colorMap[value] = `hsl(${hue}, 70%, 60%)`;
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cells.forEach(el => {
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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) {
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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();
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</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>const</th><th>max_depth</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 108</td><td>0.358754</td><td>2</td><td>0.265565</td><td>0.278940 </td></tr></tbody></table><h2>Experiment parameters: </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type </th></tr></thead><tbody><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int </td></tr><tr><td> const</td><td>range</td><td>0.1</td><td>1</td><td></td><td>float </td></tr><tr><td> max_depth</td><td>range</td><td>2</td><td>4</td><td></td><td>int </td></tr><tr><td> threshold</td><td>range</td><td>0.2</td><td>0.8</td><td></td><td>float </td></tr></tbody></table><br><h2>Number of evaluations:</h2>
<table>
<tbody>
<tr>
<th>Failed</th>
<th>Succeeded</th>
<th>Running</th>
<th>Total</th>
</tr>
<tr>
<td>0</td>
<td>492</td>
<td>16</td>
<td>508</td>
</tr>
</tbody>
</table>
<h1> Results</h1>
<div id='tab_results_csv_table'></div>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_samples,const,max_depth,threshold
0,0_0,COMPLETED,Sobol,0.296618570327128572294839159440,769,0.179557363688945759161441628748,4,0.684567558765411421362045985006
1,1_0,COMPLETED,Sobol,0.293038880934023526769749423693,200,0.793849396519362926483154296875,4,0.584290709905326499651323501894
2,2_0,COMPLETED,Sobol,0.298050446084370479482572591223,721,0.281833939626812912671027788747,3,0.548442964069545402239214126894
3,3_0,COMPLETED,Sobol,0.286925872893490474524469391326,119,0.481403946131467863622788172506,4,0.417066838219761870654167523753
4,4_0,COMPLETED,Sobol,0.294746117413812069862899534201,225,0.219617746211588388272062388751,2,0.776847092993557586382280533144
5,5_0,COMPLETED,Sobol,0.290946139442669893249160395499,134,0.765126441605389118194580078125,3,0.701942122541368007659912109375
6,6_0,COMPLETED,Sobol,0.292763520211477001886635207484,200,0.746354395803064063485976475931,4,0.704542430862784563316836283775
7,7_0,COMPLETED,Sobol,0.290009912986011642033190582879,207,0.507500544004142351006692024384,3,0.204876374453306198120117187500
8,8_0,COMPLETED,Sobol,0.300804053309835839336017215828,916,0.316926635522395416799668055319,4,0.705207260511815592352036219381
9,9_0,COMPLETED,Sobol,0.288412820795241775506667636364,135,0.634223212115466616900505414378,2,0.652044752985239117748506032513
10,10_0,COMPLETED,Sobol,0.292653375922458436342310506006,574,0.755943340808153174670280805003,2,0.270165017060935541692856531881
11,11_0,COMPLETED,Sobol,0.296122921026544805300773077761,475,0.533335997816175244601311078441,2,0.448894356191158361291115852509
12,12_0,COMPLETED,Sobol,0.297830157506333348393923188269,512,0.396652586106211013650124641572,4,0.599712880328297615051269531250
13,13_0,COMPLETED,Sobol,0.285769357858794981197547713236,142,0.115800998918712150231868918127,4,0.742901873216033070690400563763
14,14_0,COMPLETED,Sobol,0.294966405991849311973851399671,967,0.668990551866590954510627398122,3,0.707836289703846155418887065025
15,15_0,COMPLETED,Sobol,0.293479458090098010991653154633,220,0.559539464954286969167185361584,4,0.473165405727922983025734993134
16,16_0,COMPLETED,Sobol,0.298986672541028730698542403843,907,0.972348522208630994256850499369,2,0.336468357220292113574089398753
17,17_0,COMPLETED,Sobol,0.295462055292433078967917481350,718,0.660013105254620313644409179688,3,0.672342156805098123406594368134
18,18_0,COMPLETED,Sobol,0.300748981165326556563854865090,632,0.320575515180826164929328569997,4,0.320075014792382761541489344381
19,19_0,COMPLETED,Sobol,0.293314241656570051652863639902,204,0.419829713460058040475075813447,3,0.637583696842193736742387955019
20,20_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.178817477458164175718735577902,4,0.612318582597064509087658734643
21,21_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.166096812229695856011346677406,4,0.800000000000000044408920985006
22,22_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.384949810979415718570351145900,3,0.346852249515900434850834699319
23,23_0,COMPLETED,BoTorch,0.285824430003304374992012526491,122,0.100000000000000005551115123126,4,0.570140123873906334850403254677
24,24_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.625955711458969332738888624590,2,0.318947038178190145352175477456
25,25_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.270486862757708834692493837792,4,0.658530852418251155810935415502
26,26_0,COMPLETED,BoTorch,0.286154862870360182647289093438,100,0.100000000000000005551115123126,3,0.693723366458314338878210492112
27,27_0,COMPLETED,BoTorch,0.290064985130521035827655396133,143,0.100000000000000005551115123126,4,0.800000000000000044408920985006
28,28_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.579674771598672200489943406865,3,0.200000000000000011102230246252
29,29_0,COMPLETED,BoTorch,0.288467892939751058278829987103,100,0.100000000000000005551115123126,4,0.684444219083301330641688764445
30,30_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.285605756310100822314979041039,3,0.543310363468470391978826228296
31,31_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.183098479853907675218849249177,4,0.510293711603962640843690223846
32,32_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.791020412958522545210371390567,3,0.200000000000000011102230246252
33,33_0,COMPLETED,BoTorch,0.287641810772111483629487338476,148,0.100000000000000005551115123126,4,0.630613912475584736938571950304
34,34_0,COMPLETED,BoTorch,0.284998347835664689320367415348,100,0.209669280375856165177239631703,4,0.336619098613069600567371253419
35,35_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.354009725358397697725365560473,4,0.200000000000000011102230246252
36,36_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.860091372833587608504046784219,2,0.411238472239769281557641988911
37,37_0,COMPLETED,BoTorch,0.287476594338583524290697823744,100,0.100000000000000005551115123126,4,0.480384007833607129533959323453
38,38_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.662137609176870700622430376825,3,0.246987728661824501585897451150
39,39_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.233335951135688307589433065914,3,0.304177652883512728010373393772
40,40_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.422841435041076407763682709628,2,0.200000000000000011102230246252
41,41_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.909710362196232136255957811954,2,0.200000000000000011102230246252
42,42_0,COMPLETED,BoTorch,0.286980945037999757296631742065,100,0.100000000000000005551115123126,2,0.200000000000000011102230246252
43,43_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.541617983654499979273566623306,3,0.409797974348826921087152186374
44,44_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.269760893808887347589120508928,2,0.346548974969344891761124927143
45,45_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.639194822828227038336024179443,2,0.200000000000000011102230246252
46,46_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.427933794673768019833914877381,2,0.438816486938387706473463367729
47,47_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.521871732680072253351966082846,2,0.200000000000000011102230246252
48,48_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.324347418889465166635943660367
49,49_0,COMPLETED,BoTorch,0.286925872893490474524469391326,100,0.100000000000000005551115123126,4,0.200000000000000011102230246252
50,50_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.100000000000000005551115123126,3,0.200000000000000011102230246252
51,51_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.427573606081281254454040663404,4,0.200000000000000011102230246252
52,52_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.291015047525964298813505592989,4,0.358822731145480855463603120370
53,53_0,COMPLETED,BoTorch,0.287421522194074241518535473006,100,0.100000000000000005551115123126,3,0.388374443291003790257320815726
54,54_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,4,0.200000000000000011102230246252
55,55_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.358173282486457189577322424157,3,0.200000000000000011102230246252
56,56_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,3,0.200000000000000011102230246252
57,57_0,COMPLETED,BoTorch,0.284943275691155406548205064610,142,1.000000000000000000000000000000,2,0.200000000000000011102230246252
58,58_0,COMPLETED,BoTorch,0.288963542240334825272896068782,123,0.812338561718944696110611403128,2,0.200000000000000011102230246252
59,59_0,COMPLETED,BoTorch,0.289404119396409309494799799722,131,1.000000000000000000000000000000,3,0.200000000000000011102230246252
60,60_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.284409846148573286495064849078,2,0.680337460491085677105616014160
61,61_0,COMPLETED,BoTorch,0.280372287696882938057285628020,136,0.195518013588302175254085568668,3,0.200000000000000011102230246252
62,62_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.320164226433103493718590470962,4,0.423908990863908097246337547404
63,63_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.912690057016043665427673658996,2,0.200000000000000011102230246252
64,64_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,4,0.382552280461466942540482705226
65,65_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.397955310164891606916626187740,4,0.800000000000000044408920985006
66,66_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.828593850442731483241232126602,2,0.200000000000000011102230246252
67,67_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.200000000000000011102230246252
68,68_0,COMPLETED,BoTorch,0.284392554246062356781976632192,133,0.993096025468156784477002929634,2,0.200000000000000011102230246252
69,69_0,COMPLETED,BoTorch,0.284337482101553074009814281453,141,0.179897476667295441732363769916,2,0.200000000000000011102230246252
70,70_0,COMPLETED,BoTorch,0.289238902962881350156010284991,138,0.100000000000000005551115123126,4,0.200000000000000011102230246252
71,71_0,COMPLETED,BoTorch,0.286209935014869465419451444177,144,0.356366974601144081979953170958,3,0.200000000000000011102230246252
72,72_0,COMPLETED,BoTorch,0.287641810772111483629487338476,134,0.100000000000000005551115123126,2,0.200000000000000011102230246252
73,73_0,COMPLETED,BoTorch,0.286815728604471908980144689849,142,0.100000000000000005551115123126,3,0.200000000000000011102230246252
74,74_0,COMPLETED,BoTorch,0.290009912986011642033190582879,150,0.410769052352913832670822102955,2,0.200000000000000011102230246252
75,75_0,COMPLETED,BoTorch,0.290450490142086126255094313819,132,0.100000000000000005551115123126,3,0.200000000000000011102230246252
76,76_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.297771436663977373537903758915,4,0.200000000000000011102230246252
77,77_0,COMPLETED,BoTorch,0.288853397951316259728571367305,144,0.100000000000000005551115123126,2,0.200000000000000011102230246252
78,78_0,COMPLETED,BoTorch,0.282960678488820338571940737893,135,0.100000000000000005551115123126,3,0.293579571476167477950269812936
79,79_0,COMPLETED,BoTorch,0.283566472078422782132633983565,141,0.352268393914146660250708009698,2,0.200000000000000011102230246252
80,80_0,COMPLETED,BoTorch,0.289349047251900026722637448984,138,0.110783829879258777229011911913,2,0.200000000000000011102230246252
81,81_0,COMPLETED,BoTorch,0.289128758673862784611685583513,138,0.344641565714975728340618843504,3,0.200000000000000011102230246252
82,82_0,COMPLETED,BoTorch,0.288633109373279017617619501834,128,0.104782806984311388509567564142,3,0.200000000000000011102230246252
83,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
84,84_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.398383198156441409309991286136
85,85_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.765357555447992443653504324175,4,0.200000000000000011102230246252
86,86_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.564967991994544727063498612551
87,87_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.691377220392203617471693632979,4,0.385564421944849078371930772846
88,88_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.349197712726325493193257898383,3,0.744925537295982431729157724476
89,89_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.313939683012984360743757861201
90,90_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.739387922178985190768685242801
91,91_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.520556345756132299307239463815,4,0.625751177418093051940672921774
92,92_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.899242156480864696099786215200,4,0.200000000000000011102230246252
93,93_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.281060179359338468962903334614,3,0.200000000000000011102230246252
94,94_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.100000000000000005551115123126,2,0.800000000000000044408920985006
95,95_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.800000000000000044408920985006
96,96_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.367814266755175922618548156606,4,0.602559771548226041915086170775
97,97_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.785260500601631550310344209720
98,98_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.516999050797131820544905167480
99,99_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.390508661296323333900204488600
100,100_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.580252612203764339682265926967,2,0.800000000000000044408920985006
101,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
102,102_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.432247715942113308607019916963,3,0.519262095959112324194961729518
103,103_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.465262734069874728248805695330,2,0.622119548210290362888486015436
104,104_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.317161112471009198898741487938,2,0.579081237761454481471901090117
105,105_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.307502294693420386018090084690,3,0.417798898481892444500829242315
106,106_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.125425087096521364893320082956,2,0.800000000000000044408920985006
107,107_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.653496242017264639123652614217,4,0.200000000000000011102230246252
108,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
109,109_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.857098854977714541547584303771,3,0.372992394291094209179959761968
110,110_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.623484463904158792146859013883
111,111_0,COMPLETED,BoTorch,0.296508426038109895728211995447,287,1.000000000000000000000000000000,2,0.200000000000000011102230246252
112,112_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.788746489764244218534372521390,4,0.800000000000000044408920985006
113,113_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.646774774932152185513700715092,2,0.483402304101228252886102154662
114,114_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.346851953112389921329850039911,2,0.800000000000000044408920985006
115,115_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.800000000000000044408920985006
116,116_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.233383952882170481180068577487,2,0.531567435485934103311933540681
117,117_0,COMPLETED,BoTorch,0.287421522194074241518535473006,343,1.000000000000000000000000000000,2,0.200000000000000011102230246252
118,118_0,COMPLETED,BoTorch,0.291111355876197852587949910230,336,0.828081991070277134703303545393,2,0.200000000000000011102230246252
119,119_0,COMPLETED,BoTorch,0.300253331864742789569788783410,380,0.474033850860019478901108413993,2,0.200000000000000011102230246252
120,120_0,COMPLETED,BoTorch,0.292598303777949153570148155268,356,0.831229460367008288201873256185,2,0.204300670342650647626214777119
121,121_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.343643042985201430106201314629,3,0.800000000000000044408920985006
122,122_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.814039802987516769583464792959,4,0.581742537653108238160371001868
123,123_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.508178103698920025088625607168,4,0.434834805374892496843131084461
124,124_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.619112990334443225037830416113,4,0.800000000000000044408920985006
125,125_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.759275514527912087636707383353,2,0.800000000000000044408920985006
126,126_0,COMPLETED,BoTorch,0.289459191540918592266962150461,295,1.000000000000000000000000000000,2,0.247024782033900408562132611223
127,127_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.767062041687756490659921837505,4,0.800000000000000044408920985006
128,128_0,COMPLETED,BoTorch,0.283456327789404105566006819572,107,0.979066738859888596735459032061,3,0.226162409380527368307767233091
129,129_0,COMPLETED,BoTorch,0.290064985130521035827655396133,109,1.000000000000000000000000000000,3,0.200000000000000011102230246252
130,130_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,1.000000000000000000000000000000,2,0.576483043668023942274203363922
131,131_0,COMPLETED,BoTorch,0.280096926974336413174171411811,108,1.000000000000000000000000000000,4,0.200000000000000011102230246252
132,132_0,RUNNING,BoTorch,,108,0.827926161482887001952235550561,3,0.200000000000000011102230246252
133,133_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,2,0.200000000000000011102230246252
134,134_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,1.000000000000000000000000000000,4,0.586248925548114474537442220026
135,135_0,COMPLETED,BoTorch,0.283456327789404105566006819572,107,0.819430296144358805143781410152,3,0.526801248954669509849679798208
136,136_0,COMPLETED,BoTorch,0.289624407974446551605751665193,109,0.853667023177145045664815370401,3,0.378182157321190315357739564206
137,137_0,COMPLETED,BoTorch,0.281914307743143521811646223796,107,1.000000000000000000000000000000,4,0.799877619134160333658201125218
138,138_0,COMPLETED,BoTorch,0.289238902962881350156010284991,109,1.000000000000000000000000000000,4,0.200000000000000011102230246252
139,139_0,COMPLETED,BoTorch,0.284007049234497155332235251990,107,0.999968726225602622115218309773,2,0.200000000000000011102230246252
140,140_0,COMPLETED,BoTorch,0.283896904945478589787910550513,107,1.000000000000000000000000000000,4,0.200000000000000011102230246252
141,141_0,COMPLETED,BoTorch,0.292267870910893234892569125805,431,1.000000000000000000000000000000,2,0.800000000000000044408920985006
142,142_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.510864858331890769882477343344
143,143_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.569990745518046293405234337115
144,144_0,COMPLETED,BoTorch,0.297114219627712339288905241119,405,1.000000000000000000000000000000,4,0.800000000000000044408920985006
145,145_0,COMPLETED,BoTorch,0.294360612402246979435460616514,457,1.000000000000000000000000000000,3,0.800000000000000044408920985006
146,146_0,COMPLETED,BoTorch,0.289844696552483793716703530663,470,0.876927057534962917095811008039,2,0.800000000000000044408920985006
147,147_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.609179155357959523087174602551
148,148_0,COMPLETED,BoTorch,0.293864963101663212441394534835,418,0.866425851412028547038346459885,4,0.461375642329410640130049614527
149,149_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.327052477705765953785999045067
150,150_0,COMPLETED,BoTorch,0.295351911003414513423592779873,530,1.000000000000000000000000000000,2,0.800000000000000044408920985006
151,151_0,COMPLETED,BoTorch,0.295737416014979603851031697559,437,0.891500859174904625170654526300,3,0.599380105424832887450747875846
152,152_0,COMPLETED,BoTorch,0.299757682564159022575722701731,408,0.956882463491530721455546881771,3,0.739967263343734638070259279630
153,153_0,COMPLETED,BoTorch,0.293204097367551486108538938424,460,1.000000000000000000000000000000,2,0.800000000000000044408920985006
154,154_0,COMPLETED,BoTorch,0.300418548298270748908578298142,486,0.794464014810841057112611451885,3,0.800000000000000044408920985006
155,155_0,COMPLETED,BoTorch,0.293975107390681777985719236312,456,0.956895177724336298830110081326,3,0.703049755182721947122104211303
156,156_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.351694496799406119968978146062,2,0.200000000000000011102230246252
157,157_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.332249148327572685346353864588,2,0.800000000000000044408920985006
158,158_0,COMPLETED,BoTorch,0.287146161471527716635421256797,100,0.100000000000000005551115123126,2,0.467968542994602276774429583384
159,159_0,COMPLETED,BoTorch,0.295517127436942361740079832089,388,1.000000000000000000000000000000,4,0.200000000000000011102230246252
160,160_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.463222914027314525142742240860,2,0.800000000000000044408920985006
161,161_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.243029145210275188127013734629,2,0.457928619495715505394173305831
162,162_0,COMPLETED,BoTorch,0.288853397951316259728571367305,122,1.000000000000000000000000000000,3,0.630980500979821279372572462307
163,163_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.489519423569913791904895106200
164,164_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.285825988507109385317050964659
165,165_0,COMPLETED,BoTorch,0.289734552263465117150076366670,126,1.000000000000000000000000000000,4,0.441763672274622654079223593726
166,166_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.478360508781016213752934618242
167,167_0,RUNNING,BoTorch,,357,0.875385912798097742815173205599,4,0.226601519216319768901257702964
168,168_0,COMPLETED,BoTorch,0.296618570327128572294839159440,320,1.000000000000000000000000000000,4,0.800000000000000044408920985006
169,169_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.321729756357146678968916830854,3,0.683755908700369063879520581395
170,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
171,171_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.386537347469540937261456292617,3,0.452237361452681341233983403072
172,172_0,RUNNING,BoTorch,,100,0.442851076644879659838238694647,4,0.800000000000000044408920985006
173,173_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.749131453576931782833980832947,3,0.800000000000000044408920985006
174,174_0,COMPLETED,BoTorch,0.286154862870360182647289093438,100,0.100000000000000005551115123126,3,0.664759815216902749668292926799
175,101_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,4,0.800000000000000044408920985006
176,176_0,COMPLETED,BoTorch,0.288357748650732492734505285625,146,0.913304063965983736750331445364,4,0.200000000000000011102230246252
177,177_0,COMPLETED,BoTorch,0.303117083379226825989860572008,1000,0.100000000000000005551115123126,2,0.800000000000000044408920985006
178,178_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.819078586996064061942490752699,3,0.692991140352794854351259346004
179,179_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.680629625518879510792658038554
180,180_0,COMPLETED,BoTorch,0.297114219627712339288905241119,973,0.305107133977624012111107276723,2,0.432554944918137618259379451047
181,181_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.534444286896483711224448143184,3,0.656934345072449410452009033179
182,182_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.672670510253995712623975578026,3,0.800000000000000044408920985006
183,183_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.638551236345445638598050663859,4,0.200000000000000011102230246252
184,184_0,COMPLETED,BoTorch,0.290230201564048884144142448349,306,0.715467724796295279077185114147,4,0.216068289981710598413044976951
185,185_0,RUNNING,BoTorch,,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
186,186_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.586083897691065947022082127660,4,0.200000000000000011102230246252
187,187_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.510475990023492864899878895812,3,0.800000000000000044408920985006
188,188_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.795401824410743141235968778346,4,0.534324789819474177399172276637
189,189_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.339778087826879238164679009060,2,0.731206601191827898489350445743
190,190_0,COMPLETED,BoTorch,0.287476594338583524290697823744,100,0.100000000000000005551115123126,4,0.384868761607754572562356543131
191,191_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.411990047714558271785278975585,3,0.200000000000000011102230246252
192,192_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.494244745014250086434515196743,4,0.800000000000000044408920985006
193,193_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.492121881994626031442408020666,2,0.800000000000000044408920985006
194,194_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.539176992910849639528692023305,3,0.570562078682359752335173652682
195,195_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.415548916567664350374400328292,3,0.629518865148736117554051361367
196,196_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.375181757746088417881935583864,3,0.577394412181205685108409397799
197,197_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.604616181213158521146056045836,2,0.800000000000000044408920985006
198,185_0,COMPLETED,BoTorch,0.288357748650732492734505285625,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
199,199_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.537220651300486240486975475505,2,0.474532449837979597440096313221
200,200_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.588490529623626468236352593522,2,0.200000000000000011102230246252
201,201_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.100000000000000005551115123126,2,0.737202112089112127080170466797
202,202_0,COMPLETED,BoTorch,0.285604141425267132881060661020,100,0.140746335796367305626120014495,3,0.560868113737998830181652465399
203,203_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.487523414397231569239465898136,3,0.200000000000000011102230246252
204,204_0,COMPLETED,BoTorch,0.303282299812754674306347624224,1000,1.000000000000000000000000000000,4,0.200000000000000011102230246252
205,205_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.426118778389122376815123516280,4,0.692454935790626446845408281661
206,206_0,COMPLETED,BoTorch,0.284888203546646123776042713871,100,0.292291847043283425922055585033,4,0.200000000000000011102230246252
207,207_0,COMPLETED,BoTorch,0.283896904945478589787910550513,136,0.179146558813183642833166686614,2,0.514741080584647847651069696440
208,208_0,COMPLETED,BoTorch,0.284117193523515831898862415983,136,0.243861686375428515782814997692,4,0.414171844315302017935920275704
209,209_0,COMPLETED,BoTorch,0.284557770679590316120766146923,136,0.233879209865910042687175973697,2,0.200000000000000011102230246252
210,210_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.271336873343197226837730795523
211,211_0,COMPLETED,BoTorch,0.281473730587069037589742492855,136,0.100000000000000005551115123126,2,0.200000000000000011102230246252
212,212_0,COMPLETED,BoTorch,0.284667914968608881665090848401,142,0.587982981474156396473063068697,2,0.485059096243542320348041130273
213,213_0,COMPLETED,BoTorch,0.283786760656459913221283386520,136,0.100000000000000005551115123126,3,0.544227788723395944359140230517
214,214_0,COMPLETED,BoTorch,0.281969379887652804583808574534,136,0.304483693404447808283919130190,4,0.200000000000000011102230246252
215,215_0,COMPLETED,BoTorch,0.285549069280757739086595847766,136,0.247889120401724505349250193831,2,0.478325259102194366711557904637
216,216_0,COMPLETED,BoTorch,0.284117193523515831898862415983,136,0.295928000250786249480938749912,2,0.200000000000000011102230246252
217,217_0,COMPLETED,BoTorch,0.285769357858794981197547713236,142,0.710146769314897063907210394973,4,0.200000000000000011102230246252
218,218_0,COMPLETED,BoTorch,0.283456327789404105566006819572,136,0.100000000000000005551115123126,4,0.200000000000000011102230246252
219,219_0,COMPLETED,BoTorch,0.283786760656459913221283386520,136,0.100000000000000005551115123126,4,0.262983781682892103770399216955
220,220_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.335391238046071293865679763258,3,0.800000000000000044408920985006
221,221_0,COMPLETED,BoTorch,0.285989646436832223308499578707,136,0.577799123900583633250960247096,3,0.200000000000000011102230246252
222,222_0,COMPLETED,BoTorch,0.283951977089987872560072901251,136,0.693033489466276075852135818423,2,0.200000000000000011102230246252
223,223_0,COMPLETED,BoTorch,0.283786760656459913221283386520,144,0.755528120360757560192155324330,2,0.200000000000000011102230246252
224,224_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.434987241136139290986761807289,4,0.411229533725951279521382275561
225,225_0,COMPLETED,BoTorch,0.287862099350148725740439203946,137,0.801053149092940453002142930927,2,0.200000000000000011102230246252
226,226_0,RUNNING,BoTorch,,136,0.529006030573744179257289488305,3,0.200000000000000011102230246252
227,227_0,COMPLETED,BoTorch,0.285218636413701931431319280819,135,0.956088272559062923861006311199,2,0.200000000000000011102230246252
228,228_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.589865722877299658577499030798,3,0.800000000000000044408920985006
229,229_0,COMPLETED,BoTorch,0.287862099350148725740439203946,137,0.901949690655759250823564343591,4,0.626123362268657057683185485075
230,230_0,COMPLETED,BoTorch,0.284557770679590316120766146923,144,0.814709982695090206838983704074,3,0.662762920886289630395538097218
231,231_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,0.390367101025506646472251759405,2,0.800000000000000044408920985006
232,232_0,COMPLETED,BoTorch,0.287751955061130049173812039953,143,0.809467463388898855747299876384,2,0.567620844966029380884720012546
233,233_0,COMPLETED,BoTorch,0.287751955061130049173812039953,143,0.823817437307699962367735224689,2,0.526810516369289105753637159069
234,234_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.373997288752769829756061881199,2,0.691675549147858115262010869628
235,235_0,COMPLETED,BoTorch,0.288853397951316259728571367305,122,0.478995137977272000817663410999,4,0.800000000000000044408920985006
236,236_0,COMPLETED,BoTorch,0.288357748650732492734505285625,122,0.430292392699505366060463984468,4,0.200000000000000011102230246252
237,237_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.301729491111216829857255561365,4,0.799999884559750906731778741232
238,238_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.847659141739042443219886990846,2,0.233598064027858792757186279232
239,239_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.642478650847360777120798047690,4,0.284494317658181283814400330812
240,240_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.300318477474537326443737583759,3,0.200000000000000011102230246252
241,241_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.600855604676369670080759988195,4,0.799562720676648686080056904757
242,242_0,COMPLETED,BoTorch,0.291607005176781619582015991909,120,0.241192163602629461305326685761,4,0.460882465827727183516060449620
243,243_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.625201765660296504556470154057,4,0.580000296856238439779929194628
244,244_0,COMPLETED,BoTorch,0.284502698535080922326301333669,121,0.250726469999186518666789424969,4,0.277649162902254498241916280676
245,245_0,COMPLETED,BoTorch,0.290340345853067560710769612342,145,0.584013182605311653716739783704,2,0.654371870580812808881887576717
246,246_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.931056340582933628091666378168,2,0.258868904673573874131164984647
247,247_0,COMPLETED,BoTorch,0.286540367881925273074728011125,148,0.774944932668447661328059439256,4,0.624078542529035829034000926185
248,248_0,COMPLETED,BoTorch,0.283015750633329621344103088632,121,0.604080399945493118352146666439,2,0.331856693430154114921037944441
249,249_0,COMPLETED,BoTorch,0.294140323824209737324508751044,120,0.649205215668988810939765699004,3,0.499514475960462334125367078741
250,250_0,COMPLETED,BoTorch,0.293699746668135253102605020104,120,0.789731800701347741444635630614,4,0.473544897129477559971633127134
251,251_0,COMPLETED,BoTorch,0.290340345853067560710769612342,145,0.402646435366116883791676173132,2,0.629131922277261557141514458635
252,252_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.597872240548220079681129845994,3,0.528081120085982513856492914783
253,253_0,COMPLETED,BoTorch,0.294140323824209737324508751044,120,0.672576584692428536271791017498,3,0.683308627045199901139937992411
254,254_0,COMPLETED,BoTorch,0.286980945037999757296631742065,121,0.133740526382815305694862217933,2,0.509846726668204963672792473517
255,255_0,COMPLETED,BoTorch,0.290340345853067560710769612342,145,0.567677211129134273726037918095,3,0.485937662571209572082153727024
256,256_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.683660008266984253744169564015,3,0.612854411423088718535723273817
257,257_0,COMPLETED,BoTorch,0.292102654477365386576082073589,120,0.864354462490241726158046731143,3,0.200000000000000011102230246252
258,258_0,COMPLETED,BoTorch,0.291937438043837427237292558857,120,0.979275813867895084108283754176,2,0.783680271693660612797316389333
259,259_0,COMPLETED,BoTorch,0.285934574292322940536337227968,121,0.432765090076818181863416157285,2,0.229662417690795345182053210920
260,260_0,COMPLETED,BoTorch,0.293093953078532920564214236947,874,0.531017758697271413659279915009,4,0.629594424925744577947739344381
261,261_0,COMPLETED,BoTorch,0.288688181517788300389781852573,140,0.440898177942369406956402144715,2,0.200000000000000011102230246252
262,262_0,COMPLETED,BoTorch,0.290175129419539601371980097611,140,0.100000000000000005551115123126,3,0.463591401711738626545411534607
263,263_0,COMPLETED,BoTorch,0.281638947020596996928532007587,101,0.262769198773446510664086872566,3,0.200000000000000011102230246252
264,264_0,COMPLETED,BoTorch,0.299592466130631174259235649515,357,0.421760785798012238778653681948,4,0.209365742268198928854872065131
265,265_0,COMPLETED,BoTorch,0.280923009141975987823514060437,101,0.198831362116527743388871840580,2,0.624918515234376137357230618363
266,266_0,COMPLETED,BoTorch,0.287421522194074241518535473006,140,0.607947570853156760151136950299,4,0.728972688554274439454161438334
267,267_0,COMPLETED,BoTorch,0.291386716598744377471064126439,875,0.425346858823126283688509374770,2,0.652642573715641938214560013876
268,268_0,COMPLETED,BoTorch,0.288688181517788300389781852573,140,0.795045066476992245974031447986,2,0.425357450416481763788567604934
269,269_0,COMPLETED,BoTorch,0.289183830818372067383847934252,106,1.000000000000000000000000000000,4,0.800000000000000044408920985006
270,270_0,COMPLETED,BoTorch,0.288908470095825542500733718043,140,0.292513851310422290374901876930,3,0.563148556186527926570306590293
271,271_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.388197174359140473320906039589,2,0.704941586213047477471604906896
272,272_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.329210064200202379147697229200,3,0.800000000000000044408920985006
273,273_0,COMPLETED,BoTorch,0.281308514153541189273255440639,101,0.386362234327053966076448432432,2,0.280173226728228086379601791123
274,274_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,1.000000000000000000000000000000,3,0.485041066690058531030160793307
275,275_0,COMPLETED,BoTorch,0.287476594338583524290697823744,102,0.355057150298940582544560129463,3,0.609140286073968195701411332266
276,276_0,COMPLETED,BoTorch,0.293644674523625970330442669365,342,0.919054343321301314695404016675,4,0.775403220123862091384125960758
277,277_0,COMPLETED,BoTorch,0.281198369864522512706628276646,101,0.974348159053836759824207547354,4,0.202056550336747686724692130156
278,278_0,COMPLETED,BoTorch,0.283125894922348297910730252624,101,1.000000000000000000000000000000,4,0.800000000000000044408920985006
279,279_0,COMPLETED,BoTorch,0.281308514153541189273255440639,101,0.339845532767469649115810170770,2,0.281495683487864356564500667446
280,280_0,COMPLETED,BoTorch,0.286595440026434666869192824379,144,0.997969911446551005695937419659,4,0.200000000000000011102230246252
281,281_0,COMPLETED,BoTorch,0.281638947020596996928532007587,101,0.399121237526260497219254830270,4,0.503515445716535836595539876726
282,282_0,COMPLETED,BoTorch,0.281253442009031795478790627385,101,0.920902670376129939278087022103,2,0.200000000000000011102230246252
283,283_0,COMPLETED,BoTorch,0.286595440026434666869192824379,101,0.100000000000000005551115123126,2,0.200000000000000011102230246252
284,284_0,COMPLETED,BoTorch,0.281198369864522512706628276646,101,0.879623454044248243022252609080,3,0.200000000000000011102230246252
285,285_0,COMPLETED,BoTorch,0.293259169512060768880701289163,468,0.687105029177673021223426985671,4,0.678110727736390472841776499990
286,286_0,COMPLETED,BoTorch,0.285493997136248456314433497027,102,0.893699689683265297013292638439,3,0.200000000000000011102230246252
287,287_0,COMPLETED,BoTorch,0.292983808789514243997587072954,342,0.781674128555828628961421600252,3,0.716514345322489143441657688527
288,288_0,COMPLETED,BoTorch,0.281198369864522512706628276646,101,0.749549943653286976363858684635,4,0.200000000000000011102230246252
289,289_0,COMPLETED,BoTorch,0.282354884899218006033549954736,101,0.811437928530874708066278344631,4,0.512486565828633722219365154160
290,290_0,COMPLETED,BoTorch,0.286870800748981191752307040588,148,0.488089845058091387208776268380,2,0.200000000000000011102230246252
291,291_0,COMPLETED,BoTorch,0.283291111355876146227217304840,101,0.999999987274373114409797835833,2,0.432058622879172682385018333662
292,292_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.828790352078744185781999931351,4,0.271826076944031103099774782095
293,293_0,COMPLETED,BoTorch,0.293920035246172495213556885574,118,0.361378284795742188428846475290,3,0.444047269170259772952391585932
294,294_0,COMPLETED,BoTorch,0.286870800748981191752307040588,102,0.849791530698910890784247840202,2,0.200000000000000011102230246252
295,185_0,COMPLETED,BoTorch,0.288357748650732492734505285625,100,0.100000000000000005551115123126,4,0.800000000000000044408920985006
296,296_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.496381081466202189744763018098,2,0.329259378537570923661803590221
297,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
298,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
299,299_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.905683892203302143286691716639,3,0.800000000000000044408920985006
300,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
301,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
302,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
303,303_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.859194970320032780364272184670,3,0.497546377451658305979265151109
304,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
305,305_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.271721598229404170954381925185
306,306_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.327585732137790464069126983304,2,0.501594037102137457750927751476
307,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
308,308_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.256614649902521307911484882425,2,0.800000000000000044408920985006
309,309_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.680149274786067681297652143257,4,0.800000000000000044408920985006
310,310_0,RUNNING,BoTorch,,789,1.000000000000000000000000000000,2,0.800000000000000044408920985006
311,311_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.326073792383983152021187379432,2,0.427072437793009296314039602294
312,312_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.277690048737403438749993256351,2,0.800000000000000044408920985006
313,313_0,COMPLETED,BoTorch,0.292488159488930477003520991275,789,0.461104125668460484988031566900,2,0.558490567294724238323055942601
314,314_0,RUNNING,BoTorch,,100,0.743565594613862179684815600922,3,0.513406265471625822272017103387
315,315_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.575770066483886622243915098807,4,0.351470789748313361400278154179
316,316_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.552969375833803900022189736774,4,0.800000000000000044408920985006
317,317_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.865293179314119642597802339878,2,0.749320964386531729317653116595
318,318_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.625509408452764703589821237983,4,0.574372690881568503940002301533
319,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
320,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
321,83_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.200000000000000011102230246252
322,322_0,COMPLETED,BoTorch,0.293424385945588728219490803895,132,0.765863142777596483945501404378,4,0.327794091786804631105667340307
323,323_0,COMPLETED,BoTorch,0.289183830818372067383847934252,131,0.448024007378751587538090461749,4,0.314787447358427563415261829505
324,324_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.547187679006647109680727680825,3,0.677814005062404079104965148872
325,325_0,COMPLETED,BoTorch,0.283015750633329621344103088632,121,0.810012754517705380052916552813,2,0.345828844946860280984424207418
326,326_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.295966175793617114475608786961
327,327_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,1.000000000000000000000000000000,3,0.356549088612972631118225308455
328,328_0,COMPLETED,BoTorch,0.290230201564048884144142448349,344,1.000000000000000000000000000000,2,0.200000000000000011102230246252
329,329_0,COMPLETED,BoTorch,0.294140323824209737324508751044,132,0.703031378684943253354333592142,2,0.232597859765709624735308125310
330,330_0,COMPLETED,BoTorch,0.292047582332855992781617260334,132,0.744700515254135830822690422792,3,0.732961588055752510939555577352
331,331_0,COMPLETED,BoTorch,0.290946139442669893249160395499,131,0.559255751765373454453822432697,4,0.496695505042695828468168883774
332,332_0,COMPLETED,BoTorch,0.286209935014869465419451444177,147,0.488486940999286023412651047693,4,0.617909211970323779539171482611
333,333_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.731249794269268171831299696350,3,0.575900539308130787752304513560
334,334_0,RUNNING,BoTorch,,108,0.659525106429545204811404346401,2,0.414965576351546738820275095350
335,335_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.860289181169299532747629655205,3,0.621403974573101036682487574581
336,336_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.686621725531830762179197336081,3,0.321331660625882808979270066629
337,337_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.775680077326724615183195510326,3,0.765095585774862696482045976154
338,338_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.755986415837068159717659909802,3,0.447955303988349928800971611054
339,339_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,1.000000000000000000000000000000,3,0.362316615319076218426630475733
340,340_0,COMPLETED,BoTorch,0.286209935014869465419451444177,147,0.584809881453425828290448862390,3,0.606091771328936435025980244973
341,341_0,COMPLETED,BoTorch,0.300363476153761466136415947403,542,0.567144298527622492400723785977,2,0.640739819164433455078722090548
342,342_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.308573508728000600598306846223
343,343_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.769709317415815719165550490288,2,0.500582777128644407227398005489
344,344_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.661037574141976280728272286069,3,0.746922623477147995529890067701
345,345_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.389721735973463490054768953996
346,346_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.935393795205001321590998486499,3,0.400905635298875240302152178629
347,347_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.945465634849333769196277899027,3,0.362413854238589905332190710396
348,348_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,1.000000000000000000000000000000,4,0.281761424460074294540135042553
349,349_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.923970451027794847931318145129,4,0.359052428107143828395209084192
350,350_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,3,0.355222631954872691828484221332
351,351_0,COMPLETED,BoTorch,0.285328780702720607997946444812,100,0.346507955831221348130810611110,2,0.200000000000000011102230246252
352,352_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.543441167414440373661932426330,4,0.751495950193355577440001979994
353,353_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,1.000000000000000000000000000000,2,0.356605573828709387917967887915
354,354_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.875923336801282759189746229822,3,0.335059727282235986223213330959
355,355_0,COMPLETED,BoTorch,0.297169291772221622061067591858,737,0.942588657760047299305483647913,2,0.800000000000000044408920985006
356,356_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.748011522755665847483896868653,2,0.371803986075063985783373254890
357,357_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.908630430001408639917315213097,4,0.800000000000000044408920985006
358,358_0,COMPLETED,BoTorch,0.301850424055512767118614192441,682,0.848769155119617479954285954591,2,0.311320259711274416325466063427
359,359_0,COMPLETED,BoTorch,0.298821456107500771359752889111,727,0.925147217394715060123644434498,2,0.793218865307228293559660414758
360,360_0,COMPLETED,BoTorch,0.300088115431214941253301731194,817,1.000000000000000000000000000000,2,0.635956305155788492733393013623
361,361_0,COMPLETED,BoTorch,0.297279436061240187605392293335,784,1.000000000000000000000000000000,2,0.632755333053425905731614875549
362,362_0,COMPLETED,BoTorch,0.285218636413701931431319280819,100,0.725682405508452088582771466463,3,0.420817281081158123257068837120
363,363_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.839084716881272507649214276171,3,0.200000000000000011102230246252
364,364_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.775706180071359940875197480636,3,0.287211329509643009672004154709
365,365_0,COMPLETED,BoTorch,0.285604141425267132881060661020,136,1.000000000000000000000000000000,2,0.516948449540925336620489360939
366,366_0,COMPLETED,BoTorch,0.279270844806696727502526300668,108,0.652715812399090822282232693397,3,0.538781503324197164062070442014
367,367_0,COMPLETED,BoTorch,0.289293975107390632928172635729,470,0.129883551836970423387640494184,2,0.671337513224865789496220713772
368,368_0,COMPLETED,BoTorch,0.286209935014869465419451444177,147,0.126312645333904599587171446728,2,0.468133144359302477699458222560
369,369_0,COMPLETED,BoTorch,0.297169291772221622061067591858,790,0.877323461494527778725682765071,4,0.665695876604833536305250163423
370,370_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.634159146482898283814222395449,4,0.614541299737245472201152551861
371,371_0,COMPLETED,BoTorch,0.297554796783786712488506509544,334,0.615588050997825320109768654220,4,0.418224938579480176592539919511
372,372_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.663585615859651589687473460799,3,0.520799148216694596236209235940
373,373_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,1.000000000000000000000000000000,3,0.295901562927273709124875722409
374,374_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.941939112478402007511135707318,3,0.200000000000000011102230246252
375,375_0,COMPLETED,BoTorch,0.289899768696993076488865881402,296,0.606204066115995976460339988989,2,0.691636013888441092412051602878
376,376_0,COMPLETED,BoTorch,0.289789624407974399922238717409,126,0.360802492587028789117198357417,3,0.233371017567772343070942042687
377,377_0,COMPLETED,BoTorch,0.285163564269192648659156930080,149,0.821344353322974507491949225368,4,0.452785104327192988726835665148
378,378_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.572497688632799706098808201205,3,0.721650478231562031439239035535
379,379_0,COMPLETED,BoTorch,0.289789624407974399922238717409,333,0.119745143665789616216343915767,2,0.622858187223498505069585462479
380,380_0,COMPLETED,BoTorch,0.290725850864632651138208530028,297,0.572119152152188892301865053014,4,0.252409513597130374940036290354
381,381_0,COMPLETED,BoTorch,0.288027315783676574056926256162,125,0.149921055700249272746304995962,3,0.723538440277484928842000044824
382,382_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.433167780743998975800934658764,3,0.491160305973127153666979438640
383,383_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.439721476498541985478141214116,3,0.491035130099368999356812537371
384,384_0,COMPLETED,BoTorch,0.292818592355986395681100020738,612,0.319033482205122731478752484691,2,0.674374244734645023058305923769
385,385_0,COMPLETED,BoTorch,0.287862099350148725740439203946,125,0.715803694455670158625082422077,2,0.430496076691956996995713780052
386,386_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.435516437202817408014254851878,3,0.496263347247793917826896858969
387,387_0,COMPLETED,BoTorch,0.291276572309725700904436962446,298,0.688845290453992342705191731511,2,0.364852686838055273454983762349
388,388_0,COMPLETED,BoTorch,0.291441788743253660243226477178,300,0.345101170973003801289991088197,4,0.276905671347681570093612890560
389,389_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.433233479515099939582967181195,3,0.490719193429342059875608583752
390,390_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.430744038700940801156491488655,3,0.526015186082991692551047435700
391,391_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.441533948709074253180517644068,3,0.484229678034154786825382643656
392,392_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518574187077068926576828289399,3,0.497426713456130875634642052319
393,393_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518831807720019355656404513866,3,0.488809106777320367953620916524
394,394_0,COMPLETED,BoTorch,0.285163564269192648659156930080,149,0.527267021201936247898345300200,4,0.582806701664871140700086016295
395,395_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518983633241679398473422679672,3,0.488733273383903488173984897003
396,396_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.519608514125181275566944805178,3,0.478526030483827746753178189465
397,397_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.518069265093745290329252384254,3,0.493814076142934044177934538311
398,398_0,COMPLETED,BoTorch,0.285273708558211214203481631557,141,1.000000000000000000000000000000,4,0.781309911625544861735193080676
399,399_0,COMPLETED,BoTorch,0.285328780702720607997946444812,149,0.374772770132353105765332657029,3,0.544431740861973212375346520275
400,400_0,COMPLETED,BoTorch,0.280262143407864261490658464027,108,0.479734628732583101573538897355,4,0.467246355394784429471144449053
401,401_0,COMPLETED,BoTorch,0.285053419980174083114832228603,152,0.449805442812826972165396455239,3,0.488114355320856729836265230915
402,402_0,COMPLETED,BoTorch,0.283456327789404105566006819572,107,0.394048777039265107902110685245,4,0.695924476738605246595170683577
403,403_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.485219641300954518392529735138,3,0.544576105291492584825618905597
404,404_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.396472581489590525372079810040,2,0.312809872518817477793362513694
405,405_0,COMPLETED,BoTorch,0.290230201564048884144142448349,209,0.340995737811665755589984883045,2,0.517365898852744754421451034432
406,406_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.449626310888963764966774760978,3,0.499956625221870676334390282136
407,407_0,RUNNING,BoTorch,,877,0.688107543483078010204678776063,2,0.374787519187230466766180825289
408,408_0,COMPLETED,BoTorch,0.285108492124683365886994579341,210,0.440532131638879942414632751024,2,0.352489795724450272196293099114
409,409_0,COMPLETED,BoTorch,0.280372287696882938057285628020,152,0.630873357158082792572884045512,2,0.781362083632988868586721764586
410,410_0,COMPLETED,BoTorch,0.287641810772111483629487338476,106,0.313451890262590571190060018125,3,0.319787260025966690868415298610
411,411_0,COMPLETED,BoTorch,0.281638947020596996928532007587,101,0.514849490137525123145678662695,4,0.200000000000000011102230246252
412,412_0,COMPLETED,BoTorch,0.292322943055402628687033939059,293,0.880406827522119961315638647648,4,0.219085635736898098890890196344
413,413_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.367007742885556043077599497337,3,0.307705319881438843498955293398
414,414_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.394715003853915336051727535960,3,0.360792435707713188275391757998
415,415_0,COMPLETED,BoTorch,0.281969379887652804583808574534,152,0.767826228657558451651254927128,3,0.800000000000000044408920985006
416,416_0,COMPLETED,BoTorch,0.282905606344311055799778387154,152,0.741742060254327983948030578176,2,0.386306686767372786661667305452
417,417_0,COMPLETED,BoTorch,0.282575173477255248144501820207,152,0.729632989677174714771012986603,3,0.711460125175196411717593036883
418,418_0,COMPLETED,BoTorch,0.284117193523515831898862415983,151,0.767322295534190623733650227223,2,0.699041715104094407706725178286
419,419_0,COMPLETED,BoTorch,0.288412820795241775506667636364,153,0.932993725062010059723149879574,2,0.295401011174526884062174758583
420,420_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.382481124443396658385552200343,3,0.251364065947366599473866699554
421,421_0,COMPLETED,BoTorch,0.286320079303888141986078608170,153,0.623579682631386189584077328618,2,0.800000000000000044408920985006
422,422_0,COMPLETED,BoTorch,0.279325916951206121296991113923,108,0.325212428692509880612249162368,3,0.295035188018432648071609492035
423,423_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.435902271436965249584716275422,2,0.425140366221556154080474243528
424,424_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.708554734070600211737200879725,3,0.380803442759677257534178806964
425,425_0,COMPLETED,BoTorch,0.301905496200022049890776543180,328,0.906370356001680699264966278861,4,0.279384287589389601613731883845
426,426_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.332146525054038521673760442354,2,0.200000000000000011102230246252
427,427_0,COMPLETED,BoTorch,0.280372287696882938057285628020,152,0.787160366003497591336213190516,2,0.800000000000000044408920985006
428,428_0,COMPLETED,BoTorch,0.298821456107500771359752889111,954,0.603724400141488315441051781818,3,0.567647453770046883647637514514
429,429_0,COMPLETED,BoTorch,0.280372287696882938057285628020,152,0.578157609151459750407298088248,2,0.800000000000000044408920985006
430,430_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.403169554708506350237939841463,2,0.413548320351045717302440607455
431,431_0,COMPLETED,BoTorch,0.293699746668135253102605020104,329,0.903813404754384319694793248345,3,0.420754969547359358728044753661
432,432_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.356121459760811998052076887689,2,0.200000000000000011102230246252
433,433_0,COMPLETED,BoTorch,0.293259169512060768880701289163,530,0.766525665003749856474257740047,2,0.743441145268714409510835139372
434,434_0,COMPLETED,BoTorch,0.292047582332855992781617260334,211,0.443240986517338120265208090132,3,0.774619749946934943451992694463
435,435_0,COMPLETED,BoTorch,0.291331644454235094698901775701,211,0.719116323162267812030279401370,2,0.560447850011962733773884792754
436,436_0,COMPLETED,BoTorch,0.284612842824099598892928497662,151,1.000000000000000000000000000000,4,0.800000000000000044408920985006
437,437_0,COMPLETED,BoTorch,0.291166428020707135360112260969,211,0.231768649091980916132627044135,3,0.739516115603226831254346507194
438,438_0,COMPLETED,BoTorch,0.290009912986011642033190582879,150,0.916428631837954088545927788800,4,0.800000000000000044408920985006
439,439_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.321346124557125800702550577626,2,0.200000000000000011102230246252
440,440_0,COMPLETED,BoTorch,0.291056283731688458793485096976,211,0.708514579968206881233072635951,2,0.252262766976825369980019786453
441,441_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.844083767357400871489403471060,3,0.200000000000000011102230246252
442,442_0,COMPLETED,BoTorch,0.300969269743363798674806730560,344,0.144361651712485050680356835073,2,0.497926161918553156215949684338
443,443_0,COMPLETED,BoTorch,0.291496860887762943015388827916,758,0.524418244049278836627081545885,4,0.409158553079630671334143698914
444,444_0,COMPLETED,BoTorch,0.287146161471527716635421256797,153,0.100000000000000005551115123126,3,0.345837338497483270938204213962
445,445_0,COMPLETED,BoTorch,0.296012776737526128734145913768,758,0.359302075589273184341720934754,3,0.429124784818826709997807711261
446,446_0,COMPLETED,BoTorch,0.295131622425377271312640914402,759,0.889168723673551930630765127717,3,0.226877840579513961882796024838
447,447_0,COMPLETED,BoTorch,0.292157726621874669348244424327,757,0.523109411953387448690477867785,3,0.693174437011801369834529396030
448,448_0,COMPLETED,BoTorch,0.293093953078532920564214236947,308,0.803662785608411422977326310502,2,0.365327645086008900854324110696
449,449_0,COMPLETED,BoTorch,0.292873664500495678453262371477,760,0.686277079930088462766946122429,3,0.768421117980781831491299271875
450,450_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.326292015389153422511014923657,2,0.244667543887781002709402855544
451,451_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.352134948343999720776764661423,2,0.200000026538165620593190396903
452,452_0,COMPLETED,BoTorch,0.291386716598744377471064126439,875,0.398656705080247975025997675402,2,0.595227643842916709360224558623
453,453_0,COMPLETED,BoTorch,0.295847560303998280417658861552,755,0.815226329266540927775963609747,2,0.267567233122452008409197787842
454,454_0,COMPLETED,BoTorch,0.290835995153651327704835694021,880,0.486687031503313272118305121694,2,0.689730807408696078297793974343
455,455_0,COMPLETED,BoTorch,0.296673642471637855067001510179,271,0.583569317878884996630972636922,4,0.475732176803040307522252305716
456,456_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.834799366393449648882096880698,2,0.200000000000000011102230246252
457,457_0,COMPLETED,BoTorch,0.293975107390681777985719236312,877,0.611222125121848924855783025123,3,0.256319443788552647767176040361
458,458_0,COMPLETED,BoTorch,0.282244740610199329466922790743,152,1.000000000000000000000000000000,2,0.414364079632176363077178393723
459,459_0,COMPLETED,BoTorch,0.283731688511950630449121035781,152,0.870118237838723329602430567320,3,0.527610337161072862066646393941
460,460_0,COMPLETED,BoTorch,0.294470756691265544979785317992,763,0.260663989417413977101034561201,2,0.325029009404230739832541985379
461,461_0,COMPLETED,BoTorch,0.299537393986121780464770836261,355,0.912197841685568500125214086438,3,0.393390004324668529633868274686
462,462_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.354636425477899641656165385939,2,0.232785827841728992781966667280
463,463_0,COMPLETED,BoTorch,0.281914307743143521811646223796,121,0.389275850439953163828477045172,3,0.800000000000000044408920985006
464,464_0,COMPLETED,BoTorch,0.286595440026434666869192824379,144,0.648911933567943632894525762822,4,0.200000000000000011102230246252
465,465_0,COMPLETED,BoTorch,0.292267870910893234892569125805,154,0.102175338991044917236195033183,4,0.509280790888575030095353213255
466,466_0,COMPLETED,BoTorch,0.291331644454235094698901775701,428,0.478054919998894489729934775823,2,0.492116795546302221442402924367
467,467_0,COMPLETED,BoTorch,0.299206961119065972809494269313,847,0.222400309239249444059893789927,3,0.738940869085474183464157249546
468,468_0,COMPLETED,BoTorch,0.284557770679590316120766146923,144,0.603902691293907278868857702037,3,0.539495441654894047900370424031
469,469_0,COMPLETED,BoTorch,0.284557770679590316120766146923,144,1.000000000000000000000000000000,2,0.400415356764157182034580273466
470,470_0,COMPLETED,BoTorch,0.297059147483202945494440427865,430,0.244180932004080086805686278240,2,0.644764901671394885518395767576
471,471_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.372937870391715997619996869616,2,0.301586821174566199754707440661
472,472_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.338647377398691773464634025004,2,0.304824100061134306560717277534
473,473_0,COMPLETED,BoTorch,0.302456217645115099657004975597,791,0.227246866468340164013639537188,4,0.630468744598329133843606086884
474,474_0,COMPLETED,BoTorch,0.290560634431104691799419015297,130,0.141726296058199585647940921262,2,0.200000000000000011102230246252
475,475_0,COMPLETED,BoTorch,0.293259169512060768880701289163,746,0.417473060078918933868408203125,2,0.347999875620007559362534266256
476,476_0,COMPLETED,BoTorch,0.289404119396409309494799799722,130,0.132864686979716539738660685543,2,0.619332605360162657959222087811
477,477_0,COMPLETED,BoTorch,0.286925872893490474524469391326,122,0.582041157328252567459969668562,2,0.800000000000000044408920985006
478,478_0,COMPLETED,BoTorch,0.284117193523515831898862415983,151,0.344533576689018294914035323018,4,0.740354627832054967839781056682
479,479_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.358565917263725109087602049840,2,0.266370752993803583397181000691
480,480_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.694569932735671136114774526504,3,0.369072789103007448119342370774
481,481_0,COMPLETED,BoTorch,0.284557770679590316120766146923,149,0.679778908431254103028607005399,2,0.800000000000000044408920985006
482,482_0,COMPLETED,BoTorch,0.290009912986011642033190582879,150,0.486632225422628295063987025060,3,0.800000000000000044408920985006
483,483_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.360021647530475163989649445284,2,0.270318045873222945196800992562
484,484_0,COMPLETED,BoTorch,0.279050556228659596413876897714,108,0.864081114007930528586598484253,3,0.200000000000000011102230246252
485,485_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.515382434748938522695027586451,2,0.682410669743261366626541075675
486,486_0,COMPLETED,BoTorch,0.282354884899218006033549954736,124,0.520851043524026113828995221411,3,0.348124749911709718830366000475
487,487_0,COMPLETED,BoTorch,0.278940411939640919847249733721,108,0.358753799485322266704656613001,2,0.265565293421060055756299789209
488,488_0,COMPLETED,BoTorch,0.288798325806806865934106554050,109,0.226968616613779677892992481247,4,0.467378042585722019985894348792
489,489_0,COMPLETED,BoTorch,0.281804163454124956267321522319,124,0.330911961083825478802111774712,2,0.200000000000000011102230246252
490,490_0,COMPLETED,BoTorch,0.287531666483092807062860174483,129,0.660334551166536498634229701565,3,0.325002795332200389299970311185
491,491_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.719003199490056976728169502167,4,0.610953444260461497883341053239
492,492_0,COMPLETED,BoTorch,0.280151999118845695946333762549,108,0.836967148725337062309392877069,4,0.302424228819288376524099248854
493,493_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.811915702420504370451226350269,2,0.527759982614542710877003628411
494,494_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.447715848485643386212018413062,4,0.800000000000000044408920985006
495,495_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.480813168011024361092609069601,4,0.409117532515098303314005079301
496,496_0,COMPLETED,BoTorch,0.281914307743143521811646223796,124,0.291095678639053889735777147507,4,0.200000000000000011102230246252
497,497_0,COMPLETED,BoTorch,0.298105518228879873277037404478,304,0.991188185111919195513507929718,3,0.420839485816412506302697238425
498,498_0,COMPLETED,BoTorch,0.283180967066857580682892603363,128,0.329057396417723690973389238934,2,0.800000000000000044408920985006
499,499_0,COMPLETED,BoTorch,0.280867936997466705051351709699,124,0.277815038770226063746804356924,3,0.630641617478971627797079690936
500,500_0,COMPLETED,BoTorch,0.291111355876197852587949910230,127,0.676593951933761816874834948976,3,0.536892264215374837021954590455
501,501_0,RUNNING,BoTorch,,882,0.346373691731111654767971685942,3,0.501674788648996283768610737752
502,502_0,RUNNING,BoTorch,,123,0.247200804469123930351415197038,3,0.306361870109804534934028197313
503,503_0,RUNNING,BoTorch,,123,0.161449547600200998820341169449,3,0.324505336929979126825429602832
504,504_0,RUNNING,BoTorch,,338,0.201950986296839107847489458436,4,0.223865624006641977805642795829
505,505_0,RUNNING,BoTorch,,124,0.316593452324789303986563027138,3,0.488648240362981212125959018522
506,506_0,RUNNING,BoTorch,,127,0.638611631674683932757830007176,3,0.800000000000000044408920985006
507,507_0,RUNNING,BoTorch,,882,0.661067298405356273960364887898,3,0.739490478039810383847907360177
</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
1727287795,475.01953125,34.5
1727287795,475.01953125,34.8
1727287795,475.1953125,35.3
1727287795,475.1953125,30.3
1727287795,475.1953125,27.6
1727287795,475.1953125,34.9
1727287795,475.1953125,46.2
1727287838,483.36328125,36.3
1727287838,483.36328125,33.3
1727287838,483.36328125,37.7
1727287838,483.36328125,32.3
1727287841,483.3671875,37.8
1727287841,483.3671875,28.1
1727287841,483.3671875,39.2
1727287841,483.3671875,44.2
1727287843,483.3671875,37.7
1727287843,483.3671875,40.5
1727287843,483.3671875,36.7
1727287843,483.3671875,44.7
1727287845,483.3671875,37.7
1727287845,483.3671875,30.3
1727287845,483.3671875,39.0
1727287845,483.3671875,30.0
1727287847,483.3671875,37.7
1727287847,483.3671875,41.0
1727287847,483.3671875,34.8
1727287847,483.3671875,46.3
1727287849,483.37109375,37.4
1727287849,483.37109375,43.9
1727287849,483.37109375,33.0
1727287849,483.37109375,43.6
1727287852,483.375,36.8
1727287852,483.375,28.1
1727287852,483.375,37.0
1727287852,483.375,30.0
1727287854,483.375,36.3
1727287854,483.375,44.2
1727287854,483.375,36.5
1727287854,483.375,29.0
1727287856,483.375,36.6
1727287856,483.375,30.8
1727287856,483.375,39.3
1727287856,483.375,36.4
1727287858,483.375,36.7
1727287858,483.375,46.3
1727287858,483.375,36.6
1727287858,483.375,30.0
1727287859,483.375,36.6
1727287859,483.375,29.0
1727287859,483.375,36.6
1727287859,483.375,29.0
1727287861,483.375,36.6
1727287861,483.375,28.1
1727287861,483.375,36.3
1727287861,483.375,33.3
1727287864,483.4609375,35.8
1727287864,483.4609375,29.0
1727287864,483.4609375,35.3
1727287864,483.4609375,33.3
1727287866,483.4609375,35.8
1727287866,483.4609375,45.2
1727287866,483.4609375,36.0
1727287866,483.4609375,29.0
1727287868,483.4609375,35.8
1727287868,483.4609375,42.5
1727287868,483.4609375,35.0
1727287868,483.4609375,31.2
1727287870,483.4609375,35.4
1727287870,483.4609375,43.9
1727287870,483.4609375,34.6
1727287870,483.4609375,28.1
1727287872,483.4609375,35.4
1727287872,483.4609375,27.3
1727287872,483.4609375,38.8
1727287872,483.4609375,30.3
1727287874,483.4609375,35.4
1727287874,483.4609375,28.1
1727287874,483.4609375,35.6
1727287874,483.4609375,46.2
1727287876,483.5234375,35.2
1727287876,483.5234375,28.1
1727287876,483.5234375,35.2
1727287876,483.5234375,41.0
1727287878,483.5234375,35.0
1727287878,483.5234375,28.1
1727287878,483.5234375,34.3
1727287878,483.5234375,46.3
1727287880,483.5234375,35.0
1727287880,483.5234375,28.1
1727287880,483.5234375,35.8
1727287880,483.5234375,44.7
1727287881,483.5234375,35.0
1727287881,483.5234375,43.9
1727287881,483.5234375,34.0
1727287881,483.5234375,29.0
1727287883,483.5234375,35.0
1727287883,483.5234375,39.1
1727287883,483.5234375,34.0
1727287883,483.5234375,29.0
1727287886,483.5234375,34.8
1727287886,483.5234375,30.3
1727287886,483.5234375,37.8
1727287886,483.5234375,30.0
1727287888,483.5234375,34.6
1727287888,483.5234375,32.4
1727287888,483.5234375,33.6
1727287888,483.5234375,42.5
1727287890,483.5234375,34.6
1727287890,483.5234375,26.7
1727287890,483.5234375,34.3
1727287890,483.5234375,32.3
1727287892,483.5234375,34.6
1727287892,483.5234375,45.2
1727287892,483.5234375,33.3
1727287892,483.5234375,29.0
1727287893,483.5234375,34.6
1727287893,483.5234375,28.1
1727287893,483.5234375,38.4
1727287893,483.5234375,31.2
1727287895,483.578125,34.6
1727287895,483.578125,30.3
1727287895,483.578125,33.7
1727287895,483.578125,29.0
1727287897,483.578125,34.6
1727287897,483.578125,42.5
1727287897,483.578125,34.0
1727287897,483.578125,31.3
1727287899,483.578125,34.6
1727287899,483.578125,41.0
1727287899,483.578125,33.7
1727287899,483.578125,27.6
1727287901,483.578125,34.6
1727287901,483.578125,40.0
1727287901,483.578125,33.7
1727287901,483.578125,31.4
1727287903,483.59375,34.6
1727287903,483.59375,28.6
1727287903,483.59375,38.1
1727287903,483.59375,26.7
1727287905,483.59375,34.6
1727287905,483.59375,40.0
1727287905,483.59375,34.0
1727287905,483.59375,29.0
1727287907,483.59375,34.6
1727287907,483.59375,30.3
1727287907,483.59375,37.6
1727287907,483.59375,29.0
1727287908,483.59375,34.6
1727287908,483.59375,28.1
1727287908,483.59375,34.3
1727287908,483.59375,45.0
1727287910,483.59375,34.6
1727287910,483.59375,26.7
1727287910,483.59375,33.7
1727287910,483.59375,34.4
1727287912,483.59375,34.6
1727287912,483.59375,27.3
1727287912,483.59375,37.9
1727287912,483.59375,29.0
1727287914,483.59375,34.6
1727287914,483.59375,30.3
1727287914,483.59375,34.0
1727287914,483.59375,43.6
1727287916,483.59375,34.6
1727287916,483.59375,43.9
1727287916,483.59375,33.3
1727287916,483.59375,27.3
1727287918,483.59375,34.6
1727287918,483.59375,26.5
1727287918,483.59375,34.3
1727287918,483.59375,42.1
1727287920,483.59375,34.6
1727287920,483.59375,28.1
1727287920,483.59375,37.6
1727287920,483.59375,29.0
1727287921,483.59375,34.6
1727287921,483.59375,25.8
1727287921,483.59375,34.2
1727287921,483.59375,43.6
1727287923,483.59375,34.6
1727287923,483.59375,41.0
1727287923,483.59375,32.4
1727287923,483.59375,32.3
1727287925,483.59375,34.6
1727287925,483.59375,30.3
1727287925,483.59375,37.7
1727287925,483.59375,29.0
1727287927,483.59375,34.6
1727287927,483.59375,37.8
1727287927,483.59375,33.3
1727287927,483.59375,37.1
1727287929,483.59375,34.6
1727287929,483.59375,43.9
1727287929,483.59375,32.2
1727287929,483.59375,42.1
1727287931,483.59375,34.6
1727287931,483.59375,28.1
1727287931,483.59375,37.8
1727287931,483.59375,29.0
1727288053,523.59765625,35.7
1727288053,523.59765625,34.3
1727288053,523.59765625,36.8
1727288053,523.59765625,30.0
1727288147,528.171875,35.7
1727288147,528.171875,28.1
1727288147,528.171875,37.8
1727288147,528.171875,29.0
1727288279,529.4609375,35.7
1727288279,529.4609375,45.2
1727288279,529.4609375,35.3
1727288279,529.4609375,35.3
1727288439,532.08984375,33.3
1727288439,532.08984375,43.9
1727288439,532.08984375,35.0
1727288439,532.08984375,45.0
1727288578,543.35546875,35.6
1727288578,543.35546875,25.8
1727288578,543.35546875,38.1
1727288578,543.35546875,31.3
1727288741,543.265625,33.3
1727288741,543.265625,27.3
1727288741,543.265625,36.1
1727288741,543.265625,34.4
1727288943,539.95703125,33.8
1727288943,539.95703125,23.5
1727288943,539.95703125,29.7
1727288943,539.95703125,25.8
1727289126,548.70703125,34.2
1727289126,548.70703125,45.0
1727289126,548.70703125,35.9
1727289126,548.70703125,30.0
1727289413,544.79296875,35.5
1727289413,544.79296875,26.5
1727289413,544.79296875,38.2
1727289413,544.79296875,30.3
1727289600,546.35546875,36.0
1727289600,546.35546875,39.5
1727289600,546.35546875,37.6
1727289600,546.35546875,31.3
1727289819,561.25,36.2
1727289819,561.25,28.1
1727289819,561.25,37.4
1727289819,561.25,37.1
1727290001,550.66015625,30.2
1727290001,550.66015625,24.2
1727290001,550.66015625,34.1
1727290001,550.66015625,29.0
1727290217,574.625,31.3
1727290217,574.625,34.2
1727290217,574.625,28.5
1727290217,574.625,23.3
1727290478,562.56640625,27.6
1727290478,562.56640625,21.9
1727290478,562.56640625,29.2
1727290478,562.56640625,39.0
1727290757,563.359375,28.8
1727290757,563.359375,21.2
1727290757,563.359375,30.9
1727290757,563.359375,21.9
1727291025,565.31640625,27.4
1727291025,565.31640625,22.9
1727291025,565.31640625,29.6
1727291025,565.31640625,21.9
1727291343,569.4609375,25.7
1727291343,569.4609375,20.6
1727291343,569.4609375,29.5
1727291343,569.4609375,21.9
1727291626,571.40625,28.5
1727291626,571.40625,35.7
1727291626,571.40625,28.6
1727291626,571.40625,21.2
1727291910,535.7421875,28.2
1727291910,535.7421875,19.4
1727291910,535.7421875,30.0
1727291910,535.7421875,23.5
1727292238,538.6484375,28.9
1727292238,538.6484375,36.6
1727292238,538.6484375,28.2
1727292238,538.6484375,21.9
1727292589,544.20703125,30.3
1727292589,544.20703125,28.1
1727292589,544.20703125,34.9
1727292589,544.20703125,45.0
1727293004,579.45703125,31.4
1727293004,579.45703125,26.5
1727293004,579.45703125,36.5
1727293004,579.45703125,27.3
1727293323,572.890625,35.1
1727293324,572.890625,26.5
1727293324,572.890625,35.5
1727293324,572.890625,41.0
1727293624,573.60546875,34.9
1727293624,573.60546875,42.9
1727293624,573.60546875,34.9
1727293624,573.60546875,28.1
1727294024,558.42578125,34.1
1727294024,558.42578125,42.5
1727294024,558.42578125,34.9
1727294024,558.42578125,27.6
1727294447,565.19140625,35.0
1727294447,565.19140625,28.1
1727294447,565.19140625,34.9
1727294447,565.19140625,42.1
1727294866,583.12890625,34.5
1727294866,583.12890625,41.5
1727294866,583.12890625,34.9
1727294866,583.12890625,26.7
1727295162,584.64453125,35.1
1727295162,584.64453125,40.0
1727295162,584.64453125,36.5
1727295162,584.64453125,28.1
1727295559,591.546875,34.2
1727295559,591.546875,25.0
1727295559,591.546875,35.8
1727295559,591.546875,28.1
1727296045,591.68359375,35.0
1727296045,591.68359375,41.9
1727296045,591.68359375,34.6
1727296045,591.68359375,32.3
1727296482,597.1875,34.1
1727296482,597.1875,27.3
1727296482,597.1875,35.0
1727296482,597.1875,31.2
1727296989,593.203125,35.0
1727296989,593.203125,42.5
1727296989,593.203125,34.6
1727296989,593.203125,29.0
1727297498,604.953125,34.4
1727297498,604.953125,27.3
1727297498,604.953125,34.2
1727297498,604.953125,41.7
1727297926,560.30078125,35.0
1727297926,560.30078125,42.9
1727297926,560.30078125,34.5
1727297926,560.30078125,31.2
1727298459,609.13671875,34.1
1727298459,609.13671875,29.4
1727298459,609.13671875,34.7
1727298459,609.13671875,42.9
1727299002,608.70703125,34.9
1727299002,608.70703125,28.1
1727299002,608.70703125,34.8
1727299002,608.70703125,40.0
1727299541,462.01171875,33.2
1727299541,462.01171875,35.1
1727299541,462.01171875,35.8
1727299541,462.01171875,29.0
1727300083,484.390625,32.3
1727300083,484.390625,19.4
1727300083,484.390625,29.0
1727300083,484.390625,21.9
1727300663,527.19140625,26.6
1727300663,527.19140625,22.9
1727300663,527.19140625,28.4
1727300663,527.19140625,35.9
1727301459,521.7421875,30.4
1727301459,521.7421875,27.8
1727301459,521.7421875,26.0
1727301459,521.7421875,31.7
1727302086,514.50390625,34.8
1727302086,514.50390625,39.1
1727302095,514.515625,34.6
1727302095,514.515625,28.9
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<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>
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