| n_reference_samples | recent_samples_proportion | threshold | result |
|---|---|---|---|
| 50 | 0.421754 | 0.513596 | 0.249534 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_reference_samples | range | 50 | 500 | int | |
| recent_samples_proport… | range | 0.1 | 1 | float | |
| threshold | range | 0.5 | 0.8 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 497 | 4 | 501 |
trial_index,arm_name,trial_status,generation_method,result,n_reference_samples,recent_samples_proportion,threshold 0,0_0,COMPLETED,Sobol,0.305520984081042001889727544039,444,0.305429597198963176385433371252,0.798646718263626098632812500000 1,1_0,COMPLETED,Sobol,0.293402074288470759988456393330,348,0.508871104195714019091667523753,0.702787497267127081457260828756 2,2_0,COMPLETED,Sobol,0.293585383502170782499263168575,269,0.383724621124565623553337445628,0.778670352511107943804802289378 3,3_0,COMPLETED,Sobol,0.288336951278340580806514026335,241,0.768091368023306131362915039062,0.723576524574309654092019172822 4,4_0,COMPLETED,Sobol,0.300774240231548528612393056392,455,0.855721276439726397100571375631,0.574687444977462269513068804372 5,5_0,COMPLETED,Sobol,0.276834298118668553634336149116,163,0.595524707809090614318847656250,0.504377554263919614108147015941 6,6_0,COMPLETED,Sobol,0.302009165460685014004127424414,500,0.915496782865375280380249023438,0.767477884050458691866936078441 7,7_0,COMPLETED,Sobol,0.275707911239749137166654691100,153,0.229093782790005223715112947502,0.628426977805793263165412554372 8,8_0,COMPLETED,Sobol,0.298725277375783915267959400808,401,0.480900513101369164736809125316,0.544215989019721790853623133444 9,9_0,COMPLETED,Sobol,0.287563917028461180080967096728,265,0.468744014296680666653571734059,0.572022718377411365509033203125 10,10_0,COMPLETED,Sobol,0.297623010130246057336478315847,400,0.509753129351884104458747515309,0.700509583763778254095200281881 11,11_0,COMPLETED,Sobol,0.301151712493970102890727957856,448,0.408616165351122662130478602194,0.646389809250831559594985264994 12,12_0,COMPLETED,Sobol,0.261028702363724107726739021018,70,0.828558709193021103445175867819,0.729435470420867204666137695312 13,13_0,COMPLETED,Sobol,0.299264351181862009454448525503,324,0.597467388212680972081614072522,0.756832037214189790041984906566 14,14_0,COMPLETED,Sobol,0.305048239266763165922213829617,446,0.504960533976554937218850227509,0.778471415862441062927246093750 15,15_0,COMPLETED,Sobol,0.294060540279787785422627166554,347,0.627729649469256356653090733744,0.567370143532752946313735264994 16,16_0,COMPLETED,Sobol,0.284808248914616535252264384326,208,0.940473605412989854812622070312,0.686745359096676111221313476562 17,17_0,COMPLETED,Sobol,0.285338880849011045093277516571,205,0.374514772836119003152077766572,0.668042282387614205774184483744 18,18_0,COMPLETED,Sobol,0.273431017848528745695091402013,157,0.649232530221343084875229578756,0.545012427959591150283813476562 19,19_0,COMPLETED,Sobol,0.296098649300530647465734546131,369,0.723707222659140780862685460306,0.765740038361400365829467773438 20,20_0,COMPLETED,BoTorch,0.252332368547998120433817348385,50,0.723144175337181227547489470453,0.663116372411854060331393156957 21,21_0,COMPLETED,BoTorch,0.254984322238302030250167717895,50,0.652135686940745307182965007087,0.780094687355876725831649309839 22,22_0,COMPLETED,BoTorch,0.252901591895803190546132555028,50,1.000000000000000000000000000000,0.673941589985301181542354242993 23,23_0,COMPLETED,BoTorch,0.259183550410033713973234625882,50,1.000000000000000000000000000000,0.800000000000000044408920985006 24,24_0,COMPLETED,BoTorch,0.251995899662325162360332342359,50,0.508534901607298861492267860740,0.670272325599055696088157674239 25,25_0,COMPLETED,BoTorch,0.252467438494934914849920915003,50,0.824338589032663437983217136207,0.595549766455523044150766054372 26,26_0,COMPLETED,BoTorch,0.251361553304389806484664404707,50,1.000000000000000000000000000000,0.516929498217924332514883190015 27,27_0,COMPLETED,BoTorch,0.253181379643029447201740822493,50,0.343906433180010628891665191986,0.791493297315619903642414101341 28,28_0,COMPLETED,BoTorch,0.253942354076218013148036334314,50,0.604146941036584061990311056434,0.726047030443378926811703877320 29,29_0,COMPLETED,BoTorch,0.256833092136999563415145075851,50,1.000000000000000000000000000000,0.753681629929233709574987187807 30,30_0,COMPLETED,BoTorch,0.260730824891461598902253626875,74,0.890459114159984910230605237302,0.744651553714873792877426694758 31,31_0,COMPLETED,BoTorch,0.256028702363724103285846922518,50,0.824736117650769262610310761374,0.731842666010375952900801621581 32,32_0,COMPLETED,BoTorch,0.251173420164013561084459524864,50,0.100000000000000005551115123126,0.500000000000000000000000000000 33,33_0,COMPLETED,BoTorch,0.260144717800289471121288897848,74,0.772597457566617240587447668076,0.692630479834633216995598559151 34,34_0,COMPLETED,BoTorch,0.251802942595272583048426895402,50,0.510954554067603039513301155239,0.564019180352643090259334712755 35,35_0,COMPLETED,BoTorch,0.252755668113844644828702712402,50,0.857293251850912318978714665718,0.687996066517518212179993497557 36,36_0,COMPLETED,BoTorch,0.252280511336227641905338714423,50,1.000000000000000000000000000000,0.639642688677367621252756180183 37,37_0,COMPLETED,BoTorch,0.268199469368065601670991782157,94,0.700353817611247597696433331294,0.800000000000000044408920985006 38,38_0,COMPLETED,BoTorch,0.256291606367583191250503205083,50,0.895116830691361275995632240665,0.766940468894461324644851174526 39,39_0,COMPLETED,BoTorch,0.251232513265798318435884084465,50,0.900752740361539072644347925234,0.582515833725933962483622963191 40,40_0,COMPLETED,BoTorch,0.258111432706222831967579622869,50,0.814680011608184706517477025045,0.800000000000000044408920985006 41,41_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.579726293092297173537019716605 42,42_0,COMPLETED,BoTorch,0.251508683068017324657716926595,50,0.725604991148563138736449218413,0.500000000000000000000000000000 43,43_0,COMPLETED,BoTorch,0.251449589966232567306292366993,50,0.811883031469977978211716163059,0.544302078522298060647699458059 44,44_0,COMPLETED,BoTorch,0.257468644476604002768738155282,67,0.786115453940889552519877270242,0.668258841296488936833952720917 45,45_0,COMPLETED,BoTorch,0.253704775687409567197505566583,50,0.241624122136533003857294943373,0.730846862635321059542548027821 46,46_0,COMPLETED,BoTorch,0.252662807525325661117676645517,50,0.100000000000000005551115123126,0.580943391493934369051999055955 47,47_0,COMPLETED,BoTorch,0.251301254220935854633012240811,50,0.396884263871063458495314080210,0.500000000000000000000000000000 48,48_0,COMPLETED,BoTorch,0.252456584659913163548594638996,50,0.246922780228676475022808745052,0.584344643926275386647262166662 49,49_0,COMPLETED,BoTorch,0.251554510371442385796569851664,50,1.000000000000000000000000000000,0.500000000000000000000000000000 50,50_0,COMPLETED,BoTorch,0.252524119633381616267797653563,50,0.100000000000000005551115123126,0.664584522849250070031246195867 51,51_0,COMPLETED,BoTorch,0.253333333333333299286493911495,50,0.100000000000000005551115123126,0.800000000000000044408920985006 52,52_0,COMPLETED,BoTorch,0.254797395079594757305585517315,50,0.100000000000000005551115123126,0.718330539296517245340112367558 53,53_0,COMPLETED,BoTorch,0.259088277858176563306358275440,77,1.000000000000000000000000000000,0.500000000000000000000000000000 54,54_0,COMPLETED,BoTorch,0.252498794018330974253672138730,50,0.100000000000000005551115123126,0.622375611990742427792611124460 55,55_0,COMPLETED,BoTorch,0.254119633381572618269217400666,50,0.100000000000000005551115123126,0.663164821450404562419578269328 56,56_0,COMPLETED,BoTorch,0.252036903039073778565182237799,50,0.349595841374436555604177101486,0.683479622673925257814175893145 57,57_0,COMPLETED,BoTorch,0.254219729860106102847794318222,59,0.718467310643101653155895292002,0.500000000000000000000000000000 58,58_0,COMPLETED,BoTorch,0.258960443801254269757805559493,71,0.100000000000000005551115123126,0.500000000000000000000000000000 59,59_0,COMPLETED,BoTorch,0.251410998552822007034990292595,50,0.601896537418541544006700405589,0.500000000000000000000000000000 60,60_0,COMPLETED,BoTorch,0.252711046792088778190077391628,57,0.960628327325242503675895022752,0.528914786128193314951317915984 61,61_0,COMPLETED,BoTorch,0.253137964302942553018738180981,50,0.120820440797913986052947166172,0.606957357168174338468702444516 62,62_0,COMPLETED,BoTorch,0.251995899662325162360332342359,51,0.405560908370096173669594463718,0.500000000000000000000000000000 63,63_0,COMPLETED,BoTorch,0.258034249879401822447277936590,74,0.191538501447182263337509766643,0.575161269058075408366903502611 64,64_0,COMPLETED,BoTorch,0.252039315002411945521032521356,50,0.301786131289731085480809724686,0.500000000000000000000000000000 65,65_0,COMPLETED,BoTorch,0.252074288470815255358559170418,50,0.107462310525573881414551635771,0.556020489456461541877274612489 66,66_0,COMPLETED,BoTorch,0.254000241196333798043838214653,51,0.108128846752074486703421030143,0.596509307403561006566405922058 67,67_0,COMPLETED,BoTorch,0.261086589483839892622540901357,85,0.733578529366938569822309545998,0.500000000000000000000000000000 68,68_0,COMPLETED,BoTorch,0.257269657501206006067206999433,67,0.472944291480063783161824630952,0.500000000000000000000000000000 69,69_0,COMPLETED,BoTorch,0.252262421611191500758764050261,50,1.000000000000000000000000000000,0.548466769281154675752532057231 70,70_0,COMPLETED,BoTorch,0.253197057404727421392465203098,55,0.100000000000000005551115123126,0.714617781309147881430021698179 71,71_0,COMPLETED,BoTorch,0.252035697057404695087257096020,50,0.860472804840584526431257472723,0.500000000000000000000000000000 72,72_0,COMPLETED,BoTorch,0.252221418234442884553914154822,50,0.762411789569095166285706000053,0.500000000000000000000000000000 73,73_0,COMPLETED,BoTorch,0.255148335745296717114172224683,50,0.309413431032257602026902532089,0.792546817859768881753268487955 74,74_0,COMPLETED,BoTorch,0.251699228171731737013772089995,50,0.404559820117709412201634222583,0.671573203373752147804509604612 75,75_0,COMPLETED,BoTorch,0.252273275446213252060090326268,50,0.798005637053934568925228631997,0.549180774244112490656277714152 76,76_0,COMPLETED,BoTorch,0.254142547033285093327492631943,50,0.100000000000000005551115123126,0.728218622364646406275312529033 77,32_0,COMPLETED,BoTorch,0.252320308731307285654565930599,50,0.100000000000000005551115123126,0.500000000000000000000000000000 78,78_0,COMPLETED,BoTorch,0.252141823444283597055459722469,50,0.101032386639650723081373939749,0.581540975063708498460357532167 79,79_0,COMPLETED,BoTorch,0.252191268692715908628088072874,50,0.559330810394753186187699611764,0.583864368201500449906404810463 80,80_0,COMPLETED,BoTorch,0.251655812831644953853071910999,50,0.405907345398553665027918668784,0.500000000000000000000000000000 81,81_0,COMPLETED,BoTorch,0.250520984081041953039914460533,50,0.341235100216555675700647043413,0.528287564882613969174940393714 82,82_0,COMPLETED,BoTorch,0.251714905933429822226798933116,50,0.289059908492218964326525565411,0.500000000000000000000000000000 83,83_0,COMPLETED,BoTorch,0.252173178967679656459210946196,50,0.246903237003261166737644316527,0.584357754855903688273599527747 84,84_0,COMPLETED,BoTorch,0.251652194886637703419296485663,50,0.432225571037495703841102567822,0.534860916562768129445260001376 85,85_0,COMPLETED,BoTorch,0.258045103714423573748604212597,73,0.334149964889375095111745395116,0.500000000000000000000000000000 86,86_0,COMPLETED,BoTorch,0.250323203087313039816308446461,50,0.263618942299450542687822007792,0.500000000000000000000000000000 87,49_0,COMPLETED,BoTorch,0.251450795947901539761915046256,50,1.000000000000000000000000000000,0.500000000000000000000000000000 88,32_0,COMPLETED,BoTorch,0.252268451519536918148389759153,50,0.100000000000000005551115123126,0.500000000000000000000000000000 89,89_0,COMPLETED,BoTorch,0.251442354076218066438741516322,50,1.000000000000000000000000000000,0.553166901500531715818453903921 90,90_0,COMPLETED,BoTorch,0.251508683068017324657716926595,50,0.718566964967315979784245882911,0.553503417233016659082522892277 91,32_0,COMPLETED,BoTorch,0.252720694645441334991176063340,50,0.100000000000000005551115123126,0.500000000000000000000000000000 92,92_0,COMPLETED,BoTorch,0.251601543656536419391045455995,50,0.647157886174812513324638985068,0.583045015936292321256928516959 93,32_0,COMPLETED,BoTorch,0.252020019295706720896532715415,50,0.100000000000000005551115123126,0.500000000000000000000000000000 94,94_0,COMPLETED,BoTorch,0.252368547998070402726966676710,50,0.543615943101882659327372948610,0.524497559153069059512120020372 95,49_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.500000000000000000000000000000 96,49_0,COMPLETED,BoTorch,0.251357935359382556050888979371,50,1.000000000000000000000000000000,0.500000000000000000000000000000 97,49_0,COMPLETED,BoTorch,0.251367583212735112851987651084,50,1.000000000000000000000000000000,0.500000000000000000000000000000 98,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000 99,99_0,COMPLETED,BoTorch,0.251494211287988433944917687768,50,1.000000000000000000000000000000,0.548143530284457614065729558206 100,100_0,COMPLETED,BoTorch,0.251447178002894400350442083436,50,0.512211278341243358980250377499,0.609524129777032963595218006958 101,101_0,COMPLETED,BoTorch,0.251466473709599624974941889377,50,0.528434527503533923997736110323,0.675932928146487865816993689805 102,102_0,COMPLETED,BoTorch,0.251892185238784316325677536952,50,0.506172487329796472188547795668,0.591503236837691126481786341174 103,103_0,COMPLETED,BoTorch,0.251388084901109531976715061319,50,0.386240144451517330992373899790,0.500000000000000000000000000000 104,104_0,COMPLETED,BoTorch,0.251164978292329976738983532414,50,0.363147119901957249332724586566,0.500000000000000000000000000000 105,105_0,COMPLETED,BoTorch,0.250789917993246458394196451991,50,0.370130213921177175784293922334,0.500000000000000000000000000000 106,106_0,COMPLETED,BoTorch,0.252533767486734173068896325276,50,0.915187038391401030956728845922,0.559458837524642760286042175721 107,107_0,COMPLETED,BoTorch,0.251700434153400820491697231773,50,0.310525474986082350437044397040,0.500000000000000000000000000000 108,108_0,COMPLETED,BoTorch,0.251482151471297599165666269982,50,1.000000000000000000000000000000,0.547121163311953595531633709470 109,109_0,COMPLETED,BoTorch,0.251490593342981183511142262432,50,1.000000000000000000000000000000,0.554494648742221429849053038197 110,110_0,COMPLETED,BoTorch,0.251642547033285146618197813950,50,0.351432865117274451094431242382,0.519515478476362813076150359848 111,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000 112,112_0,COMPLETED,BoTorch,0.252185238784370491238462363981,50,0.508781014142472898242886003572,0.670458293735125776535710429016 113,113_0,COMPLETED,BoTorch,0.251397732754462088777813733032,50,1.000000000000000000000000000000,0.547459954415160932050810060900 114,114_0,COMPLETED,BoTorch,0.252689339122045386609727302130,50,0.334499016205316579419815070651,0.500000000000000000000000000000 115,115_0,COMPLETED,BoTorch,0.250705499276410948006343915040,50,0.339272640320934348867609742229,0.500000000000000000000000000000 116,49_0,COMPLETED,BoTorch,0.251374819102749613719538501755,50,1.000000000000000000000000000000,0.500000000000000000000000000000 117,49_0,COMPLETED,BoTorch,0.251330197780993747080913180980,50,1.000000000000000000000000000000,0.500000000000000000000000000000 118,49_0,COMPLETED,BoTorch,0.251575012059816693898994799383,50,1.000000000000000000000000000000,0.500000000000000000000000000000 119,119_0,COMPLETED,BoTorch,0.252331162566329036955892206606,50,0.460946065966412299452770184871,0.605387249222629875156087564392 120,120_0,COMPLETED,BoTorch,0.251947660395561934265629133733,50,0.536165641818900695625416119583,0.621598748946203039089652975235 121,49_0,COMPLETED,BoTorch,0.251603955619874586346895739553,50,1.000000000000000000000000000000,0.500000000000000000000000000000 122,122_0,COMPLETED,BoTorch,0.252422817173178937188993131713,50,0.766886666096983860008151623333,0.593025631460632363101126429683 123,49_0,COMPLETED,BoTorch,0.251302460202604938110937382589,50,1.000000000000000000000000000000,0.500000000000000000000000000000 124,124_0,COMPLETED,BoTorch,0.252244331886155359612189386098,50,0.557835692256274606748434052861,0.619899672605909390910028378130 125,49_0,COMPLETED,BoTorch,0.251388084901109531976715061319,50,1.000000000000000000000000000000,0.500000000000000000000000000000 126,126_0,COMPLETED,BoTorch,0.251324167872648329691287472087,50,0.335456322976191989582162022998,0.500000000000000000000000000000 127,49_0,COMPLETED,BoTorch,0.251453207911239706717765329813,50,1.000000000000000000000000000000,0.500000000000000000000000000000 128,128_0,COMPLETED,BoTorch,0.252630246020260518236000280012,50,0.121172213570191728004843412236,0.587100507360714840565663052985 129,129_0,COMPLETED,BoTorch,0.252430053063193438056543982384,50,0.503593042103052335178858811560,0.627345925627251665979144945595 130,130_0,COMPLETED,BoTorch,0.252793053545586121622079645022,50,0.550399998403406476299437599664,0.697532488795094329780965836107 131,131_0,COMPLETED,BoTorch,0.252310660877954617831164796371,50,0.494585803067729257165296985477,0.619084781955730023561557118228 132,49_0,COMPLETED,BoTorch,0.251338639652677331426389173430,50,1.000000000000000000000000000000,0.500000000000000000000000000000 133,133_0,COMPLETED,BoTorch,0.283087313072841295635839742317,189,0.425342917814850784985480913747,0.611657321732491210397597569681 134,134_0,COMPLETED,BoTorch,0.251868065605402757789477163897,50,0.342714373103922498309259481175,0.500000000000000000000000000000 135,135_0,COMPLETED,BoTorch,0.264916787264833586412748900329,96,0.897528963307195559728768330388,0.697450228229425039927491525305 136,136_0,COMPLETED,BoTorch,0.252842498794018322172405532910,50,0.462910517238185326149846332555,0.614627597504567635411376613774 137,137_0,COMPLETED,BoTorch,0.302928123492522960447104196646,491,0.865237909887539791142785361444,0.783414345822994073742506770941 138,49_0,COMPLETED,BoTorch,0.251359141341051639528814121149,50,1.000000000000000000000000000000,0.500000000000000000000000000000 139,139_0,COMPLETED,BoTorch,0.288209117221418287257961310388,231,0.268733718153089329305771570944,0.760840942244976781161369672191 140,49_0,COMPLETED,BoTorch,0.251377231066087780675388785312,50,1.000000000000000000000000000000,0.500000000000000000000000000000 141,141_0,COMPLETED,BoTorch,0.255704293294741868969310871762,57,0.543619520651145382572622111184,0.697341626743621878503631705826 142,142_0,COMPLETED,BoTorch,0.251690786300048263690598560061,50,1.000000000000000000000000000000,0.521446287426022614397425058996 143,143_0,COMPLETED,BoTorch,0.251382054992764114587089352426,50,1.000000000000000000000000000000,0.521917893307950686576646148751 144,144_0,COMPLETED,BoTorch,0.261052821997105666262939394073,76,0.893324286709682557905409794330,0.540606580259798907839297044120 145,145_0,COMPLETED,BoTorch,0.251304872165943105066787666146,50,0.340601703755768203674136884729,0.500000000000000000000000000000 146,146_0,COMPLETED,BoTorch,0.252582006753497401163599533902,50,0.816425129810199767810274806834,0.607291751826386172474769864493 147,147_0,COMPLETED,BoTorch,0.254169078630004818819543288555,56,0.584123401304225464691910474357,0.717028551053389806568816311483 148,148_0,COMPLETED,BoTorch,0.253681862035697092139230335306,50,0.381543826778766970164724625647,0.680889482027213777115548509755 149,49_0,COMPLETED,BoTorch,0.251646164978292286029670776770,50,1.000000000000000000000000000000,0.500000000000000000000000000000 150,150_0,COMPLETED,BoTorch,0.252026049204052138286158424307,50,0.339693000203483863597142544677,0.500000000000000000000000000000 151,151_0,COMPLETED,BoTorch,0.284767245537867808025112026371,190,0.527237577363848686218261718750,0.717803246807307004928588867188 152,152_0,COMPLETED,BoTorch,0.258572117703810944178144382022,73,0.920934926539291986991031535581,0.547321384955335665800646438583 153,153_0,COMPLETED,BoTorch,0.257685721177038140616843975295,74,0.906013477338800110594263514940,0.542320124778088885619808934280 154,154_0,COMPLETED,BoTorch,0.250962373371924729603676951228,50,0.276452012372551902963380143774,0.660016498504453319462470517465 155,49_0,COMPLETED,BoTorch,0.251435118186203565571190665651,50,1.000000000000000000000000000000,0.500000000000000000000000000000 156,156_0,COMPLETED,BoTorch,0.253064399421128793932211920037,50,0.340028095636878302521211026033,0.500000000000000000000000000000 157,157_0,COMPLETED,BoTorch,0.252709840810419694712152249849,50,0.100000000000000005551115123126,0.767562921840241108029090355558 158,49_0,COMPLETED,BoTorch,0.251314520019295661867886337859,50,1.000000000000000000000000000000,0.500000000000000000000000000000 159,49_0,COMPLETED,BoTorch,0.251314520019295661867886337859,50,1.000000000000000000000000000000,0.500000000000000000000000000000 160,160_0,COMPLETED,BoTorch,0.252604920405209876221874765179,50,0.766570525627019994097111066367,0.605870747656967911787262437429 161,161_0,COMPLETED,BoTorch,0.251097443318861524019780517847,50,0.347129110110642902231603557084,0.500000000000000000000000000000 162,49_0,COMPLETED,BoTorch,0.251315726000964745345811479638,50,1.000000000000000000000000000000,0.500000000000000000000000000000 163,163_0,COMPLETED,BoTorch,0.251587071876507528678246217169,51,0.650654112129341322123821100831,0.633951864513824348001946873410 164,164_0,COMPLETED,BoTorch,0.252083936324167923181960304646,50,0.100000000000000005551115123126,0.560565887599899670412639807182 165,165_0,COMPLETED,BoTorch,0.277680897250361824468711802183,155,0.496894221689799242547280755389,0.552302184659011263789807344438 166,166_0,COMPLETED,BoTorch,0.258174143753014950775082070322,74,0.905304960216311016552026558202,0.538042376024265411871283504297 167,49_0,COMPLETED,BoTorch,0.251449589966232567306292366993,50,1.000000000000000000000000000000,0.500000000000000000000000000000 168,168_0,COMPLETED,BoTorch,0.251383260974433198065014494205,50,0.357425099760597664300121323322,0.500000000000000000000000000000 169,169_0,COMPLETED,BoTorch,0.251075735648818132439430428349,50,0.562644323221510478383322606533,0.638035700168082309247097327898 170,170_0,COMPLETED,BoTorch,0.276395320791123944026423941978,152,0.255092379450798045770198996252,0.517569698113948151174668055319 171,171_0,COMPLETED,BoTorch,0.251357935359382556050888979371,50,0.366717189035585811041073611705,0.500000000000000000000000000000 172,172_0,COMPLETED,BoTorch,0.251871683550410008223252589232,50,0.361340662863280592809189784020,0.500000000000000000000000000000 173,49_0,COMPLETED,BoTorch,0.251445972021225316872516941658,50,1.000000000000000000000000000000,0.500000000000000000000000000000 174,174_0,COMPLETED,BoTorch,0.296993487698986924350208482792,328,0.251555677317082893029720480627,0.701927577704191230090202680003 175,175_0,COMPLETED,BoTorch,0.279448866377231097679612048523,175,0.123433384040146337801147069513,0.541190282904247421669197137817 176,176_0,COMPLETED,BoTorch,0.264456102267245585224486603693,93,0.440465000259538608240461599053,0.530974039488364524252972387330 177,177_0,COMPLETED,BoTorch,0.251515918958031825525267777266,50,0.404368779577207737574440216122,0.500000000000000000000000000000 178,178_0,COMPLETED,BoTorch,0.296158948383984599317386710027,335,0.328728231590496355885022694565,0.541447449674491987359203903907 179,49_0,COMPLETED,BoTorch,0.251547274481427884929019000992,50,1.000000000000000000000000000000,0.500000000000000000000000000000 180,49_0,COMPLETED,BoTorch,0.251328991799324663602988039202,50,1.000000000000000000000000000000,0.500000000000000000000000000000 181,49_0,COMPLETED,BoTorch,0.251386878919440448498789919540,50,1.000000000000000000000000000000,0.500000000000000000000000000000 182,49_0,COMPLETED,BoTorch,0.251413410516160173990840576153,50,1.000000000000000000000000000000,0.500000000000000000000000000000 183,183_0,COMPLETED,BoTorch,0.250560781476121596789141676709,50,0.320680022736107239733627238820,0.521644708558210767890273018565 184,49_0,COMPLETED,BoTorch,0.251391702846116782410490486654,50,1.000000000000000000000000000000,0.500000000000000000000000000000 185,49_0,COMPLETED,BoTorch,0.251369995176073279807837934641,50,1.000000000000000000000000000000,0.500000000000000000000000000000 186,186_0,COMPLETED,BoTorch,0.278564881813796461074161925353,158,0.574378353434689903345145012281,0.556240660920693796853697676852 187,187_0,COMPLETED,BoTorch,0.278480463096960950686309388402,155,0.576519214656641798555369859969,0.563019247069350692314060324861 188,49_0,COMPLETED,BoTorch,0.251345875542691721271637561586,50,1.000000000000000000000000000000,0.500000000000000000000000000000 189,49_0,COMPLETED,BoTorch,0.251360347322720723006739262928,50,1.000000000000000000000000000000,0.500000000000000000000000000000 190,49_0,COMPLETED,BoTorch,0.251460443801254207585316180484,50,1.000000000000000000000000000000,0.500000000000000000000000000000 191,49_0,COMPLETED,BoTorch,0.251348287506029888227487845143,50,1.000000000000000000000000000000,0.500000000000000000000000000000 192,49_0,COMPLETED,BoTorch,0.251351905451037138661263270478,50,1.000000000000000000000000000000,0.500000000000000000000000000000 193,49_0,COMPLETED,BoTorch,0.251348287506029888227487845143,50,1.000000000000000000000000000000,0.500000000000000000000000000000 194,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000 195,49_0,COMPLETED,BoTorch,0.251373613121080530241613359976,50,1.000000000000000000000000000000,0.500000000000000000000000000000 196,196_0,COMPLETED,BoTorch,0.251373613121080530241613359976,50,0.999999999999999888977697537484,0.500000000000000000000000000000 197,197_0,COMPLETED,BoTorch,0.265085624698504607188453974231,95,0.812039849720895268170295366872,0.729513344448059819491447797191 198,198_0,COMPLETED,BoTorch,0.277319102749638224381101281324,150,0.695445684135409747383960166189,0.545450451672552905080237906077 199,199_0,COMPLETED,BoTorch,0.293128316449589920722473834758,369,0.562304641019254702882790297735,0.714826373893722277763629335823 200,200_0,COMPLETED,BoTorch,0.288236854799807096227937108779,278,0.522633547987788937838615765941,0.711401487234979867935180664062 201,201_0,COMPLETED,BoTorch,0.250968403280270146993302660121,50,0.881645372230559609683098187816,0.579951639752835035324096679688 202,49_0,COMPLETED,BoTorch,0.251766763145200189732975104562,50,1.000000000000000000000000000000,0.500000000000000000000000000000 203,49_0,COMPLETED,BoTorch,0.251355523396044389095038695814,50,1.000000000000000000000000000000,0.500000000000000000000000000000 204,204_0,RUNNING,BoTorch,,73,0.608640702348202444760261187184,0.750569714233279228210449218750 205,205_0,COMPLETED,BoTorch,0.259301736613603450720688670117,73,0.729538020677864551544189453125,0.529738754127174638064445844066 206,206_0,COMPLETED,BoTorch,0.255412445730824888556753649027,57,0.875585343111211833466711595975,0.723723907253296383501606214850 207,207_0,COMPLETED,BoTorch,0.265888808490110983839826985786,103,0.500345596391707725381081672822,0.508012700453400634081901898753 208,208_0,COMPLETED,BoTorch,0.294546550892426428625014978024,366,0.378925083018839425896828743134,0.641226308513432763369621625316 209,209_0,COMPLETED,BoTorch,0.252034491075735611609331954241,50,0.313665793690479532962456232781,0.522880117065096849948702129041 210,210_0,COMPLETED,BoTorch,0.252036903039073778565182237799,50,0.313867609092131738535158547165,0.525154920182485551727324946114 211,211_0,COMPLETED,BoTorch,0.268277858176555694669218610215,102,0.533779872395098275994484993134,0.726147207338363021023042165325 212,212_0,COMPLETED,BoTorch,0.281494211287988460590270278772,195,0.144490762334316985571192049065,0.585501269903033971786499023438 213,213_0,COMPLETED,BoTorch,0.252205740472744799340887311700,50,0.719497866090387128146232953441,0.631079064216464757919311523438 214,49_0,COMPLETED,BoTorch,0.251505065123010185246243963775,50,1.000000000000000000000000000000,0.500000000000000000000000000000 215,215_0,COMPLETED,BoTorch,0.257274481427882339978907566547,68,0.677391667863296964569030933490,0.592357463650894366935517609818 216,49_0,COMPLETED,BoTorch,0.251455619874577873673615613370,50,1.000000000000000000000000000000,0.500000000000000000000000000000 217,217_0,COMPLETED,BoTorch,0.253780752532561493239882111084,50,0.834201061827886447552771187475,0.634106695610154647013700923708 218,218_0,COMPLETED,BoTorch,0.256015436565364185028670362954,62,0.885037713684141635894775390625,0.535661337058991215975822797191 219,49_0,RUNNING,BoTorch,,50,1.000000000000000000000000000000,0.500000000000000000000000000000 220,220_0,COMPLETED,BoTorch,0.253400868306801752005696926062,50,0.272383149347841913012757686374,0.726599773571164120333776281768 221,221_0,COMPLETED,BoTorch,0.253016160154365676859811173927,50,0.272175176703347121431875166309,0.726611954605530852013828280178 222,222_0,COMPLETED,BoTorch,0.252144235407621764011310006026,50,0.407428847847306596641203668696,0.634223804858913831061784094345 223,49_0,COMPLETED,BoTorch,0.251345875542691721271637561586,50,1.000000000000000000000000000000,0.500000000000000000000000000000 224,224_0,COMPLETED,BoTorch,0.252238301977809942222563677205,50,0.540634313016997047895983996568,0.642230741774745927941125955840 225,49_0,COMPLETED,BoTorch,0.251344669561022637793712419807,50,1.000000000000000000000000000000,0.500000000000000000000000000000 226,49_0,COMPLETED,BoTorch,0.251355523396044389095038695814,50,1.000000000000000000000000000000,0.500000000000000000000000000000 227,49_0,COMPLETED,BoTorch,0.251532802701398994216219762166,50,1.000000000000000000000000000000,0.500000000000000000000000000000 228,228_0,COMPLETED,BoTorch,0.251292812349252270287536248361,50,0.376597473663913384633872283302,0.621272645944888624036650526250 229,229_0,COMPLETED,BoTorch,0.252919681620839331692707219190,50,0.638449047655448298499436532438,0.640020329041448121643043123186 230,230_0,COMPLETED,BoTorch,0.251958514230583685566955409740,50,0.628657027610797891625793454295,0.637703860573932579391964736715 231,49_0,COMPLETED,BoTorch,0.251331403762662830558838322759,50,1.000000000000000000000000000000,0.500000000000000000000000000000 232,49_0,COMPLETED,BoTorch,0.251454413892908790195690471592,50,1.000000000000000000000000000000,0.500000000000000000000000000000 233,233_0,COMPLETED,BoTorch,0.251626869271587061405170970829,50,1.000000000000000000000000000000,0.522680301652058698635983091663 234,234_0,COMPLETED,BoTorch,0.250554751567776179399515967816,50,0.334516068035660474322412483161,0.523658365062494191022324230289 235,49_0,COMPLETED,BoTorch,0.251320549927641079257512046752,50,1.000000000000000000000000000000,0.500000000000000000000000000000 236,236_0,COMPLETED,BoTorch,0.251729377713458712939598171943,50,0.419435216470962379808895548194,0.500000000000000000000000000000 237,49_0,RUNNING,BoTorch,,50,1.000000000000000000000000000000,0.500000000000000000000000000000 238,49_0,COMPLETED,BoTorch,0.251304872165943105066787666146,50,1.000000000000000000000000000000,0.500000000000000000000000000000 239,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000 240,240_0,COMPLETED,BoTorch,0.250891220453449137473000973841,50,0.426824203149736791651491785160,0.500000000000000000000000000000 241,49_0,COMPLETED,BoTorch,0.251378437047756864153313927090,50,1.000000000000000000000000000000,0.500000000000000000000000000000 242,242_0,COMPLETED,BoTorch,0.251636517124939729228572105058,50,0.435300350969221483410365181044,0.640204128896388224845281911257 243,243_0,COMPLETED,BoTorch,0.251214423540762177289309420303,50,0.388517069086351418683022984624,0.500000000000000000000000000000 244,244_0,COMPLETED,BoTorch,0.252451760733236829636894071882,50,0.323140383945104137097814600565,0.523904298951193947431193009834 245,245_0,COMPLETED,BoTorch,0.250940665701881338023326861730,50,0.321300731777231451946619245064,0.524587547384697372443440599454 246,49_0,COMPLETED,BoTorch,0.251366377231066140396364971821,50,1.000000000000000000000000000000,0.500000000000000000000000000000 247,49_0,COMPLETED,BoTorch,0.251351905451037138661263270478,50,1.000000000000000000000000000000,0.500000000000000000000000000000 248,49_0,COMPLETED,BoTorch,0.251318137964302912301661763195,50,1.000000000000000000000000000000,0.500000000000000000000000000000 249,249_0,COMPLETED,BoTorch,0.252392667631451961263167049765,50,0.357893535285238595378132231417,0.623917419872544121162150076998 250,250_0,COMPLETED,BoTorch,0.278118668596237350598698867543,153,0.430355031007679911247976178856,0.621694231706615973997998025879 251,251_0,COMPLETED,BoTorch,0.251607573564881836780671164888,50,0.383084185083425898810105536541,0.500000000000000000000000000000 252,49_0,COMPLETED,BoTorch,0.251489387361312100033217120654,50,1.000000000000000000000000000000,0.500000000000000000000000000000 253,49_0,COMPLETED,BoTorch,0.251472503617945042364567598270,50,1.000000000000000000000000000000,0.500000000000000000000000000000 254,49_0,COMPLETED,BoTorch,0.251642547033285146618197813950,50,1.000000000000000000000000000000,0.500000000000000000000000000000 255,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000 256,256_0,COMPLETED,BoTorch,0.250779064158224818115172638500,50,0.886064076616317475298956196639,0.588395452912066074802055481996 257,49_0,COMPLETED,BoTorch,0.251540038591413384061468150321,50,1.000000000000000000000000000000,0.500000000000000000000000000000 258,49_0,COMPLETED,BoTorch,0.251436324167872649049115807429,50,1.000000000000000000000000000000,0.500000000000000000000000000000 259,49_0,COMPLETED,BoTorch,0.251374819102749613719538501755,50,1.000000000000000000000000000000,0.500000000000000000000000000000 260,49_0,COMPLETED,BoTorch,0.251391702846116782410490486654,50,1.000000000000000000000000000000,0.500000000000000000000000000000 261,49_0,COMPLETED,BoTorch,0.251350699469368055183338128700,50,1.000000000000000000000000000000,0.500000000000000000000000000000 262,262_0,COMPLETED,BoTorch,0.252297395079594810596290699323,50,0.336735949722954841512034818152,0.523920643391982188141753340460 263,49_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.500000000000000000000000000000 264,264_0,COMPLETED,BoTorch,0.251213217559093093811384278524,50,0.338561593741065869345163719117,0.523138814727089451750430271204 265,265_0,COMPLETED,BoTorch,0.251443560057887149916666658100,50,0.341438401383369727337679933044,0.523115100489205397948921927309 266,49_0,COMPLETED,BoTorch,0.251439942112879899482891232765,50,1.000000000000000000000000000000,0.500000000000000000000000000000 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408,49_0,COMPLETED,BoTorch,0.251511095031355491613567210152,50,1.000000000000000000000000000000,0.500000000000000000000000000000 409,49_0,COMPLETED,BoTorch,0.251466473709599624974941889377,50,1.000000000000000000000000000000,0.500000000000000000000000000000 410,49_0,COMPLETED,BoTorch,0.251477327544621376276268165384,50,1.000000000000000000000000000000,0.500000000000000000000000000000 411,411_0,COMPLETED,BoTorch,0.251514712976362742047342635487,50,0.341099376161345446689665550366,0.524204401133920305788649329770 412,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000 413,49_0,COMPLETED,BoTorch,0.251302460202604938110937382589,50,1.000000000000000000000000000000,0.500000000000000000000000000000 414,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000 415,49_0,COMPLETED,BoTorch,0.251380849011095031109164210648,50,1.000000000000000000000000000000,0.500000000000000000000000000000 416,416_0,COMPLETED,BoTorch,0.250593342981186739670818042214,50,0.322582618416136168715269150198,0.525419156054855562842931249179 417,417_0,COMPLETED,BoTorch,0.251849975880366616642902499734,50,0.316867364915205451669777403367,0.524422635368591127935644635727 418,49_0,COMPLETED,BoTorch,0.251476121562952292798343023605,50,1.000000000000000000000000000000,0.500000000000000000000000000000 419,49_0,COMPLETED,BoTorch,0.251390496864447698932565344876,50,1.000000000000000000000000000000,0.500000000000000000000000000000 420,49_0,COMPLETED,BoTorch,0.251418234442836507902541143267,50,1.000000000000000000000000000000,0.500000000000000000000000000000 421,49_0,COMPLETED,BoTorch,0.251418234442836507902541143267,50,1.000000000000000000000000000000,0.500000000000000000000000000000 422,422_0,COMPLETED,BoTorch,0.251663048721659454720622761670,50,1.000000000000000000000000000000,0.582455746457651524217169480835 423,423_0,COMPLETED,BoTorch,0.254056922334780499461714953213,52,0.739283017293395960933821697836,0.705416982116536916969096182584 424,49_0,COMPLETED,BoTorch,0.251337433671008247948464031651,50,1.000000000000000000000000000000,0.500000000000000000000000000000 425,49_0,COMPLETED,BoTorch,0.251443560057887149916666658100,50,1.000000000000000000000000000000,0.500000000000000000000000000000 426,49_0,COMPLETED,BoTorch,0.251366377231066140396364971821,50,1.000000000000000000000000000000,0.500000000000000000000000000000 427,427_0,COMPLETED,BoTorch,0.252553063193439508715698593733,50,1.000000000000000000000000000000,0.577082589518987942867056517571 428,49_0,COMPLETED,BoTorch,0.251343463579353554315787278028,50,1.000000000000000000000000000000,0.500000000000000000000000000000 429,429_0,COMPLETED,BoTorch,0.252058610709117170145532327297,50,0.317865566343900729506799507362,0.524030044245835657790166806080 430,49_0,COMPLETED,BoTorch,0.251523154848046326392818627937,50,1.000000000000000000000000000000,0.500000000000000000000000000000 431,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000 432,49_0,COMPLETED,BoTorch,0.251546068499758801451093859214,50,1.000000000000000000000000000000,0.500000000000000000000000000000 433,49_0,COMPLETED,BoTorch,0.251326579835986496647137755645,50,1.000000000000000000000000000000,0.500000000000000000000000000000 434,49_0,COMPLETED,BoTorch,0.251777616980221941034301380569,50,1.000000000000000000000000000000,0.500000000000000000000000000000 435,49_0,COMPLETED,BoTorch,0.251672696575012011521721433382,50,1.000000000000000000000000000000,0.500000000000000000000000000000 436,49_0,COMPLETED,BoTorch,0.251388084901109531976715061319,50,1.000000000000000000000000000000,0.500000000000000000000000000000 437,49_0,COMPLETED,BoTorch,0.251395320791123921821963449474,50,1.000000000000000000000000000000,0.500000000000000000000000000000 438,49_0,COMPLETED,BoTorch,0.251474915581283209320417881827,50,1.000000000000000000000000000000,0.500000000000000000000000000000 439,49_0,COMPLETED,BoTorch,0.251409792571152923557065150817,50,1.000000000000000000000000000000,0.500000000000000000000000000000 440,440_0,COMPLETED,BoTorch,0.251799324650265332614651470067,50,1.000000000000000000000000000000,0.585254528204475210628743298003 441,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000 442,49_0,COMPLETED,BoTorch,0.251413410516160173990840576153,50,1.000000000000000000000000000000,0.500000000000000000000000000000 443,49_0,COMPLETED,BoTorch,0.251383260974433198065014494205,50,1.000000000000000000000000000000,0.500000000000000000000000000000 444,444_0,COMPLETED,BoTorch,0.254463338157259966187950794847,52,0.758599611941763063427401903027,0.723681730896888031345781655546 445,49_0,COMPLETED,BoTorch,0.251530390738060827260369478608,50,1.000000000000000000000000000000,0.500000000000000000000000000000 446,49_0,COMPLETED,BoTorch,0.251530390738060827260369478608,50,1.000000000000000000000000000000,0.500000000000000000000000000000 447,447_0,COMPLETED,BoTorch,0.257676073323685472793442841066,56,0.769516202509516911156595142529,0.764395443931254248148832175502 448,49_0,COMPLETED,BoTorch,0.251408586589483840079140009038,50,1.000000000000000000000000000000,0.500000000000000000000000000000 449,449_0,COMPLETED,BoTorch,0.249534491075735664900037136249,50,0.421753624267876214837258430634,0.513595570158213332589980382181 450,450_0,COMPLETED,BoTorch,0.250760974433188565946295511822,50,0.324184863024038927292735934316,0.524196617966436795299500772671 451,49_0,COMPLETED,BoTorch,0.251511095031355491613567210152,50,1.000000000000000000000000000000,0.500000000000000000000000000000 452,49_0,COMPLETED,BoTorch,0.251342257597684470837862136250,50,1.000000000000000000000000000000,0.500000000000000000000000000000 453,49_0,COMPLETED,BoTorch,0.251314520019295661867886337859,50,1.000000000000000000000000000000,0.500000000000000000000000000000 454,49_0,COMPLETED,BoTorch,0.251456825856246957151540755149,50,1.000000000000000000000000000000,0.500000000000000000000000000000 455,49_0,COMPLETED,BoTorch,0.251424264351181814269864389644,50,1.000000000000000000000000000000,0.500000000000000000000000000000 456,456_0,COMPLETED,BoTorch,0.276675108538350200682032209443,158,0.648730381452036430900420782564,0.532822475961984731718246166565 457,49_0,COMPLETED,BoTorch,0.251365171249397056918439830042,50,1.000000000000000000000000000000,0.500000000000000000000000000000 458,49_0,COMPLETED,BoTorch,0.251518330921369992481118060823,50,1.000000000000000000000000000000,0.500000000000000000000000000000 459,459_0,COMPLETED,BoTorch,0.252114085865894788085483924078,50,0.252317400738965524897849945773,0.543508429767719181135987582820 460,460_0,COMPLETED,BoTorch,0.252449348769898662681043788325,50,0.361456154274081953303721093107,0.641854147791773410602900185040 461,461_0,COMPLETED,BoTorch,0.252456584659913163548594638996,52,0.202426617871969943829313365313,0.550424197316169760973991742503 462,49_0,COMPLETED,BoTorch,0.251458031837916040629465896927,50,1.000000000000000000000000000000,0.500000000000000000000000000000 463,49_0,COMPLETED,BoTorch,0.251476121562952292798343023605,50,1.000000000000000000000000000000,0.500000000000000000000000000000 464,49_0,COMPLETED,BoTorch,0.251414616497829257468765717931,50,1.000000000000000000000000000000,0.500000000000000000000000000000 465,465_0,COMPLETED,BoTorch,0.252458996623251330504444922553,51,0.134935280401202756239698032914,0.660540461167679060494606346765 466,466_0,COMPLETED,BoTorch,0.253719247467438457910304805409,56,0.133662553285813473502585679853,0.635395418911348652812876025564 467,467_0,COMPLETED,BoTorch,0.250176073323685521643255924573,50,0.369856802436601683758965464222,0.524582138891190652429941110313 468,468_0,COMPLETED,BoTorch,0.251105885190545108365256510297,50,0.372651302105680315435165539384,0.524496365581944012923543141369 469,49_0,COMPLETED,BoTorch,0.251402556681138422689514300146,50,1.000000000000000000000000000000,0.500000000000000000000000000000 470,470_0,COMPLETED,BoTorch,0.251244573082489153215135502251,50,0.373888261111739450548441254796,0.524896081385635926430666131637 471,49_0,COMPLETED,BoTorch,0.251559334298118719708270418778,50,1.000000000000000000000000000000,0.500000000000000000000000000000 472,49_0,COMPLETED,BoTorch,0.251378437047756864153313927090,50,1.000000000000000000000000000000,0.500000000000000000000000000000 473,49_0,COMPLETED,BoTorch,0.251371201157742363285763076419,50,1.000000000000000000000000000000,0.500000000000000000000000000000 474,49_0,COMPLETED,BoTorch,0.251341051616015387359936994471,50,1.000000000000000000000000000000,0.500000000000000000000000000000 475,49_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.500000000000000000000000000000 476,476_0,COMPLETED,BoTorch,0.250366618427399933999311087973,50,0.385011005755664492689049893670,0.522602750613219035891177099984 477,477_0,COMPLETED,BoTorch,0.261108297153883284202890990855,86,0.744514949433505490716811436869,0.570286335889250040054321289062 478,49_0,COMPLETED,BoTorch,0.251427882296189064703639814979,50,1.000000000000000000000000000000,0.500000000000000000000000000000 479,479_0,COMPLETED,BoTorch,0.250528219971056453907465311204,50,0.391172553480237827905341418955,0.522766627547402773501517003751 480,480_0,COMPLETED,BoTorch,0.250594548962855712126440721477,50,0.396629973393557433425371527846,0.524016282386301046614107690402 481,481_0,COMPLETED,BoTorch,0.249911963338157239178372037713,50,0.393122961913602964045821863692,0.524252679710148439262695774232 482,49_0,COMPLETED,BoTorch,0.251566570188133109553518806933,50,1.000000000000000000000000000000,0.500000000000000000000000000000 483,483_0,COMPLETED,BoTorch,0.251620839363241644015545261936,50,0.396527269384645997263305616798,0.524410569836330830995052565413 484,49_0,COMPLETED,BoTorch,0.251394114809454949366340770212,50,1.000000000000000000000000000000,0.500000000000000000000000000000 485,485_0,COMPLETED,BoTorch,0.251000964785335289874979025626,50,0.397109511868172115889308315673,0.524555872193372074718809017213 486,486_0,COMPLETED,BoTorch,0.259878195851423021700554727431,78,0.793729635048657677920402875316,0.583341975975781634744521397806 487,49_0,COMPLETED,BoTorch,0.251394114809454949366340770212,50,1.000000000000000000000000000000,0.500000000000000000000000000000 488,49_0,COMPLETED,BoTorch,0.251357935359382556050888979371,50,1.000000000000000000000000000000,0.500000000000000000000000000000 489,489_0,COMPLETED,BoTorch,0.254815484804631009474462643993,62,0.564778322912752650530876508128,0.522606668435037113873420366872 490,490_0,COMPLETED,BoTorch,0.251794500723588998702950902953,51,0.147923926170915376321346457189,0.539880184177309319082382899069 491,49_0,COMPLETED,BoTorch,0.251397732754462088777813733032,50,1.000000000000000000000000000000,0.500000000000000000000000000000 492,492_0,COMPLETED,BoTorch,0.253263386396526790633743075887,54,0.174169476330280292852847878748,0.546222475264221407620368609059 493,49_0,COMPLETED,BoTorch,0.251534008683067966671842441428,50,1.000000000000000000000000000000,0.500000000000000000000000000000 494,494_0,COMPLETED,BoTorch,0.251515918958031825525267777266,50,0.407874216568157033080410656112,0.525486180735032171185139304725 495,495_0,COMPLETED,BoTorch,0.251888567293777176914204574132,50,0.419650814452644960184102274070,0.525669264920565648679939840804 496,496_0,COMPLETED,BoTorch,0.252047756874095529866508513805,50,0.416637460689971539373743780743,0.527253421683711365197666509630 497,497_0,COMPLETED,BoTorch,0.250143511818620378761579559068,50,0.572022732254117705075202593434,0.550174748525023460388183593750 498,498_0,COMPLETED,BoTorch,0.291204775687409544993045074079,291,0.920848842617221108319824907085,0.735204365011294269649511079479 499,499_0,COMPLETED,BoTorch,0.287357694163048682511885090207,257,0.537422461435198783874511718750,0.629534575622528791427612304688 500,500_0,COMPLETED,BoTorch,0.251239749155812819303434935136,50,0.417325344358840943925770261558,0.527318303893845707897014563059
start_time,end_time,run_time,program_string,n_reference_samples,recent_samples_proportion,threshold,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727627260,1727627799,539,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 348 recent_samples_proportion 0.508871104195714 threshold 0.7027874972671271,348,0.508871104195714,0.7027874972671271,0.29340207428847076,0,None,i7186,531,0.0001417900413837694 1727627260,1727627803,543,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 269 recent_samples_proportion 0.3837246211245656 threshold 0.7786703525111079,269,0.3837246211245656,0.7786703525111079,0.2935853835021708,0,None,i7186,536,0.00011235680481944863 1727627260,1727627805,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 455 recent_samples_proportion 0.8557212764397264 threshold 0.5746874449774623,455,0.8557212764397264,0.5746874449774623,0.30077424023154853,0,None,i7186,537,0.00017485404563636738 1727627260,1727627807,547,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 241 recent_samples_proportion 0.7680913680233061 threshold 0.7235765245743097,241,0.7680913680233061,0.7235765245743097,0.2883369512783406,0,None,i7186,540,0.00010205031818500879 1727627264,1727627814,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.9154967828653753 threshold 0.7674778840504587,500,0.9154967828653753,0.7674778840504587,0.302009165460685,0,None,i7186,542,0.00019467055671918297 1727627261,1727627817,556,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 444 recent_samples_proportion 0.3054295971989632 threshold 0.7986467182636261,444,0.3054295971989632,0.7986467182636261,0.305520984081042,0,None,i7186,549,0.0001791505131507322 1727627265,1727627817,552,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 163 recent_samples_proportion 0.5955247078090906 threshold 0.5043775542639196,163,0.5955247078090906,0.5043775542639196,0.27683429811866855,0,None,i7186,546,6.784510359929403e-05 1727627312,1727627823,511,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 369 recent_samples_proportion 0.7237072226591408 threshold 0.7657400383614004,369,0.7237072226591408,0.7657400383614004,0.29609864930053065,0,None,i7174,480,0.00015225204811513166 1727627312,1727627824,512,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 157 recent_samples_proportion 0.6492325302213431 threshold 0.5450124279595912,157,0.6492325302213431,0.5450124279595912,0.27343101784852875,0,None,i7174,481,6.642152711921924e-05 1727627280,1727627828,548,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 265 recent_samples_proportion 0.46874401429668067 threshold 0.5720227183774114,265,0.46874401429668067,0.5720227183774114,0.2875639170284612,0,None,i7186,541,0.00010685496830124767 1727627280,1727627830,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 401 recent_samples_proportion 0.48090051310136916 threshold 0.5442159890197218,401,0.48090051310136916,0.5442159890197218,0.2987252773757839,0,None,i7186,543,0.00015496805904860925 1727627280,1727627834,554,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 153 recent_samples_proportion 0.22909378279000522 threshold 0.6284269778057933,153,0.22909378279000522,0.6284269778057933,0.27570791123974914,0,None,i7186,547,6.394834973396297e-05 1727627298,1727627844,546,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 448 recent_samples_proportion 0.40861616535112266 threshold 0.6463898092508316,448,0.40861616535112266,0.6463898092508316,0.3011517124939701,0,None,i7181,502,0.00017321365036738592 1727627298,1727627851,553,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 347 recent_samples_proportion 0.6277296494692564 threshold 0.567370143532753,347,0.6277296494692564,0.567370143532753,0.2940605402797878,0,None,i7181,508,0.00013707107013801573 1727627298,1727627853,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 400 recent_samples_proportion 0.5097531293518841 threshold 0.7005095837637783,400,0.5097531293518841,0.7005095837637783,0.29762301013024606,0,None,i7181,510,0.00015921295670262074 1727627298,1727627863,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 446 recent_samples_proportion 0.5049605339765549 threshold 0.7784714158624411,446,0.5049605339765549,0.7784714158624411,0.30504823926676317,0,None,i7181,520,0.00017779047713020217 1727627298,1727627863,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 208 recent_samples_proportion 0.9404736054129899 threshold 0.6867453590966761,208,0.9404736054129899,0.6867453590966761,0.28480824891461654,0,None,i7181,521,8.721236390086451e-05 1727627298,1727627864,566,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 205 recent_samples_proportion 0.374514772836119 threshold 0.6680422823876142,205,0.374514772836119,0.6680422823876142,0.28533888084901105,0,None,i7181,521,8.472251608938052e-05 1727627298,1727627864,566,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 324 recent_samples_proportion 0.597467388212681 threshold 0.7568320372141898,324,0.597467388212681,0.7568320372141898,0.299264351181862,0,None,i7181,521,0.00013240345983965806 1727627298,1727627904,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 70 recent_samples_proportion 0.8285587091930211 threshold 0.7294354704208672,70,0.8285587091930211,0.7294354704208672,0.2610287023637241,0,None,i7181,562,3.150871969034206e-05 1727628079,1727628627,548,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8951168306913613 threshold 0.7669404688944613,50,0.8951168306913613,0.7669404688944613,0.2562916063675832,0,None,i7174,542,2.4831299186291505e-05 1727628085,1727628642,557,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9007527403615391 threshold 0.582515833725934,50,0.9007527403615391,0.582515833725934,0.2512325132657983,0,None,i7174,551,2.275969501335075e-05 1727628079,1727628645,566,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 94 recent_samples_proportion 0.7003538176112476 threshold 0.8,94,0.7003538176112476,0.8,0.2681994693680656,0,None,i7181,560,4.219568218697712e-05 1727628065,1727628656,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.8904591141599849 threshold 0.7446515537148738,74,0.8904591141599849,0.7446515537148738,0.2607308248914616,0,None,i7181,584,3.335779756508146e-05 1727628065,1727628666,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.7725974575666172 threshold 0.6926304798346332,74,0.7725974575666172,0.6926304798346332,0.26014471780028947,0,None,i7181,595,3.290250677844869e-05 1727628065,1727628694,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8247361176507693 threshold 0.731842666010376,50,0.8247361176507693,0.731842666010376,0.2560287023637241,0,None,i7181,623,2.4138373792996855e-05 1727628079,1727628725,646,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.510954554067603 threshold 0.5640191803526431,50,0.510954554067603,0.5640191803526431,0.2518029425952726,0,None,i7181,639,2.2700343237485827e-05 1727628065,1727628726,661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.25117342016401356,0,None,i7181,654,2.3027753569305963e-05 1727628079,1727628727,648,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.6396426886773676,50,1,0.6396426886773676,0.25228051133622764,0,None,i7181,642,2.2863509295966695e-05 1727628079,1727628727,648,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8572932518509123 threshold 0.6879960665175182,50,0.8572932518509123,0.6879960665175182,0.25275566811384464,0,None,i7181,642,2.3481245476890267e-05 1727628045,1727628731,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6521356869407453 threshold 0.7800946873558767,50,0.6521356869407453,0.7800946873558767,0.25498432223830203,0,None,i7186,678,2.479169160423584e-05 1727628026,1727628736,710,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7231441753371812 threshold 0.6631163724118541,50,0.7231441753371812,0.6631163724118541,0.2523323685479981,0,None,i7186,703,2.3120469866442445e-05 1727628045,1727628738,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.8,50,1,0.8,0.2591835504100337,0,None,i7186,685,2.5508420092846445e-05 1727628045,1727628745,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5085349016072989 threshold 0.6702723255990557,50,0.5085349016072989,0.6702723255990557,0.25199589966232516,0,None,i7186,692,2.3259411424532466e-05 1727628045,1727628748,703,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.6739415899853012,50,1,0.6739415899853012,0.2529015918958032,0,None,i7186,695,2.3097620191086098e-05 1727628049,1727628748,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6041469410365841 threshold 0.7260470304433789,50,0.6041469410365841,0.7260470304433789,0.253942354076218,0,None,i7186,692,2.4290479443460627e-05 1727628045,1727628751,706,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34390643318001063 threshold 0.7914932973156199,50,0.34390643318001063,0.7914932973156199,0.25318137964302945,0,None,i7186,698,2.4816524891005478e-05 1727628045,1727628752,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8243385890326634 threshold 0.595549766455523,50,0.8243385890326634,0.595549766455523,0.2524674384949349,0,None,i7186,699,2.2657742267744346e-05 1727628046,1727628756,710,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5169294982179243,50,1,0.5169294982179243,0.2513615533043898,0,None,i7186,703,2.257515399418247e-05 1727628065,1727628768,703,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.7536816299292337,50,1,0.7536816299292337,0.25683309213699956,0,None,i7186,696,2.4677216542224914e-05 1727628727,1727629299,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.241624122136533 threshold 0.7308468626353211,50,0.241624122136533,0.7308468626353211,0.25370477568740957,0,None,i7174,565,2.402577028748166e-05 1727628706,1727629300,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 67 recent_samples_proportion 0.7861154539408896 threshold 0.6682588412964889,67,0.7861154539408896,0.6682588412964889,0.257468644476604,0,None,i7181,588,2.9973462587454476e-05 1727628666,1727629303,637,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5797262930922972,50,1,0.5797262930922972,0.251396526772793,0,None,i7181,631,2.2635726333402863e-05 1727628663,1727629308,645,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8146800116081847 threshold 0.8,50,0.8146800116081847,0.8,0.25811143270622283,0,None,i7127,602,2.6064612944350003e-05 1727628686,1727629324,638,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7256049911485631 threshold 0.5,50,0.7256049911485631,0.5,0.2515086830680173,0,None,i7181,632,2.2592527493705403e-05 1727628706,1727629338,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.811883031469978 threshold 0.5443020785222981,50,0.811883031469978,0.5443020785222981,0.25144958996623257,0,None,i7181,625,2.258920733742289e-05 1727628786,1727629461,675,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.39688426387106346 threshold 0.5,50,0.39688426387106346,0.5,0.25130125422093585,0,None,i7186,668,2.2692055006497284e-05 1727628786,1727629478,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.24692278022867648 threshold 0.5843446439262754,50,0.24692278022867648,0.5843446439262754,0.25245658465991316,0,None,i7186,685,2.2892577624702857e-05 1727628773,1727629479,706,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5809433914939344,50,0.1,0.5809433914939344,0.25266280752532566,0,None,i7186,699,2.2889696816253733e-05 1727628786,1727629484,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515545103714424,0,None,i7186,691,2.2644148609138903e-05 1727628906,1727629508,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 71 recent_samples_proportion 0.1 threshold 0.5,71,0.1,0.5,0.25896044380125427,0,None,i7181,595,3.1315338033379683e-05 1727628894,1727629521,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 59 recent_samples_proportion 0.7184673106431017 threshold 0.5,59,0.7184673106431017,0.5,0.2542197298601061,0,None,i7181,620,2.6331239985218827e-05 1727628886,1727629522,636,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 77 recent_samples_proportion 1 threshold 0.5,77,1,0.5,0.25908827785817656,0,None,i7186,628,3.377293442101848e-05 1727628894,1727629536,642,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34959584137443656 threshold 0.6834796226739253,50,0.34959584137443656,0.6834796226739253,0.2520369030390738,0,None,i7181,635,2.3597251304368684e-05 1727628906,1727629546,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6018965374185415 threshold 0.5,50,0.6018965374185415,0.5,0.251410998552822,0,None,i7181,634,2.27345084948891e-05 1727628886,1727629601,715,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6223756119907424,50,0.1,0.6223756119907424,0.252498794018331,0,None,i7186,707,2.3193544773598272e-05 1727628886,1727629605,719,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6631648214504046,50,0.1,0.6631648214504046,0.2541196333815726,0,None,i7186,712,2.327532277599452e-05 1727628886,1727629607,721,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6645845228492501,50,0.1,0.6645845228492501,0.2525241196333816,0,None,i7186,713,2.3470479086868166e-05 1727628886,1727629610,724,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.7183305392965172,50,0.1,0.7183305392965172,0.25479739507959476,0,None,i7186,717,2.3703549633595994e-05 1727628886,1727629630,744,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.8,50,0.1,0.8,0.2533333333333333,0,None,i7186,737,2.4912964312830394e-05 1727629369,1727629908,539,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.4055609083700962 threshold 0.5,51,0.4055609083700962,0.5,0.25199589966232516,0,None,i7174,533,2.3029612947806544e-05 1727629377,1727629940,563,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.19153850144718226 threshold 0.5751612690580754,74,0.19153850144718226,0.5751612690580754,0.2580342498794018,0,None,i7181,556,3.273535508290288e-05 1727629346,1727629941,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 57 recent_samples_proportion 0.9606283273252425 threshold 0.5289147861281933,57,0.9606283273252425,0.5289147861281933,0.2527110467920888,0,None,i7181,588,2.559195853586254e-05 1727629406,1727629951,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.10746231052557388 threshold 0.5560204894564615,50,0.10746231052557388,0.5560204894564615,0.25207428847081526,0,None,i7174,538,2.303047715396889e-05 1727629346,1727629968,622,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.12082044079791399 threshold 0.6069573571681743,50,0.12082044079791399,0.6069573571681743,0.25313796430294255,0,None,i7181,615,2.2933210630687478e-05 1727629377,1727629994,617,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3017861312897311 threshold 0.5,50,0.3017861312897311,0.5,0.25203931500241195,0,None,i7181,610,2.2611392449700735e-05 1727629527,1727630145,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 85 recent_samples_proportion 0.7335785293669386 threshold 0.5,85,0.7335785293669386,0.5,0.2610865894838399,0,None,i7186,611,3.7116265161942704e-05 1727629527,1727630174,647,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 67 recent_samples_proportion 0.4729442914800638 threshold 0.5,67,0.4729442914800638,0.5,0.257269657501206,0,None,i7186,640,2.961571734253329e-05 1727629566,1727630193,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8604728048405845 threshold 0.5,50,0.8604728048405845,0.5,0.2520356970574047,0,None,i7181,620,2.254613567212041e-05 1727629566,1727630198,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7624117895690952 threshold 0.5,50,0.7624117895690952,0.5,0.2522214182344429,0,None,i7181,626,2.2537469853430866e-05 1727629498,1727630203,705,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.10812884675207449 threshold 0.596509307403561,51,0.10812884675207449,0.596509307403561,0.2540002411963338,0,None,i7186,698,2.34797263285972e-05 1727629528,1727630223,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5484667692811547,50,1,0.5484667692811547,0.2522624216111915,0,None,i7186,687,2.279054327587732e-05 1727629586,1727630226,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3094134310322576 threshold 0.7925468178597689,50,0.3094134310322576,0.7925468178597689,0.2551483357452967,0,None,i7181,633,2.5533897056135003e-05 1727629558,1727630251,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 55 recent_samples_proportion 0.1 threshold 0.7146177813091479,55,0.1,0.7146177813091479,0.2531970574047274,0,None,i7186,686,2.5856838563209154e-05 1727629607,1727630299,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4045598201177094 threshold 0.6715732033737521,50,0.4045598201177094,0.6715732033737521,0.25169922817173174,0,None,i7186,685,2.3273829245992706e-05 1727629679,1727630301,622,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5593308103947532 threshold 0.5838643682015004,50,0.5593308103947532,0.5838643682015004,0.2521912686927159,0,None,i7181,616,2.2731229278677712e-05 1727629647,1727630343,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7980056370539346 threshold 0.5491807742441125,50,0.7980056370539346,0.5491807742441125,0.25227327544621325,0,None,i7186,689,2.2591351304732545e-05 1727629667,1727630371,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.2523203087313073,0,None,i7186,697,2.2716151235375976e-05 1727629647,1727630382,735,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.7282186223646464,50,0.1,0.7282186223646464,0.2541425470332851,0,None,i7186,728,2.4173882523763046e-05 1727629679,1727630388,709,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.10103238663965072 threshold 0.5815409750637085,50,0.10103238663965072,0.5815409750637085,0.2521418234442836,0,None,i7186,703,2.3021878890600087e-05 1727629927,1727630476,549,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40590734539855367 threshold 0.5,50,0.40590734539855367,0.5,0.25165581283164495,0,None,i7174,543,2.2663098817315722e-05 1727630027,1727630542,515,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 73 recent_samples_proportion 0.3341499648893751 threshold 0.5,73,0.3341499648893751,0.5,0.2580451037144236,0,None,i7174,509,3.214956456467132e-05 1727629967,1727630584,617,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3412351002165557 threshold 0.528287564882614,50,0.3412351002165557,0.528287564882614,0.25052098408104195,0,None,i7181,610,2.2727685713792108e-05 1727629980,1727630602,622,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28905990849221896 threshold 0.5,50,0.28905990849221896,0.5,0.2517149059334298,0,None,i7181,615,2.2645432083255372e-05 1727630007,1727630607,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.24690323700326117 threshold 0.5843577548559037,50,0.24690323700326117,0.5843577548559037,0.25217317896767966,0,None,i7181,593,2.3161292980956076e-05 1727630007,1727630620,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4322255710374957 threshold 0.5348609165627681,50,0.4322255710374957,0.5348609165627681,0.2516521948866377,0,None,i7181,607,2.2793061049672157e-05 1727630167,1727630857,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.26361894229945054 threshold 0.5,50,0.26361894229945054,0.5,0.25032320308731304,0,None,i7186,683,2.2753993682528063e-05 1727630207,1727630910,703,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25145079594790154,0,None,i7186,696,2.256410041667287e-05 1727630307,1727630925,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6471578861748125 threshold 0.5830450159362923,50,0.6471578861748125,0.5830450159362923,0.2516015436565364,0,None,i7181,611,2.2880210801860015e-05 1727630312,1727630935,623,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.2520200192957067,0,None,i7181,616,2.291418032061442e-05 1727630248,1727630948,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5531669015005317,50,1,0.5531669015005317,0.25144235407621807,0,None,i7186,693,2.2631498915696256e-05 1727630342,1727630957,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5436159431018827 threshold 0.5244975591530691,50,0.5436159431018827,0.5244975591530691,0.2523685479980704,0,None,i7181,608,2.2599427027127934e-05 1727630248,1727630969,721,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.2522684515195369,0,None,i7186,714,2.2953240490608966e-05 1727630367,1727630980,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135793535938256,0,None,i7181,606,2.256426070391681e-05 1727630288,1727630999,711,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.718566964967316 threshold 0.5535034172330167,50,0.718566964967316,0.5535034172330167,0.2515086830680173,0,None,i7186,704,2.2627403269398064e-05 1727630308,1727631023,715,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.25272069464544133,0,None,i7186,708,2.274497349423824e-05 1727630348,1727631048,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251396526772793,0,None,i7186,693,2.2600835578218508e-05 1727630507,1727631052,545,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5122112783412434 threshold 0.609524129777033,50,0.5122112783412434,0.609524129777033,0.2514471780028944,0,None,i7174,538,2.289986260624958e-05 1727630403,1727631111,708,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513675832127351,0,None,i7186,701,2.2569223407202108e-05 1727630567,1727631117,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5284345275035339 threshold 0.6759329281464879,50,0.5284345275035339,0.6759329281464879,0.2514664737095996,0,None,i7174,543,2.3412099482964058e-05 1727630428,1727631132,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513627592860589,0,None,i7186,697,2.2558409927380472e-05 1727630428,1727631135,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5481435302844576,50,1,0.5481435302844576,0.25149421128798843,0,None,i7186,700,2.2579498935793625e-05 1727630614,1727631211,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5061724873297965 threshold 0.5915032368376911,50,0.5061724873297965,0.5915032368376911,0.2518921852387843,0,None,i7181,591,2.279940793330435e-05 1727630644,1727631247,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.36314711990195725 threshold 0.5,50,0.36314711990195725,0.5,0.25116497829233,0,None,i7181,595,2.266402006448276e-05 1727630645,1727631250,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.38624014445151733 threshold 0.5,50,0.38624014445151733,0.5,0.25138808490110953,0,None,i7181,598,2.2661436332517893e-05 1727630668,1727631286,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3701302139211772 threshold 0.5,50,0.3701302139211772,0.5,0.25078991799324646,0,None,i7181,611,2.2673185193446493e-05 1727630886,1727631585,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.915187038391401 threshold 0.5594588375246428,50,0.915187038391401,0.5594588375246428,0.2525337674867342,0,None,i7186,692,2.2579443371798367e-05 1727630988,1727631606,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.35143286511727445 threshold 0.5195154784763628,50,0.35143286511727445,0.5195154784763628,0.25164254703328515,0,None,i7181,612,2.2708879299648738e-05 1727631028,1727631646,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5474599544151609,50,1,0.5474599544151609,0.2513977327544621,0,None,i7181,611,2.260215478243044e-05 1727630946,1727631649,703,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31052547498608235 threshold 0.5,50,0.31052547498608235,0.5,0.2517004341534008,0,None,i7186,695,2.2689818167669318e-05 1727631111,1727631661,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133019778099375,0,None,i7174,544,2.2571525040628285e-05 1727630968,1727631670,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5471211633119536,50,1,0.5471211633119536,0.2514821514712976,0,None,i7186,695,2.2612286514754203e-05 1727630977,1727631684,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5544946487422214,50,1,0.5544946487422214,0.2514905933429812,0,None,i7186,700,2.2592249902776512e-05 1727631068,1727631688,620,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33927264032093435 threshold 0.5,50,0.33927264032093435,0.5,0.25070549927641095,0,None,i7181,614,2.2679798662354598e-05 1727631097,1727631711,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513748191027496,0,None,i7181,607,2.2563221512474264e-05 1727631028,1727631727,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513398456343463,0,None,i7186,692,2.256676310362998e-05 1727631028,1727631732,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5087810141424729 threshold 0.6704582937351258,50,0.5087810141424729,0.6704582937351258,0.2521852387843705,0,None,i7186,697,2.328733941755577e-05 1727631208,1727631763,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2516039556198746,0,None,i7174,548,2.258943910445458e-05 1727631067,1727631765,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3344990162053166 threshold 0.5,50,0.3344990162053166,0.5,0.2526893391220454,0,None,i7186,691,2.2751176573894337e-05 1727631148,1727631843,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515750120598167,0,None,i7186,686,2.258286901849743e-05 1727631261,1727631874,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7668866660969839 threshold 0.5930256314606324,50,0.7668866660969839,0.5930256314606324,0.25242281717317894,0,None,i7181,606,2.2664723576558783e-05 1727631188,1727631879,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4609460659664123 threshold 0.6053872492226299,50,0.4609460659664123,0.6053872492226299,0.25233116256632904,0,None,i7186,684,2.3021553736909074e-05 1727631209,1727631900,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5361656418189007 threshold 0.621598748946203,50,0.5361656418189007,0.621598748946203,0.25194766039556193,0,None,i7186,684,2.2861450257361465e-05 1727631289,1727631910,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25130246020260494,0,None,i7181,614,2.2587138471836013e-05 1727631309,1727631913,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5578356922562746 threshold 0.6198996726059094,50,0.5578356922562746,0.6198996726059094,0.25224433188615536,0,None,i7181,597,2.296628655675481e-05 1727631329,1727631940,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25138808490110953,0,None,i7181,605,2.2566571910539858e-05 1727631641,1727632243,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514532079112397,0,None,i7181,595,2.2577851974661448e-05 1727631769,1727632274,505,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 189 recent_samples_proportion 0.4253429178148508 threshold 0.6116573217324912,189,0.4253429178148508,0.6116573217324912,0.2830873130728413,0,None,i7181,497,7.78591411167351e-05 1727631629,1727632294,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.335456322976192 threshold 0.5,50,0.335456322976192,0.5,0.25132416787264833,0,None,i7186,658,2.2717349968602183e-05 1727631689,1727632303,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.12117221357019173 threshold 0.5871005073607148,50,0.12117221357019173,0.5871005073607148,0.2526302460202605,0,None,i7181,608,2.3367611167689552e-05 1727631769,1727632367,598,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133863965267733,0,None,i7181,590,2.256961581896341e-05 1727631702,1727632381,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5035930421030523 threshold 0.6273459256272517,50,0.5035930421030523,0.6273459256272517,0.25243005306319344,0,None,i7186,672,2.3039989029481598e-05 1727631749,1727632421,672,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5503999984034065 threshold 0.6975324887950943,50,0.5503999984034065,0.6975324887950943,0.2527930535455861,0,None,i7186,664,2.3542416702361733e-05 1727631869,1727632423,554,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135914134105164,0,None,i7174,548,2.25586325564499e-05 1727631869,1727632424,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 491 recent_samples_proportion 0.8652379098875398 threshold 0.7834143458229941,491,0.8652379098875398,0.7834143458229941,0.30292812349252296,0,None,i7186,547,0.00019232683487386813 1727631762,1727632439,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.49458580306772926 threshold 0.61908478195573,50,0.49458580306772926,0.61908478195573,0.2523106608779546,0,None,i7186,670,2.288022139653115e-05 1727631850,1727632452,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 96 recent_samples_proportion 0.8975289633071956 threshold 0.697450228229425,96,0.8975289633071956,0.697450228229425,0.2649167872648336,0,None,i7186,595,4.20940374092149e-05 1727631889,1727632454,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 231 recent_samples_proportion 0.26873371815308933 threshold 0.7608409422449768,231,0.26873371815308933,0.7608409422449768,0.2882091172214183,0,None,i7186,557,9.667263611682812e-05 1727631943,1727632516,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 57 recent_samples_proportion 0.5436195206511454 threshold 0.6973416267436219,57,0.5436195206511454,0.6973416267436219,0.25570429329474187,0,None,i7181,567,2.6155609077341307e-05 1727631849,1727632523,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3427143731039225 threshold 0.5,50,0.3427143731039225,0.5,0.25186806560540276,0,None,i7186,666,2.2613590302706542e-05 1727631849,1727632525,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4629105172381853 threshold 0.6146275975045676,50,0.4629105172381853,0.6146275975045676,0.2528424987940183,0,None,i7186,668,2.332568710291322e-05 1727632004,1727632572,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 76 recent_samples_proportion 0.8933242867096826 threshold 0.5406065802597989,76,0.8933242867096826,0.5406065802597989,0.26105282199710567,0,None,i7181,561,3.317284664177092e-05 1727632029,1727632585,556,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3406017037557682 threshold 0.5,50,0.3406017037557682,0.5,0.2513048721659431,0,None,i7174,548,2.2656776052028417e-05 1727631929,1727632603,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513772310660878,0,None,i7186,667,2.2561684388837468e-05 1727631990,1727632604,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5214462874260226,50,1,0.5214462874260226,0.25169078630004826,0,None,i7181,607,2.2591049942383932e-05 1727632004,1727632610,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5219178933079507,50,1,0.5219178933079507,0.2513820549927641,0,None,i7181,600,2.2586409366799438e-05 1727632275,1727632882,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8164251298101998 threshold 0.6072917518263862,50,0.8164251298101998,0.6072917518263862,0.2525820067534974,0,None,i7181,600,2.2776304217829544e-05 1727632350,1727632955,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.38154382677876697 threshold 0.6808894820272138,50,0.38154382677876697,0.6808894820272138,0.2536818620356971,0,None,i7181,598,2.3480573480741386e-05 1727632307,1727632960,653,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 56 recent_samples_proportion 0.5841234013042255 threshold 0.7170285510533898,56,0.5841234013042255,0.7170285510533898,0.2541690786300048,0,None,i7186,646,2.5988741276319057e-05 1727632426,1727632986,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 190 recent_samples_proportion 0.5272375773638487 threshold 0.717803246807307,190,0.5272375773638487,0.717803246807307,0.2847672455378678,0,None,i7186,552,8.020997435677688e-05 1727632366,1727632994,628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2516461649782923,0,None,i7181,622,2.2567355472993264e-05 1727632410,1727633105,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33969300020348386 threshold 0.5,50,0.33969300020348386,0.5,0.25202604920405214,0,None,i7186,687,2.2687918024171792e-05 1727632487,1727633116,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 73 recent_samples_proportion 0.920934926539292 threshold 0.5473213849553357,73,0.920934926539292,0.5473213849553357,0.25857211770381094,0,None,i7186,622,3.209675460499313e-05 1727632487,1727633117,630,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.9060134773388001 threshold 0.5423201247780889,74,0.9060134773388001,0.5423201247780889,0.25768572117703814,0,None,i7186,624,3.261871958704217e-05 1727632727,1727633205,478,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 155 recent_samples_proportion 0.49689422168979924 threshold 0.5523021846590113,155,0.49689422168979924,0.5523021846590113,0.2776808972503618,0,None,i7174,469,6.460576832457891e-05 1727632510,1727633208,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2764520123725519 threshold 0.6600164985044533,50,0.2764520123725519,0.6600164985044533,0.25096237337192473,0,None,i7186,691,2.3372634418799784e-05 1727632547,1727633236,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25143511818620357,0,None,i7186,682,2.256506561778707e-05 1727632630,1727633241,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25131452001929566,0,None,i7181,604,2.2568321986742785e-05 1727632637,1727633251,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.76657052562702 threshold 0.6058707476569679,50,0.76657052562702,0.6058707476569679,0.2526049204052099,0,None,i7181,607,2.2903370023239036e-05 1727632570,1727633259,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3400280956368783 threshold 0.5,50,0.3400280956368783,0.5,0.2530643994211288,0,None,i7186,682,2.2564781373742513e-05 1727632727,1727633296,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5605658875998997,50,0.1,0.5605658875998997,0.2520839363241679,0,None,i7174,561,2.2967705128170184e-05 1727632570,1727633305,735,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.7675629218402411,50,0.1,0.7675629218402411,0.2527098408104197,0,None,i7186,728,2.4639074352772483e-05 1727632630,1727633316,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25131452001929566,0,None,i7186,678,2.2568321986742785e-05 1727632710,1727633328,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25131572600096475,0,None,i7181,610,2.255991588819556e-05 1727632710,1727633337,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3471291101106429 threshold 0.5,50,0.3471291101106429,0.5,0.2510974433188615,0,None,i7181,620,2.265419511529316e-05 1727632728,1727633352,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.6506541121293413 threshold 0.6339518645138243,51,0.6506541121293413,0.6339518645138243,0.25158707187650753,0,None,i7181,617,2.3338795205721203e-05 1727632909,1727633473,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 74 recent_samples_proportion 0.905304960216311 threshold 0.5380423760242654,74,0.905304960216311,0.5380423760242654,0.25817414375301495,0,None,i7181,556,3.2577447959335746e-05 1727633051,1727633562,511,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 152 recent_samples_proportion 0.25509237945079805 threshold 0.5175696981139482,152,0.25509237945079805,0.5175696981139482,0.27639532079112394,0,None,i7181,505,6.314737626636995e-05 1727633051,1727633644,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5626443232215105 threshold 0.6380357001680823,50,0.5626443232215105,0.6380357001680823,0.25107573564881813,0,None,i7181,586,2.306284532103978e-05 1727633000,1727633669,669,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25144958996623257,0,None,i7186,661,2.2561396678958842e-05 1727633011,1727633687,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.35742509976059766 threshold 0.5,50,0.35742509976059766,0.5,0.2513832609744332,0,None,i7186,669,2.263514776421647e-05 1727633262,1727633813,551,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 328 recent_samples_proportion 0.2515556773170829 threshold 0.7019275777041912,328,0.2515556773170829,0.7019275777041912,0.2969934876989869,0,None,i7186,543,0.00012999837639405916 1727633262,1727633820,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 175 recent_samples_proportion 0.12343338404014634 threshold 0.5411902829042474,175,0.12343338404014634,0.5411902829042474,0.2794488663772311,0,None,i7186,551,7.193170689304884e-05 1727633151,1727633838,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3667171890355858 threshold 0.5,50,0.3667171890355858,0.5,0.25135793535938256,0,None,i7186,679,2.2663300176591025e-05 1727633182,1727633855,673,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3613406628632806 threshold 0.5,50,0.3613406628632806,0.5,0.25187168355041,0,None,i7186,666,2.2686236318120403e-05 1727633202,1727633873,671,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514459720212253,0,None,i7186,664,2.2568565701643696e-05 1727633333,1727633883,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 335 recent_samples_proportion 0.32872823159049636 threshold 0.541447449674492,335,0.32872823159049636,0.541447449674492,0.2961589483839846,0,None,i7186,543,0.00013065388982214145 1727633322,1727633918,596,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 93 recent_samples_proportion 0.4404650002595386 threshold 0.5309740394883645,93,0.4404650002595386,0.5309740394883645,0.2644561022672456,0,None,i7186,589,4.0239428201328784e-05 1727633342,1727633965,623,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515472744814279,0,None,i7181,615,2.2569276534308043e-05 1727633322,1727633998,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40436877957720774 threshold 0.5,50,0.40436877957720774,0.5,0.2515159189580318,0,None,i7186,669,2.2592081384579973e-05 1727633407,1727634017,610,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25132899179932466,0,None,i7181,603,2.2563264644999343e-05 1727633447,1727634021,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513917028461168,0,None,i7174,567,2.2567738482411018e-05 1727633427,1727634031,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514134105161602,0,None,i7181,597,2.258169470796937e-05 1727633467,1727634031,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513699951760733,0,None,i7174,558,2.2574634142486956e-05 1727633507,1727634031,524,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 158 recent_samples_proportion 0.5743783534346899 threshold 0.5562406609206938,158,0.5743783534346899,0.5562406609206938,0.27856488181379646,0,None,i7181,517,6.58507173461627e-05 1727633423,1727634037,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25138687891944045,0,None,i7181,607,2.2563868034991905e-05 1727633447,1727634054,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32068002273610724 threshold 0.5216447085582108,50,0.32068002273610724,0.5216447085582108,0.2505607814761216,0,None,i7181,600,2.276888308997257e-05 1727633593,1727634113,520,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 155 recent_samples_proportion 0.5765192146566418 threshold 0.5630192470693507,155,0.5765192146566418,0.5630192470693507,0.27848046309696095,0,None,i7181,514,6.468731248455754e-05 1727633746,1727634348,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514604438012542,0,None,i7181,596,2.2598288026554738e-05 1727633688,1727634358,670,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513458755426917,0,None,i7186,662,2.256361419702285e-05 1727633706,1727634387,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513603473227207,0,None,i7186,674,2.257800897291361e-05 1727633847,1727634537,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513482875060299,0,None,i7186,683,2.257597090398972e-05 1727633877,1727634565,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135190545103714,0,None,i7186,681,2.259383542246283e-05 1727633887,1727634568,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513482875060299,0,None,i7186,674,2.2560688708300866e-05 1727633998,1727634602,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 95 recent_samples_proportion 0.8120398497208953 threshold 0.7295133444480598,95,0.8120398497208953,0.7295133444480598,0.2650856246985046,0,None,i7186,597,4.20688905957814e-05 1727634027,1727634603,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 150 recent_samples_proportion 0.6954456841354097 threshold 0.5454504516725529,150,0.6954456841354097,0.5454504516725529,0.2773191027496382,0,None,i7186,569,6.280242925263288e-05 1727634087,1727634611,524,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 369 recent_samples_proportion 0.5623046410192547 threshold 0.7148263738937223,369,0.5623046410192547,0.7148263738937223,0.2931283164495899,0,None,i7181,518,0.00015066380452983722 1727634108,1727634630,522,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 278 recent_samples_proportion 0.5226335479877889 threshold 0.7114014872349799,278,0.5226335479877889,0.7114014872349799,0.2882368547998071,0,None,i7181,515,0.00011630427661943981 1727633947,1727634630,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513627592860589,0,None,i7186,676,2.256396379207608e-05 1727633967,1727634651,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9999999999999999 threshold 0.5,50,0.9999999999999999,0.5,0.25137361312108053,0,None,i7186,677,2.2563295740434445e-05 1727633967,1727634654,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137361312108053,0,None,i7186,680,2.2563295740434445e-05 1727634107,1727634709,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8816453722305596 threshold 0.579951639752835,50,0.8816453722305596,0.579951639752835,0.25096840328027015,0,None,i7181,596,2.2718278800027784e-05 1727634239,1727634712,473,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 103 recent_samples_proportion 0.5003455963917077 threshold 0.5080127004534006,103,0.5003455963917077,0.5080127004534006,0.265888808490111,0,None,i7174,467,4.431348905296513e-05 1727634227,1727634757,530,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 57 recent_samples_proportion 0.8755853431112118 threshold 0.7237239072532964,57,0.8755853431112118,0.7237239072532964,0.2554124457308249,0,None,i7174,522,2.6390088315991706e-05 1727634207,1727634771,564,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 73 recent_samples_proportion 0.7295380206778646 threshold 0.5297387541271746,73,0.7295380206778646,0.5297387541271746,0.25930173661360345,0,None,i7181,557,3.209752482569496e-05 1727634207,1727634773,566,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 73 recent_samples_proportion 0.6086407023482024 threshold 0.7505697142332792,73,0.6086407023482024,0.7505697142332792,0.2602424023154848,0,None,i7181,560,3.317417014749459e-05 1727634180,1727634785,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2517667631452002,0,None,i7181,598,2.261565685960467e-05 1727634187,1727634791,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513555233960444,0,None,i7181,597,2.257552531682213e-05 1727634387,1727634924,537,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 366 recent_samples_proportion 0.3789250830188394 threshold 0.6412263085134328,366,0.3789250830188394,0.6412263085134328,0.29454655089242643,0,None,i7186,529,0.00014450979808913877 1727634447,1727635051,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31386760909213174 threshold 0.5251549201824856,50,0.31386760909213174,0.5251549201824856,0.2520369030390738,0,None,i7181,597,2.2714002233199577e-05 1727634447,1727635112,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31366579369047953 threshold 0.5228801170650968,50,0.31366579369047953,0.5228801170650968,0.2520344910757356,0,None,i7186,658,2.2711332419326176e-05 1727634628,1727635180,552,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 195 recent_samples_proportion 0.14449076233431699 threshold 0.585501269903034,195,0.14449076233431699,0.585501269903034,0.28149421128798846,0,None,i7186,545,7.967446548139487e-05 1727634588,1727635181,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 102 recent_samples_proportion 0.5337798723950983 threshold 0.726147207338363,102,0.5337798723950983,0.726147207338363,0.2682778581765557,0,None,i7186,585,4.4517074581713974e-05 1727634648,1727635313,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7194978660903871 threshold 0.6310790642164648,50,0.7194978660903871,0.6310790642164648,0.2522057404727448,0,None,i7186,658,2.3119705344069163e-05 1727634728,1727635409,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515050651230102,0,None,i7186,674,2.258578822144276e-05 1727634784,1727635427,643,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 68 recent_samples_proportion 0.677391667863297 threshold 0.5923574636508944,68,0.677391667863297,0.5923574636508944,0.25727448142788234,0,None,i7186,636,3.0157608493982997e-05 1727634788,1727635470,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514556198745779,0,None,i7186,675,2.2567971622003763e-05 1727634828,1727635484,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.8850377136841416 threshold 0.5356613370589912,62,0.8850377136841416,0.5356613370589912,0.2560154365653642,0,None,i7186,649,2.7528448619604106e-05 1727634808,1727635509,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8342010618278864 threshold 0.6341066956101546,50,0.8342010618278864,0.6341066956101546,0.2537807525325615,0,None,i7186,695,2.2828228325917224e-05 1727634965,1727635586,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2723831493478419 threshold 0.7265997735711641,50,0.2723831493478419,0.7265997735711641,0.25340086830680175,0,None,i7181,614,2.4007811044393628e-05 1727634988,1727635594,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4074288478473066 threshold 0.6342238048589138,50,0.4074288478473066,0.6342238048589138,0.25214423540762176,0,None,i7181,599,2.309585476408215e-05 1727634995,1727635601,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513458755426917,0,None,i7181,600,2.256222558146515e-05 1727634988,1727635603,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2721751767033471 threshold 0.7266119546055309,50,0.2721751767033471,0.7266119546055309,0.2530161601543657,0,None,i7181,608,2.3882162087661363e-05 1727635008,1727635619,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.540634313016997 threshold 0.6422307417747459,50,0.540634313016997,0.6422307417747459,0.25223830197780994,0,None,i7181,604,2.2942172827182048e-05 1727635008,1727635620,612,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25134466956102264,0,None,i7181,606,2.2576193697573516e-05 1727635108,1727635640,532,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3765974736639134 threshold 0.6212726459448886,50,0.3765974736639134,0.6212726459448886,0.25129281234925227,0,None,i7174,525,2.3137923055888603e-05 1727634965,1727635643,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513832609744332,0,None,i7186,671,2.2562701916752987e-05 1727635025,1727635648,623,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513555233960444,0,None,i7181,616,2.2560243422761512e-05 1727635055,1727635673,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251532802701399,0,None,i7181,611,2.259940029076847e-05 1727635149,1727635839,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6384490476554483 threshold 0.6400203290414481,50,0.6384490476554483,0.6400203290414481,0.25291968162083933,0,None,i7186,683,2.3255386213612767e-05 1727635225,1727635932,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6286570276107979 threshold 0.6377038605739326,50,0.6286570276107979,0.6377038605739326,0.2519585142305837,0,None,i7186,699,2.3119257455962053e-05 1727635237,1727635935,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133140376266283,0,None,i7186,691,2.25631162073516e-05 1727635358,1727636051,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514544138929088,0,None,i7186,686,2.2581951024215043e-05 1727635447,1727636147,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5226803016520587,50,1,0.5226803016520587,0.25162686927158706,0,None,i7186,693,2.2617312449486443e-05 1727635478,1727636178,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3345160680356605 threshold 0.5236583650624942,50,0.3345160680356605,0.5236583650624942,0.2505547515677762,0,None,i7186,693,2.273403424590946e-05 1727635527,1727636224,697,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513205499276411,0,None,i7186,689,2.25651728882495e-05 1727635547,1727636245,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4194352164709624 threshold 0.5,50,0.4194352164709624,0.5,0.2517293777134587,0,None,i7186,691,2.2599821123261973e-05 1727635648,1727636261,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513048721659431,0,None,i7181,606,2.256891584697623e-05 1727635587,1727636286,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25142908827785815,0,None,i7186,691,2.2570995180618315e-05 1727635660,1727636344,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513398456343463,0,None,i7186,677,2.256676310362998e-05 1727635767,1727636376,609,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4268242031497368 threshold 0.5,50,0.4268242031497368,0.5,0.25089122045344914,0,None,i7181,602,2.268654255639492e-05 1727635782,1727636389,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4353003509692215 threshold 0.6402041288963882,50,0.4353003509692215,0.6402041288963882,0.25163651712493973,0,None,i7181,601,2.3161542861088724e-05 1727635782,1727636390,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137843704775686,0,None,i7181,601,2.2567165843890798e-05 1727635808,1727636419,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32314038394510414 threshold 0.523904298951194,50,0.32314038394510414,0.523904298951194,0.25245176073323683,0,None,i7181,605,2.2925905932552352e-05 1727635811,1727636426,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32130073177723145 threshold 0.5245875473846974,50,0.32130073177723145,0.5245875473846974,0.25094066570188134,0,None,i7181,608,2.2703130849563135e-05 1727635807,1727636427,620,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3885170690863514 threshold 0.5,50,0.3885170690863514,0.5,0.2512144235407622,0,None,i7181,612,2.2620431473210188e-05 1727635888,1727636509,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135190545103714,0,None,i7181,611,2.2559077814588752e-05 1727635848,1727636525,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25136637723106614,0,None,i7186,670,2.2574856936070746e-05 1727636088,1727636655,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 153 recent_samples_proportion 0.4303550310076799 threshold 0.621694231706616,153,0.4303550310076799,0.621694231706616,0.27811866859623735,0,None,i7186,560,6.408463330561599e-05 1727635968,1727636659,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513181379643029,0,None,i7186,683,2.257087792733416e-05 1727636008,1727636689,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3578935352852386 threshold 0.6239174198725441,50,0.3578935352852386,0.6239174198725441,0.25239266763145196,0,None,i7186,674,2.3121043032348807e-05 1727636188,1727636876,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3830841850834259 threshold 0.5,50,0.3830841850834259,0.5,0.25160757356488184,0,None,i7186,681,2.27915830192259e-05 1727636248,1727636930,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514893873613121,0,None,i7186,675,2.2564502903430013e-05 1727636391,1727637077,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25147250361794504,0,None,i7186,679,2.2569711503755016e-05 1727636390,1727637086,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25164254703328515,0,None,i7186,689,2.2582882188683424e-05 1727636410,1727637110,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513627592860589,0,None,i7186,693,2.256952039216032e-05 1727636507,1727637137,630,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515400385914134,0,None,i7181,621,2.2587809174239033e-05 1727636530,1727637145,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513748191027496,0,None,i7181,607,2.2556279837138158e-05 1727636490,1727637182,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8860640766163175 threshold 0.5883954529120661,50,0.8860640766163175,0.5883954529120661,0.2507790641582248,0,None,i7186,684,2.2724372747722128e-05 1727636530,1727637223,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25143632416787265,0,None,i7186,686,2.257750122262205e-05 1727636650,1727637230,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33673594972295484 threshold 0.5239206433919822,50,0.33673594972295484,0.5239206433919822,0.2522973950795948,0,None,i7181,573,2.2623404314565623e-05 1727636650,1727637238,588,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135069946936806,0,None,i7181,581,2.2568873402274744e-05 1727636657,1727637242,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251396526772793,0,None,i7181,578,2.2560496814569543e-05 1727636628,1727637249,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513917028461168,0,None,i7181,615,2.255524096540747e-05 1727636671,1727637372,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33856159374106587 threshold 0.5231388147270895,50,0.33856159374106587,0.5231388147270895,0.2512132175590931,0,None,i7186,694,2.268486803097828e-05 1727636779,1727637387,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25151471297636274,0,None,i7181,601,2.2578235738728048e-05 1727636731,1727637424,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3414384013833697 threshold 0.5231151004892054,50,0.3414384013833697,0.5231151004892054,0.25144356005788715,0,None,i7186,686,2.2833037562797897e-05 1727636749,1727637443,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514399421128799,0,None,i7186,687,2.2568937001418663e-05 1727636930,1727637629,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251363965267728,0,None,i7186,692,2.25638895641159e-05 1727636990,1727637685,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25139290882778587,0,None,i7186,688,2.257183313966337e-05 1727637131,1727637819,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25143753014954173,0,None,i7186,681,2.2570475328922794e-05 1727637171,1727637868,697,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25138084901109503,0,None,i7186,689,2.256840669856701e-05 1727637191,1727637893,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137361312108053,0,None,i7186,695,2.2571631420161224e-05 1727637291,1727637913,622,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25142908827785815,0,None,i7181,615,2.2568215672560853e-05 1727637262,1727637961,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514061746261457,0,None,i7186,692,2.257657701341299e-05 1727637291,1727637997,706,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25142908827785815,0,None,i7186,699,2.2568215672560853e-05 1727637412,1727637997,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 94 recent_samples_proportion 0.5091589198387483 threshold 0.5258226755790714,94,0.5091589198387483,0.5258226755790714,0.26493367100820064,0,None,i7181,578,4.0652442193426796e-05 1727637413,1727638038,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513567293777135,0,None,i7181,619,2.2572670995517856e-05 1727637432,1727638048,616,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135914134105164,0,None,i7181,609,2.2569743130878984e-05 1727637383,1727638079,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513675832127351,0,None,i7186,689,2.256644480167337e-05 1727637511,1727638102,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25146406174626146,0,None,i7181,584,2.26315559632986e-05 1727637432,1727638118,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3319582541554261 threshold 0.5211622494830332,50,0.3319582541554261,0.5211622494830332,0.2527231066087795,0,None,i7186,679,2.26250617857362e-05 1727637531,1727638120,589,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25132296189097925,0,None,i7181,582,2.2563635739118698e-05 1727637564,1727638144,580,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133019778099375,0,None,i7181,573,2.260213804543032e-05 1727637503,1727638184,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25141582247949834,0,None,i7186,674,2.2556532709933578e-05 1727637685,1727638351,666,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514242643511818,0,None,i7186,659,2.256156682658729e-05 1727637726,1727638382,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3388911924586947 threshold 0.5211656201019664,50,0.3388911924586947,0.5211656201019664,0.25145682585624696,0,None,i7186,649,2.2765553957368285e-05 1727637861,1727638516,655,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25142667631452,0,None,i7186,647,2.2575314283160816e-05 1727637921,1727638577,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513060781476122,0,None,i7186,649,2.2589698778159495e-05 1727638066,1727638731,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513760250844187,0,None,i7186,657,2.2570093210903845e-05 1727638166,1727638859,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25153642064640613,0,None,i7186,685,2.2581072186449302e-05 1727638357,1727638993,636,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 69 recent_samples_proportion 0.7362679142289114 threshold 0.5879193437879183,69,0.7362679142289114,0.5879193437879183,0.25889652677279307,0,None,i7186,629,3.044196288623365e-05 1727638437,1727639005,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 232 recent_samples_proportion 0.8584471366047023 threshold 0.5026368643402982,232,0.8584471366047023,0.5026368643402982,0.28455981669078634,0,None,i7186,561,9.399483008884484e-05 1727638437,1727639041,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513543174143753,0,None,i7181,596,2.256865067726814e-05 1727638377,1727639064,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4341151000735881 threshold 0.5,50,0.4341151000735881,0.5,0.2518077665219489,0,None,i7186,680,2.2582441870850502e-05 1727638457,1727639068,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25132175590931016,0,None,i7181,604,2.2567876591567697e-05 1727638457,1727639075,618,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25132175590931016,0,None,i7181,612,2.2567876591567697e-05 1727638397,1727639081,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3348771035825129 threshold 0.5173707031393673,50,0.3348771035825129,0.5173707031393673,0.2512180414857694,0,None,i7186,676,2.2685972877035108e-05 1727638397,1727639084,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4492209544543231 threshold 0.5,50,0.4492209544543231,0.5,0.2507006753497347,0,None,i7186,680,2.2747525776784406e-05 1727638411,1727639094,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137964302942595,0,None,i7186,676,2.2564313430166996e-05 1727638471,1727639099,628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8286242499292555 threshold 0.5973747543983585,50,0.8286242499292555,0.5973747543983585,0.2506886155330439,0,None,i7181,622,2.279062137653584e-05 1727638577,1727639198,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513398456343463,0,None,i7181,613,2.2621067031626624e-05 1727638577,1727639203,626,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513398456343463,0,None,i7181,619,2.2621067031626624e-05 1727638558,1727639220,662,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4336673998071624 threshold 0.5,50,0.4336673998071624,0.5,0.25191871683550415,0,None,i7186,656,2.2574206733297244e-05 1727638677,1727639227,550,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 89 recent_samples_proportion 0.42632930149239123 threshold 0.5,89,0.42632930149239123,0.5,0.2618572117703811,0,None,i7181,543,3.87151845233244e-05 1727638652,1727639313,661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.5143817401161495 threshold 0.7251665549250768,51,0.5143817401161495,0.7251665549250768,0.25413169319826334,0,None,i7186,654,2.465291948893181e-05 1727638779,1727639449,670,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513567293777135,0,None,i7186,663,2.2587969786703974e-05 1727638918,1727639493,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 128 recent_samples_proportion 0.39359446549788124 threshold 0.6607169561088085,128,0.39359446549788124,0.6607169561088085,0.272392667631452,0,None,i7186,567,5.453160981291889e-05 1727639065,1727639733,668,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513820549927641,0,None,i7186,661,2.2573891497207638e-05 1727639125,1727639798,673,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.45635134007021705 threshold 0.5,50,0.45635134007021705,0.5,0.25225759768451517,0,None,i7186,665,2.2645481536404966e-05 1727639165,1727639844,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4430115326997509 threshold 0.5,50,0.4430115326997509,0.5,0.25152918475639174,0,None,i7186,672,2.2599623427872655e-05 1727639407,1727640032,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513350217076701,0,None,i7181,618,2.256567103516027e-05 1727639347,1727640034,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.42743806372297066 threshold 0.5,50,0.42743806372297066,0.5,0.25103955619874574,0,None,i7186,679,2.267176982310759e-05 1727639428,1727640054,626,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5280799234737081,50,1,0.5280799234737081,0.25137240713941145,0,None,i7181,619,2.2575875839174006e-05 1727639367,1727640065,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513844669561023,0,None,i7186,691,2.2568183959848346e-05 1727639367,1727640068,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41075049479074677 threshold 0.5,50,0.41075049479074677,0.5,0.2519573082489146,0,None,i7186,694,2.2673904768120702e-05 1727639407,1727640082,675,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 58 recent_samples_proportion 0.5785717002507298 threshold 0.5581800854754218,58,0.5785717002507298,0.5581800854754218,0.2538579353593825,0,None,i7186,668,2.6037009700839278e-05 1727639379,1727640086,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25143029425952723,0,None,i7186,700,2.2573701099734663e-05 1727639559,1727640201,642,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515677761698022,0,None,i7181,634,2.2593063010798346e-05 1727639567,1727640206,639,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513627592860589,0,None,i7181,633,2.256952039216032e-05 1727639527,1727640225,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25144235407621807,0,None,i7186,690,2.2604978718373065e-05 1727639587,1727640227,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25132175590931016,0,None,i7181,633,2.2559544862577455e-05 1727639547,1727640235,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25138687891944045,0,None,i7186,680,2.2566646152208725e-05 1727639647,1727640272,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514025566811384,0,None,i7181,618,2.2574019111638166e-05 1727639647,1727640276,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514025566811384,0,None,i7181,622,2.2574019111638166e-05 1727639788,1727640468,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514315002411963,0,None,i7186,673,2.2555567924598317e-05 1727639848,1727640533,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25142305836951273,0,None,i7186,678,2.2565808080182617e-05 1727639892,1727640577,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25143632416787265,0,None,i7186,678,2.2594202616696235e-05 1727640103,1727640792,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514520019295706,0,None,i7186,681,2.2566804820291138e-05 1727640128,1727640816,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5727944828002781,50,1,0.5727944828002781,0.2515750120598167,0,None,i7186,681,2.268774951737583e-05 1727640253,1727640971,718,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513531114327062,0,None,i7186,711,2.2574283718178123e-05 1727640285,1727640991,706,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.574189837261398,50,1,0.574189837261398,0.25154124457308247,0,None,i7186,699,2.263937191491112e-05 1727640285,1727640994,709,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135190545103714,0,None,i7186,702,2.257018860831631e-05 1727640315,1727641013,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513205499276411,0,None,i7186,692,2.256795082409688e-05 1727640315,1727641019,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513205499276411,0,None,i7186,697,2.256795082409688e-05 1727640525,1727641127,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513627592860589,0,None,i7174,570,2.257507972965454e-05 1727640513,1727641128,615,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34700736046509695 threshold 0.5191072330242864,50,0.34700736046509695,0.5191072330242864,0.25082609744331885,0,None,i7174,571,2.2779262987690025e-05 1727640525,1727641128,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513627592860589,0,None,i7174,571,2.257507972965454e-05 1727640556,1727641132,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137361312108053,0,None,i7174,570,2.259528266117101e-05 1727640496,1727641180,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3772755446470414 threshold 0.6346939508163618,50,0.3772755446470414,0.6346939508163618,0.25188253738543176,0,None,i7186,677,2.3020811321248096e-05 1727640633,1727641217,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 52 recent_samples_proportion 0.705545808622103 threshold 0.7042484127889296,52,0.705545808622103,0.7042484127889296,0.25416666666666665,0,None,i7181,577,2.4430381596215992e-05 1727640646,1727641231,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25148697539797393,0,None,i7181,579,2.2581336103145298e-05 1727640556,1727641240,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513748191027496,0,None,i7186,676,2.257294703898826e-05 1727640586,1727641269,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25147250361794504,0,None,i7186,676,2.257805417680963e-05 1727640693,1727641275,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514134105161602,0,None,i7181,575,2.258169470796937e-05 1727640856,1727641523,667,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514327062228654,0,None,i7186,660,2.256938256114862e-05 1727640889,1727641561,672,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514097925711529,0,None,i7186,665,2.256523556050025e-05 1727641036,1727641730,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2519766039556198,0,None,i7186,687,2.276720840188486e-05 1727641137,1727641827,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513904968644477,0,None,i7186,683,2.2581715157486063e-05 1727641161,1727641851,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32600398784620577 threshold 0.5185367900891995,50,0.32600398784620577,0.5185367900891995,0.2513904968644477,0,None,i7186,682,2.2682320534092647e-05 1727641177,1727641866,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251363965267728,0,None,i7186,682,2.2569446145920765e-05 1727641192,1727641879,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2520803183791607,0,None,i7186,680,2.2700034783270773e-05 1727641357,1727641965,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 91 recent_samples_proportion 0.4079751861281693 threshold 0.5725898454897106,91,0.4079751861281693,0.5725898454897106,0.2633116256632899,0,None,i7186,602,3.953270018421904e-05 1727641377,1727641982,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513121080559575,0,None,i7181,598,2.256291513599763e-05 1727641397,1727641997,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513977327544621,0,None,i7181,594,2.2572926058344534e-05 1727641337,1727642024,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513567293777135,0,None,i7186,681,2.2582404191093106e-05 1727641373,1727642053,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137964302942595,0,None,i7186,674,2.2575430224565405e-05 1727641553,1727642167,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513277858176556,0,None,i7181,607,2.2588361357788393e-05 1727641577,1727642197,620,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135914134105164,0,None,i7181,613,2.2564186475956627e-05 1727641537,1727642202,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133622768933916,0,None,i7186,657,2.2580885151920412e-05 1727641597,1727642210,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25148456343463577,0,None,i7181,606,2.2574529726578982e-05 1727641577,1727642251,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32980875608274896 threshold 0.520559175718262,50,0.32980875608274896,0.520559175718262,0.25174023154848046,0,None,i7186,667,2.26592777792005e-05 1727641676,1727642278,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135914134105164,0,None,i7181,595,2.2575302523238336e-05 1727641676,1727642290,614,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135914134105164,0,None,i7181,607,2.2575302523238336e-05 1727641796,1727642481,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133863965267733,0,None,i7186,678,2.256961581896341e-05 1727641898,1727642583,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.12363110216871866 threshold 0.5444349619894201,50,0.12363110216871866,0.5444349619894201,0.25194042450554754,0,None,i7186,678,2.295653879267257e-05 1727642018,1727642612,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 105 recent_samples_proportion 0.46362540731206536 threshold 0.7084912454709411,105,0.46362540731206536,0.7084912454709411,0.26828991799324653,0,None,i7186,587,4.5672617688436836e-05 1727641998,1727642681,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513917028461168,0,None,i7186,676,2.2567738482411018e-05 1727642037,1727642721,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3278893661155632 threshold 0.5192966177598527,50,0.3278893661155632,0.5192966177598527,0.2509792571152919,0,None,i7186,677,2.273027078975702e-05 1727642158,1727642842,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513109020742885,0,None,i7186,677,2.2588009915083197e-05 1727642178,1727642866,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251319343945972,0,None,i7186,681,2.2561081476213586e-05 1727642188,1727642876,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25134225759768447,0,None,i7186,681,2.2597215324102225e-05 1727642218,1727642895,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32773827647527815 threshold 0.5198099924302245,50,0.32773827647527815,0.5198099924302245,0.2504884225759768,0,None,i7186,670,2.2726888270143e-05 1727642403,1727642997,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.33444573239148134 threshold 0.5202706604699966,50,0.33444573239148134,0.5202706604699966,0.25111794500723594,0,None,i7181,588,2.2696376539546065e-05 1727642423,1727643022,599,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32986530102740796 threshold 0.520325837989092,50,0.32986530102740796,0.520325837989092,0.25112518089725033,0,None,i7181,593,2.2666479122507947e-05 1727642363,1727643042,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5084381713436884 threshold 0.5569939802387508,50,0.5084381713436884,0.5569939802387508,0.2512071876507477,0,None,i7186,671,2.2737277827970942e-05 1727642399,1727643059,660,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.753471114222934,50,0.1,0.753471114222934,0.25345875542691754,0,None,i7181,653,2.4568665650377557e-05 1727642566,1727643161,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5667923996850011,50,1,0.5667923996850011,0.25243728895320794,0,None,i7181,588,2.277964539839904e-05 1727642580,1727643178,598,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 55 recent_samples_proportion 0.17847142526879906 threshold 0.6061619845218956,55,0.17847142526879906,0.6061619845218956,0.25342981186685964,0,None,i7181,591,2.5162536560299532e-05 1727642546,1727643273,727,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.10126274536828829 threshold 0.7526753199620813,50,0.10126274536828829,0.7526753199620813,0.2553051133622769,0,None,i7186,719,2.4394885352727167e-05 1727642686,1727643372,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514749155812832,0,None,i7186,679,2.2576514719068303e-05 1727642701,1727643385,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513555233960444,0,None,i7186,676,2.2571355474687512e-05 1727642811,1727643504,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513917028461168,0,None,i7186,685,2.2573297380984186e-05 1727642831,1727643525,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251488181379643,0,None,i7186,686,2.2609124414601515e-05 1727642883,1727643565,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.3642456153455388 threshold 0.6723573746143168,51,0.3642456153455388,0.6723573746143168,0.2526808972503618,0,None,i7186,675,2.38059555373799e-05 1727642995,1727643676,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31756493854383905 threshold 0.5183572167331217,50,0.31756493854383905,0.5183572167331217,0.25209479015918956,0,None,i7186,674,2.2648550752552272e-05 1727643015,1727643711,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3198927774143264 threshold 0.5196884922464999,50,0.3198927774143264,0.5196884922464999,0.2517667631452002,0,None,i7186,688,2.2701178840166063e-05 1727643033,1727643737,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133019778099375,0,None,i7186,696,2.2568746151734394e-05 1727643184,1727643761,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513760250844187,0,None,i7181,571,2.2570093210903845e-05 1727643175,1727643782,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515617462614568,0,None,i7181,598,2.2569775332910078e-05 1727643154,1727643794,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 69 recent_samples_proportion 0.43438282888371327 threshold 0.6835870238950849,69,0.43438282888371327,0.6835870238950849,0.2598227206946454,0,None,i7186,633,3.132208836113989e-05 1727643365,1727643934,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.1820185050368317 threshold 0.508170366846027,62,0.1820185050368317,0.508170366846027,0.25476845151953686,0,None,i7181,562,2.7701245328074297e-05 1727643357,1727643952,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3373475896957437 threshold 0.5243209342028756,50,0.3373475896957437,0.5243209342028756,0.2511722141823445,0,None,i7181,588,2.280306278107395e-05 1727643335,1727644030,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513326097443319,0,None,i7186,688,2.2568597677536906e-05 1727643395,1727644083,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251441148094549,0,None,i7186,680,2.2585551572675007e-05 1727643494,1727644182,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133140376266283,0,None,i7186,681,2.2564504877723788e-05 1727643514,1727644201,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25154124457308247,0,None,i7186,679,2.259191284560309e-05 1727643534,1727644222,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25157018813314036,0,None,i7186,681,2.2609644608625646e-05 1727643637,1727644230,593,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.6184996284544468 threshold 0.5138409595936537,50,0.6184996284544468,0.5138409595936537,0.2515726000964785,0,None,i7181,586,2.266684477353628e-05 1727643694,1727644255,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 154 recent_samples_proportion 0.3447868466049706 threshold 0.5579959534745359,154,0.3447868466049706,0.5579959534745359,0.2783924264351182,0,None,i7186,553,6.39245204406628e-05 1727643634,1727644312,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.28571009123697877 threshold 0.5231757217086852,50,0.28571009123697877,0.5231757217086852,0.2506874095513748,0,None,i7186,670,2.2721607860959177e-05 1727643754,1727644361,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 85 recent_samples_proportion 0.5722459741868079 threshold 0.5361120420508086,85,0.5722459741868079,0.5361120420508086,0.26347322720694644,0,None,i7186,600,3.696030151276953e-05 1727643930,1727644521,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513687891944042,0,None,i7181,585,2.2562203952579687e-05 1727643879,1727644553,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3519600382693079 threshold 0.5245321497946327,50,0.3519600382693079,0.5245321497946327,0.2515231548480463,0,None,i7186,667,2.2714872561856722e-05 1727643999,1727644585,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514194404245056,0,None,i7181,577,2.256464162714931e-05 1727643909,1727644587,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25151350699469366,0,None,i7186,670,2.257135645341702e-05 1727644030,1727644608,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25144356005788715,0,None,i7181,572,2.256176786445256e-05 1727644107,1727644719,612,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 90 recent_samples_proportion 0.7060299530473402 threshold 0.7400506161563039,90,0.7060299530473402,0.7400506161563039,0.2655836951278341,0,None,i7186,605,3.995643314299636e-05 1727644127,1727644811,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515110950313555,0,None,i7186,677,2.2586808677549522e-05 1727644229,1727644910,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514773275446214,0,None,i7186,674,2.257636613511351e-05 1727644209,1727644924,715,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514664737095996,0,None,i7186,706,2.2593736841521445e-05 1727644389,1727645082,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.34109937616134545 threshold 0.5242044011339203,50,0.34109937616134545,0.5242044011339203,0.25151471297636274,0,None,i7186,686,2.2701323630253762e-05 1727644423,1727645124,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513398456343463,0,None,i7186,692,2.2568152254944456e-05 1727644429,1727645131,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25130246020260494,0,None,i7186,695,2.257184306902943e-05 1727644450,1727645146,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513567293777135,0,None,i7186,689,2.2571281223876707e-05 1727644626,1727645233,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31686736491520545 threshold 0.5244226353685911,50,0.31686736491520545,0.5244226353685911,0.2518499758803666,0,None,i7181,599,2.2649684678022292e-05 1727644574,1727645262,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25138084901109503,0,None,i7186,681,2.2571185887865523e-05 1727644604,1727645293,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.32258261841613617 threshold 0.5254191560548556,50,0.32258261841613617,0.5254191560548556,0.25059334298118674,0,None,i7186,681,2.276827617196977e-05 1727644867,1727645479,612,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514182344428365,0,None,i7181,605,2.2559160793518124e-05 1727644867,1727645498,631,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514182344428365,0,None,i7181,625,2.2559160793518124e-05 1727644887,1727645507,620,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5824557464576515,50,1,0.5824557464576515,0.25166304872165945,0,None,i7181,614,2.2664053353190167e-05 1727644816,1727645514,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514761215629523,0,None,i7186,690,2.25597634627865e-05 1727644846,1727645534,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513904968644477,0,None,i7186,681,2.2573371645512117e-05 1727644987,1727645659,672,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 52 recent_samples_proportion 0.739283017293396 threshold 0.7054169821165369,52,0.739283017293396,0.7054169821165369,0.2540569223347805,0,None,i7186,665,2.4331542971458845e-05 1727645107,1727645685,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25136637723106614,0,None,i7181,571,2.2569297653441656e-05 1727644997,1727645688,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25133743367100825,0,None,i7186,683,2.2565522579239903e-05 1727645087,1727645758,671,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25144356005788715,0,None,i7186,665,2.2570104006283135e-05 1727645167,1727645847,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5770825895189879,50,1,0.5770825895189879,0.2525530631934395,0,None,i7186,673,2.2968534529038874e-05 1727645298,1727645984,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25134346357935355,0,None,i7186,679,2.2580439482419648e-05 1727645318,1727646014,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.31786556634390073 threshold 0.5240300442458357,50,0.31786556634390073,0.5240300442458357,0.25205861070911717,0,None,i7186,689,2.2804666145640497e-05 1727645338,1727646030,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515231548480463,0,None,i7186,684,2.2639090829597964e-05 1727645480,1727646056,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513265798359865,0,None,i7181,570,2.259678885546579e-05 1727645469,1727646059,590,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515460684997588,0,None,i7181,583,2.2723312280071948e-05 1727645449,1727646145,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513567293777135,0,None,i7186,689,2.256433493187699e-05 1727645540,1727646245,705,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25177761698022194,0,None,i7186,698,2.2588480786854422e-05 1727645712,1727646401,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251672696575012,0,None,i7186,681,2.2664858761271392e-05 1727645732,1727646419,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25138808490110953,0,None,i7186,679,2.257352017456798e-05 1727645751,1727646442,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513953207911239,0,None,i7186,684,2.2577245517664143e-05 1727645772,1727646452,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514749155812832,0,None,i7186,672,2.260297048443541e-05 1727646009,1727646686,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514097925711529,0,None,i7186,669,2.2577744776394583e-05 1727646029,1727646709,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5852545282044752,50,1,0.5852545282044752,0.25179932465026533,0,None,i7186,673,2.2690720354815248e-05 1727646049,1727646735,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513398456343463,0,None,i7186,679,2.2558431785723153e-05 1727646069,1727646754,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514134105161602,0,None,i7186,678,2.2565012835493646e-05 1727646374,1727646976,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515303907380608,0,None,i7181,594,2.257587876765807e-05 1727646374,1727646977,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515303907380608,0,None,i7181,595,2.257587876765807e-05 1727646395,1727646986,591,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 56 recent_samples_proportion 0.7695162025095169 threshold 0.7643954439312542,56,0.7695162025095169,0.7643954439312542,0.2576760733236855,0,None,i7181,584,2.706862552158028e-05 1727646314,1727647008,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513832609744332,0,None,i7186,687,2.25682582060879e-05 1727646355,1727647024,669,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 52 recent_samples_proportion 0.7585996119417631 threshold 0.723681730896888,52,0.7585996119417631,0.723681730896888,0.25446333815725997,0,None,i7186,662,2.4617102861889408e-05 1727646415,1727647086,671,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25140858658948384,0,None,i7186,664,2.2568088439012137e-05 1727646655,1727647232,577,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25134225759768447,0,None,i7181,571,2.257634222662938e-05 1727646595,1727647263,668,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.4217536242678762 threshold 0.5135955701582133,50,0.4217536242678762,0.5135955701582133,0.24953449107573566,0,None,i7186,662,2.2785885827211533e-05 1727646615,1727647292,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3241848630240389 threshold 0.5241966179664368,50,0.3241848630240389,0.5241966179664368,0.25076097443318857,0,None,i7186,670,2.2794618199933602e-05 1727646626,1727647306,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515110950313555,0,None,i7186,673,2.258263289483968e-05 1727646735,1727647413,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25131452001929566,0,None,i7186,671,2.256554400520823e-05 1727646775,1727647445,670,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25145682585624696,0,None,i7186,663,2.2583193860353196e-05 1727646949,1727647518,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 158 recent_samples_proportion 0.6487303814520364 threshold 0.5328224759619847,158,0.6487303814520364,0.5328224759619847,0.2766751085383502,0,None,i7186,561,6.598692391290967e-05 1727646969,1727647556,587,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25136517124939706,0,None,i7181,580,2.2572151207839537e-05 1727646989,1727647595,606,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25151833092137,0,None,i7181,599,2.2583578706677492e-05 1727646949,1727647618,669,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514242643511818,0,None,i7186,661,2.256295565803245e-05 1727647140,1727647832,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.2523174007389655 threshold 0.5435084297677192,50,0.2523174007389655,0.5435084297677192,0.2521140858658948,0,None,i7186,685,2.2836791083022734e-05 1727647170,1727647857,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.36145615427408195 threshold 0.6418541477917734,50,0.36145615427408195,0.6418541477917734,0.25244934876989866,0,None,i7186,680,2.324536569867847e-05 1727647189,1727647865,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 52 recent_samples_proportion 0.20242661787196994 threshold 0.5504241973161698,52,0.20242661787196994,0.5504241973161698,0.25245658465991316,0,None,i7186,669,2.3621979858851277e-05 1727647321,1727647988,667,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25145803183791604,0,None,i7186,660,2.2598436747934574e-05 1727647351,1727648023,672,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514761215629523,0,None,i7186,665,2.2575049738386515e-05 1727647370,1727648051,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25141461649782926,0,None,i7186,675,2.256354955482239e-05 1727647590,1727648283,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.13493528040120276 threshold 0.6605404611676791,51,0.13493528040120276,0.6605404611676791,0.25245899662325133,0,None,i7186,684,2.3902980994877225e-05 1727647623,1727648299,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 56 recent_samples_proportion 0.13366255328581347 threshold 0.6353954189113487,56,0.13366255328581347,0.6353954189113487,0.25371924746743846,0,None,i7186,669,2.561644500657434e-05 1727647650,1727648337,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3698568024366017 threshold 0.5245821388911907,50,0.3698568024366017,0.5245821388911907,0.2501760733236855,0,None,i7186,680,2.2705482509706967e-05 1727647671,1727648363,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3726513021056803 threshold 0.524496365581944,50,0.3726513021056803,0.524496365581944,0.2511058851905451,0,None,i7186,684,2.271117401259047e-05 1727648046,1727648618,572,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513410516160154,0,None,i7181,562,2.257780683581128e-05 1727647931,1727648633,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2514025566811384,0,None,i7186,695,2.258653782332741e-05 1727647971,1727648664,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37388826111173945 threshold 0.5248960813856359,50,0.37388826111173945,0.5248960813856359,0.25124457308248915,0,None,i7186,686,2.2775908697188362e-05 1727647986,1727648675,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515593342981187,0,None,i7186,682,2.2600551734381954e-05 1727648011,1727648692,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137843704775686,0,None,i7186,674,2.256577666778468e-05 1727648031,1727648722,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25137120115774236,0,None,i7186,683,2.2574559877959025e-05 1727648291,1727648831,540,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 86 recent_samples_proportion 0.7445149494335055 threshold 0.57028633588925,86,0.7445149494335055,0.57028633588925,0.2611082971538833,0,None,i7181,533,3.762665350717373e-05 1727648252,1727648926,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.251396526772793,0,None,i7186,667,2.261059414280323e-05 1727648288,1727648959,671,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3850110057556645 threshold 0.522602750613219,50,0.3850110057556645,0.522602750613219,0.25036661842739993,0,None,i7186,664,2.272879865136456e-05 1727648318,1727648978,660,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25142788229618906,0,None,i7186,652,2.2579412106186773e-05 1727648348,1727649009,661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.39117255348023783 threshold 0.5227666275474028,50,0.39117255348023783,0.5227666275474028,0.25052821997105645,0,None,i7186,654,2.279915978188524e-05 1727648589,1727649181,592,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2515665701881331,0,None,i7181,585,2.2601499526727893e-05 1727648559,1727649148,589,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.39312296191360296 threshold 0.5242526797101484,50,0.39312296191360296,0.5242526797101484,0.24991196333815724,0,None,i7181,582,2.2827483450831837e-05 1727648612,1727649195,583,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.396527269384646 threshold 0.5244105698363308,50,0.396527269384646,0.5244105698363308,0.25162083936324164,0,None,i7181,577,2.2669470275536963e-05 1727648552,1727649200,648,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.39662997339355743 threshold 0.524016282386301,50,0.39662997339355743,0.524016282386301,0.2505945489628557,0,None,i7186,641,2.2711896757530958e-05 1727648672,1727649327,655,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25139411480945495,0,None,i7186,648,2.2602377864500186e-05 1727648952,1727649548,596,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 78 recent_samples_proportion 0.7937296350486577 threshold 0.5833419759757816,78,0.7937296350486577,0.5833419759757816,0.259878195851423,0,None,i7186,588,3.424321346126957e-05 1727648932,1727649578,646,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3971095118681721 threshold 0.5245558721933721,50,0.3971095118681721,0.5245558721933721,0.2510009647853353,0,None,i7186,639,2.274160350958005e-05 1727648972,1727649629,657,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25139411480945495,0,None,i7186,650,2.2591234081502775e-05 1727649033,1727649658,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.5647783229127527 threshold 0.5226066684350371,62,0.5647783229127527,0.5226066684350371,0.254815484804631,0,None,i7186,619,2.76344261632515e-05 1727649012,1727649669,657,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25135793535938256,0,None,i7186,650,2.2573986678358153e-05 1727649348,1727650030,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 51 recent_samples_proportion 0.14792392617091538 threshold 0.5398801841773093,51,0.14792392617091538,0.5398801841773093,0.251794500723589,0,None,i7186,675,2.334640259451224e-05 1727649466,1727650031,565,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41965081445264496 threshold 0.5256692649205656,50,0.41965081445264496,0.5256692649205656,0.2518885672937772,0,None,i7181,556,2.2754283423802947e-05 1727649388,1727650066,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2513977327544621,0,None,i7186,671,2.2565977977188925e-05 1727649406,1727650073,667,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 54 recent_samples_proportion 0.1741694763302803 threshold 0.5462224752642214,54,0.1741694763302803,0.5462224752642214,0.2532633863965268,0,None,i7186,660,2.468165218052194e-05 1727649428,1727650110,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.25153400868306797,0,None,i7186,675,2.2574265052541338e-05 1727649448,1727650126,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.40787421656815703 threshold 0.5254861807350322,50,0.40787421656815703,0.5254861807350322,0.2515159189580318,0,None,i7186,666,2.2668948967391718e-05 1727649752,1727650293,541,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 291 recent_samples_proportion 0.9208488426172211 threshold 0.7352043650112943,291,0.9208488426172211,0.7352043650112943,0.29120477568740954,0,None,i7186,533,0.00012156052951209428 1727649768,1727650323,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 257 recent_samples_proportion 0.5374224614351988 threshold 0.6295345756225288,257,0.5374224614351988,0.6295345756225288,0.2873576941630487,0,None,i7186,548,0.00010565732977081185 1727649703,1727650344,641,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41663746068997154 threshold 0.5272534216837114,50,0.41663746068997154,0.5272534216837114,0.25204775687409553,0,None,i7186,634,2.262205391912355e-05 1727649732,1727650379,647,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5720227322541177 threshold 0.5501747485250235,50,0.5720227322541177,0.5501747485250235,0.2501435118186204,0,None,i7186,640,2.276511525637795e-05 1727649792,1727650434,642,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.41732534435884094 threshold 0.5273183038938457,50,0.41732534435884094,0.5273183038938457,0.2512397491558128,0,None,i7186,635,2.276631052024498e-05
timestamp,ram_usage_mb,cpu_usage_percent 1727627245,473.546875,49.7 1727627245,473.5859375,47.2 1727627246,473.65234375,49.9 1727627246,473.65234375,39.4 1727627246,473.65234375,56.5 1727627246,473.65234375,49.6 1727627246,473.65234375,40.0 1727627292,478.390625,49.8 1727627292,478.390625,55.6 1727627292,478.390625,49.6 1727627292,478.390625,57.8 1727627293,478.390625,49.7 1727627293,478.390625,36.4 1727627293,478.390625,53.4 1727627293,478.390625,41.2 1727627295,478.390625,49.8 1727627295,478.390625,37.5 1727627295,478.390625,55.5 1727627295,478.390625,39.4 1727627296,478.390625,49.7 1727627296,478.390625,56.2 1727627296,478.390625,50.9 1727627296,478.390625,47.2 1727627297,478.390625,49.7 1727627297,478.390625,54.3 1727627297,478.390625,49.5 1727627297,478.390625,40.6 1727627299,478.390625,49.8 1727627299,478.390625,39.4 1727627299,478.390625,48.6 1727627299,478.390625,57.8 1727627300,478.390625,49.7 1727627300,478.390625,56.5 1727627300,478.390625,46.8 1727627300,478.390625,56.5 1727627302,478.390625,49.7 1727627302,478.390625,56.5 1727627302,478.390625,49.5 1727627302,478.390625,53.2 1727627303,478.390625,49.7 1727627303,478.390625,47.7 1727627303,478.390625,50.5 1727627303,478.390625,41.9 1727627304,478.390625,49.8 1727627304,478.390625,41.7 1727627304,478.390625,52.9 1727627304,478.390625,39.4 1727627306,478.390625,49.7 1727627306,478.390625,38.2 1727627306,478.390625,54.2 1727627306,478.390625,39.4 1727627307,478.390625,49.8 1727627307,478.390625,36.4 1727627307,478.390625,54.2 1727627307,478.390625,40.6 1727627308,478.390625,49.7 1727627308,478.390625,35.3 1727627308,478.390625,54.2 1727627308,478.390625,38.7 1727627310,478.390625,49.8 1727627310,478.390625,52.3 1727627310,478.390625,49.6 1727627310,478.390625,54.5 1727627311,478.390625,49.8 1727627311,478.390625,52.3 1727627311,478.390625,49.5 1727627311,478.390625,40.0 1727627313,478.40625,49.7 1727627313,478.40625,54.3 1727627313,478.40625,48.1 1727627313,478.40625,57.8 1727627314,478.40625,49.7 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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 (2024-09-29 18:27:38,570) - Starting with JobEnvironment(job_id=2300383, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-29 18:27:38,572) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/DiscriminativeDriftDetector2019_PokerHand/0/single_runs/2300383/2300383_submitted.pkl
Parameters: {"n_reference_samples": 444, "recent_samples_proportion": 0.3054295971989632, "threshold": 0.7986467182636261}
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 PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 444 recent_samples_proportion 0.3054295971989632 threshold 0.7986467182636261
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--
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Last access: 1727626340.0
Last modification: 1724759185.0
Hostname: i7186
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: main_omniopt.py
UID: 2063769
GID: 200270
Status-Change-Time: 1727277242.0
Size: 1729 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727626340.0
Last modification: 1727277242.0
Hostname: i7186
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 DiscriminativeDriftDetector2019 n_reference_samples 444 recent_samples_proportion 0.3054295971989632 threshold 0.7986467182636261
stdout:
RESULT: 0.305520984081042
OO-Info: runtime: 549
OO-Info: lpd: 0.0001791505131507322
Result: 0.305520984081042
EXIT_CODE: 0
submitit INFO (2024-09-29 18:36:57,247) - Job completed successfully
submitit INFO (2024-09-29 18:36:57,250) - Exiting after successful completion
Job-Infos
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}
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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: :-webkit-scrollbar-button: vertical: end{
height: 17px;
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}
: :-webkit-scrollbar-button: horizontal: start{
width: 17px;
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}
: :-webkit-scrollbar-button: horizontal: end{
width: 17px;
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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
menu[role=tablist] button{
background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
margin-left: -1px;
margin-right: 2px;
border-radius: 0;
border-color: #91a7b4;
border-top-right-radius: 3px;
border-top-left-radius: 3px;
padding: 0 12px 3px
}
menu[role=tablist] button: hover{
box-shadow: unset;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]{
border-color: #919b9c;
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]: first-of-type: before{
content: "";
display: block;
position: absolute;
z-index: -1;
top: 100%;
left: -1px;
height: 2px;
width: 0;
border-left: 1px solid #919b9c
}
[role=tabpanel]{
box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
}
ul.tree-view{
-webkit-font-smoothing: auto;
border: 1px solid #7f9db9;
padding: 2px 5px
}
@keyframes sliding{
0%{
transform: translateX(-30px)
}
to{
transform: translateX(100%)
}
}
progress{
box-sizing: border-box;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
height: 14px;
border: 1px solid #686868;
border-radius: 4px;
padding: 1px 2px 1px 0;
overflow: hidden;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress,progress: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
height: 14px
}
progress[value]: :-webkit-progress-bar{
background-color: transparent
}
progress[value]: :-webkit-progress-value{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress[value]: :-moz-progress-bar{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-webkit-progress-bar{
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite
}
progress: not([value]): :-webkit-progress-bar: not([value]){
animation: sliding 2s linear 0s infinite;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]){
position: relative
}
progress: not([value]): before{
box-sizing: border-box;
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before,progress: not([value]): before: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): after{
box-sizing: border-box;
content: "";
position: absolute;
top: 1px;
left: 2px;
width: 100%;
height: calc(100% - 2px);
padding: 1px 2px;
border-radius: 2px;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): after,progress: not([value]): after: not([value]){
animation: sliding 2s linear 0s infinite
}
progress: not([value]): after: not([value]){
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-moz-progress-bar{
width: 100%;
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var tab_main_worker_cpu_ram_headers_json = [
"timestamp",
"ram_usage_mb",
"cpu_usage_percent"
];
"use strict";
function add_default_layout_data (layout) {
layout["width"] = get_graph_width();
layout["height"] = get_graph_height();
layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
return layout;
}
function get_marker_size() {
return 12;
}
function get_text_color() {
return theme == "dark" ? "white" : "black";
}
function get_font_size() {
return 14;
}
function get_graph_height() {
return 800;
}
function get_font_data() {
return {
size: get_font_size(),
color: get_text_color()
}
}
function get_axis_title_data(name, axis_type = "") {
if(axis_type) {
return {
text: name,
type: axis_type,
font: get_font_data()
};
}
return {
text: name,
font: get_font_data()
};
}
function get_graph_width() {
var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
return Math.max(800, Math.floor(width * 0.9));
}
function createTable(data, headers, table_name) {
if (!$("#" + table_name).length) {
console.error("#" + table_name + " not found");
return;
}
new gridjs.Grid({
columns: headers,
data: data,
search: true,
sort: true
}).render(document.getElementById(table_name));
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
colorize_table_entries();
add_colorize_to_gridjs_table();
}
function download_as_file(id, filename) {
var text = $("#" + id).text();
var blob = new Blob([text], {
type: "text/plain"
});
var link = document.createElement("a");
link.href = URL.createObjectURL(blob);
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
function copy_to_clipboard_from_id (id) {
var text = $("#" + id).text();
copy_to_clipboard(text);
}
function copy_to_clipboard(text) {
if (!navigator.clipboard) {
let textarea = document.createElement("textarea");
textarea.value = text;
document.body.appendChild(textarea);
textarea.select();
try {
document.execCommand("copy");
} catch (err) {
console.error("Copy failed:", err);
}
document.body.removeChild(textarea);
return;
}
navigator.clipboard.writeText(text).then(() => {
console.log("Text copied to clipboard");
}).catch(err => {
console.error("Failed to copy text:", err);
});
}
function filterNonEmptyRows(data) {
var new_data = [];
for (var row_idx = 0; row_idx < data.length; row_idx++) {
var line = data[row_idx];
var line_has_empty_data = false;
for (var col_idx = 0; col_idx < line.length; col_idx++) {
var col_header_name = tab_results_headers_json[col_idx];
var single_data_point = line[col_idx];
if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
line_has_empty_data = true;
continue;
}
}
if(!line_has_empty_data) {
new_data.push(line);
}
}
return new_data;
}
function make_text_in_parallel_plot_nicer() {
$(".parcoords g > g > text").each(function() {
if (theme == "dark") {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "white")
.css("stroke", "black")
.css("stroke-width", "2px")
.css("paint-order", "stroke fill");
} else {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "black")
.css("stroke", "unset")
.css("stroke-width", "unset")
.css("paint-order", "stroke fill");
}
});
}
function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
if ($("#parallel-plot").data("loaded") == "true") {
return;
}
dataArray = filterNonEmptyRows(dataArray);
const ignoreSet = new Set(ignoreColumns);
const numericalCols = [];
const categoricalCols = [];
const categoryMappings = {};
headers.forEach((header, colIndex) => {
if (ignoreSet.has(header)) return;
const values = dataArray.map(row => row[colIndex]);
if (values.every(val => !isNaN(parseFloat(val)))) {
numericalCols.push({ name: header, index: colIndex });
} else {
categoricalCols.push({ name: header, index: colIndex });
const uniqueValues = [...new Set(values)];
categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
}
});
const dimensions = [];
numericalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => parseFloat(row[col.index])),
range: [
Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
Math.max(...dataArray.map(row => parseFloat(row[col.index])))
]
});
});
categoricalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
tickvals: Object.values(categoryMappings[col.name]),
ticktext: Object.keys(categoryMappings[col.name])
});
});
let colorScale = null;
let colorValues = null;
if (resultNames.length > 1) {
let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
selectBox += '<option value="none">No color</option>';
var k = 0;
resultNames.forEach(resultName => {
var minMax = result_min_max[k];
if(minMax === undefined) {
minMax = "min [automatically chosen]"
}
selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
k = k + 1;
});
selectBox += '</select>';
$("#parallel-plot").before(selectBox);
$("#result-select").change(function() {
const selectedResult = $(this).val();
if (selectedResult === "none") {
colorValues = null;
colorScale = null;
} else {
const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
let minResult = Math.min(...colorValues);
let maxResult = Math.max(...colorValues);
var _result_min_max_idx = result_names.indexOf(selectedResult);
let invertColor = false;
if (result_min_max.length > _result_min_max_idx) {
invertColor = result_min_max[_result_min_max_idx] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
}
updatePlot();
});
} else {
let invertColor = false;
if (Object.keys(result_min_max).length == 1) {
invertColor = result_min_max[0] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
}
function updatePlot() {
const trace = {
type: 'parcoords',
dimensions: dimensions,
line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
unselected: {
line: {
color: get_text_color(),
opacity: 0
}
},
};
dimensions.forEach(dim => {
if (!dim.line) {
dim.line = {};
}
if (!dim.line.color) {
dim.line.color = 'rgba(169,169,169, 0.01)';
}
});
Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
make_text_in_parallel_plot_nicer();
}
updatePlot();
$("#parallel-plot").data("loaded", "true");
make_text_in_parallel_plot_nicer();
}
function plotWorkerUsage() {
if($("#workerUsagePlot").data("loaded") == "true") {
return;
}
var data = tab_worker_usage_csv_json;
if (!Array.isArray(data) || data.length === 0) {
console.error("Invalid or empty data provided.");
return;
}
let timestamps = [];
let desiredWorkers = [];
let realWorkers = [];
for (let i = 0; i < data.length; i++) {
let entry = data[i];
if (!Array.isArray(entry) || entry.length < 3) {
console.warn("Skipping invalid entry:", entry);
continue;
}
let unixTime = parseFloat(entry[0]);
let desired = parseInt(entry[1], 10);
let real = parseInt(entry[2], 10);
if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
console.warn("Skipping invalid numerical values:", entry);
continue;
}
timestamps.push(new Date(unixTime * 1000).toISOString());
desiredWorkers.push(desired);
realWorkers.push(real);
}
let trace1 = {
x: timestamps,
y: desiredWorkers,
mode: 'lines+markers',
name: 'Desired Workers',
line: {
color: 'blue'
}
};
let trace2 = {
x: timestamps,
y: realWorkers,
mode: 'lines+markers',
name: 'Real Workers',
line: {
color: 'red'
}
};
let layout = {
title: "Worker Usage Over Time",
xaxis: {
title: get_axis_title_data("Time", "date")
},
yaxis: {
title: get_axis_title_data("Number of Workers")
},
legend: {
x: 0,
y: 1
}
};
Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
$("#workerUsagePlot").data("loaded", "true");
}
function plotCPUAndRAMUsage() {
if($("#mainWorkerCPURAM").data("loaded") == "true") {
return;
}
var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
var trace1 = {
x: timestamps,
y: cpuUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'CPU Usage (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: ramUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'RAM Usage (MB)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'CPU and RAM Usage Over Time',
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
overlaying: 'y',
side: 'right',
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var data = [trace1, trace2];
Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
$("#mainWorkerCPURAM").data("loaded", "true");
}
function plotScatter2d() {
if ($("#plotScatter2d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter2d");
var minInput = document.getElementById("minValue");
var maxInput = document.getElementById("maxValue");
if (!minInput || !maxInput) {
minInput = document.createElement("input");
minInput.id = "minValue";
minInput.type = "number";
minInput.placeholder = "Min Value";
minInput.step = "any";
maxInput = document.createElement("input");
maxInput.id = "maxValue";
maxInput.type = "number";
maxInput.placeholder = "Max Value";
maxInput.step = "any";
var inputContainer = document.createElement("div");
inputContainer.style.marginBottom = "10px";
inputContainer.appendChild(minInput);
inputContainer.appendChild(maxInput);
plotDiv.appendChild(inputContainer);
}
var resultSelect = document.getElementById("resultSelect");
if (result_names.length > 1 && !resultSelect) {
resultSelect = document.createElement("select");
resultSelect.id = "resultSelect";
resultSelect.style.marginBottom = "10px";
var sortedResults = [...result_names].sort();
sortedResults.forEach(result => {
var option = document.createElement("option");
option.value = result;
option.textContent = result;
resultSelect.appendChild(option);
});
var selectContainer = document.createElement("div");
selectContainer.style.marginBottom = "10px";
selectContainer.appendChild(resultSelect);
plotDiv.appendChild(selectContainer);
}
minInput.addEventListener("input", updatePlots);
maxInput.addEventListener("input", updatePlots);
if (resultSelect) {
resultSelect.addEventListener("change", updatePlots);
}
updatePlots();
async function updatePlots() {
var minValue = parseFloat(minInput.value);
var maxValue = parseFloat(maxInput.value);
if (isNaN(minValue)) minValue = -Infinity;
if (isNaN(maxValue)) maxValue = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var selectedResult = resultSelect ? resultSelect.value : result_names[0];
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 2) {
console.error("Not enough columns for Scatter-Plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
showlegend: false
};
let subDiv = document.createElement("div");
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
symbol: data.map(d => d.result === null ? 'x' : 'circle'),
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter',
showlegend: false
};
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
$("#plotScatter2d").data("loaded", "true");
}
function plotScatter3d() {
if ($("#plotScatter3d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter3d");
if (!plotDiv) {
console.error("Div element with id 'plotScatter3d' not found");
return;
}
plotDiv.innerHTML = "";
var minInput3d = document.getElementById("minValue3d");
var maxInput3d = document.getElementById("maxValue3d");
if (!minInput3d || !maxInput3d) {
minInput3d = document.createElement("input");
minInput3d.id = "minValue3d";
minInput3d.type = "number";
minInput3d.placeholder = "Min Value";
minInput3d.step = "any";
maxInput3d = document.createElement("input");
maxInput3d.id = "maxValue3d";
maxInput3d.type = "number";
maxInput3d.placeholder = "Max Value";
maxInput3d.step = "any";
var inputContainer3d = document.createElement("div");
inputContainer3d.style.marginBottom = "10px";
inputContainer3d.appendChild(minInput3d);
inputContainer3d.appendChild(maxInput3d);
plotDiv.appendChild(inputContainer3d);
}
var select3d = document.getElementById("select3dScatter");
if (result_names.length > 1 && !select3d) {
if (!select3d) {
select3d = document.createElement("select");
select3d.id = "select3dScatter";
select3d.style.marginBottom = "10px";
select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
select3d.addEventListener("change", updatePlots3d);
plotDiv.appendChild(select3d);
}
}
minInput3d.addEventListener("input", updatePlots3d);
maxInput3d.addEventListener("input", updatePlots3d);
updatePlots3d();
async function updatePlots3d() {
var selectedResult = select3d ? select3d.value : result_names[0];
var minValue3d = parseFloat(minInput3d.value);
var maxValue3d = parseFloat(maxInput3d.value);
if (isNaN(minValue3d)) minValue3d = -Infinity;
if (isNaN(maxValue3d)) maxValue3d = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 3) {
console.error("Not enough columns for 3D scatter plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
for (let k = j + 1; k < numericColumns.length; k++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let zCol = numericColumns[k];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let zIndex = tab_results_headers_json.indexOf(zCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
z: parseFloat(row[zIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
scene: {
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
zaxis: {
title: get_axis_title_data(zCol)
}
},
showlegend: false
};
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
z: data.map(d => d.z),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter3d',
showlegend: false
};
let subDiv = document.createElement("div");
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
}
$("#plotScatter3d").data("loaded", "true");
}
async function load_pareto_graph() {
if($("#tab_pareto_fronts").data("loaded") == "true") {
return;
}
var data = pareto_front_data;
if (!data || typeof data !== "object") {
console.error("Invalid data format for pareto_front_data");
return;
}
if (!Object.keys(data).length) {
console.warn("No data found in pareto_front_data");
return;
}
let categories = Object.keys(data);
let allMetrics = new Set();
function extractMetrics(obj, prefix = "") {
let keys = Object.keys(obj);
for (let key of keys) {
let newPrefix = prefix ? `${prefix} -> ${key}` : key;
if (typeof obj[key] === "object" && !Array.isArray(obj[key])) {
extractMetrics(obj[key], newPrefix);
} else {
if (!newPrefix.includes("param_dicts") && !newPrefix.includes(" -> sems -> ") && !newPrefix.includes("absolute_metrics")) {
allMetrics.add(newPrefix);
}
}
}
}
for (let cat of categories) {
extractMetrics(data[cat]);
}
allMetrics = Array.from(allMetrics);
function extractValues(obj, metricPath, values) {
let parts = metricPath.split(" -> ");
let data = obj;
for (let part of parts) {
if (data && typeof data === "object") {
data = data[part];
} else {
return;
}
}
if (Array.isArray(data)) {
values.push(...data);
}
}
let graphContainer = document.getElementById("pareto_front_graphs_container");
graphContainer.classList.add("invert_in_dark_mode");
graphContainer.innerHTML = "";
var already_plotted = [];
for (let i = 0; i < allMetrics.length; i++) {
for (let j = i + 1; j < allMetrics.length; j++) {
let xMetric = allMetrics[i];
let yMetric = allMetrics[j];
let xValues = [];
let yValues = [];
for (let cat of categories) {
let metricData = data[cat];
extractValues(metricData, xMetric, xValues);
extractValues(metricData, yMetric, yValues);
}
xValues = xValues.filter(v => v !== undefined && v !== null);
yValues = yValues.filter(v => v !== undefined && v !== null);
let cleanXMetric = xMetric.replace(/.* -> /g, "");
let cleanYMetric = yMetric.replace(/.* -> /g, "");
let plot_key = `${cleanXMetric}-${cleanYMetric}`;
if (xValues.length > 0 && yValues.length > 0 && xValues.length === yValues.length && !already_plotted.includes(plot_key)) {
let div = document.createElement("div");
div.id = `pareto_front_graph_${i}_${j}`;
div.style.marginBottom = "20px";
graphContainer.appendChild(div);
let layout = {
title: `${cleanXMetric} vs ${cleanYMetric}`,
xaxis: {
title: get_axis_title_data(cleanXMetric)
},
yaxis: {
title: get_axis_title_data(cleanYMetric)
},
hovermode: "closest"
};
let trace = {
x: xValues,
y: yValues,
mode: "markers",
marker: {
size: get_marker_size(),
},
type: "scatter",
name: `${cleanXMetric} vs ${cleanYMetric}`
};
Plotly.newPlot(div.id, [trace], add_default_layout_data(layout));
already_plotted.push(plot_key);
}
}
}
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
$("#tab_pareto_fronts").data("loaded", "true");
}
async function plot_worker_cpu_ram() {
if($("#worker_cpu_ram_pre").data("loaded") == "true") {
return;
}
const logData = $("#worker_cpu_ram_pre").text();
const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
const hostData = {};
logData.split("\n").forEach(line => {
line = line.trim();
const match = line.match(regex);
if (match) {
const timestamp = new Date(parseInt(match[1]) * 1000);
const hostname = match[2];
const cpu = parseFloat(match[3]);
const ram = parseFloat(match[4]);
if (!hostData[hostname]) {
hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
}
hostData[hostname].timestamps.push(timestamp);
hostData[hostname].cpuUsage.push(cpu);
hostData[hostname].ramUsage.push(ram);
}
});
if (!Object.keys(hostData).length) {
console.log("No valid data found");
return;
}
const container = document.getElementById("cpuRamWorkerChartContainer");
container.innerHTML = "";
var i = 1;
Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
const chartId = `workerChart_${index}`;
const chartDiv = document.createElement("div");
chartDiv.id = chartId;
chartDiv.style.marginBottom = "40px";
container.appendChild(chartDiv);
const cpuTrace = {
x: timestamps,
y: cpuUsage,
mode: "lines+markers",
name: "CPU Usage (%)",
yaxis: "y1",
line: {
color: "red"
}
};
const ramTrace = {
x: timestamps,
y: ramUsage,
mode: "lines+markers",
name: "RAM Usage (MB)",
yaxis: "y2",
line: {
color: "blue"
}
};
const layout = {
title: `Worker CPU and RAM Usage - ${hostname}`,
xaxis: {
title: get_axis_title_data("Timestamp", "date")
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
side: "left",
color: "red"
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
side: "right",
overlaying: "y",
color: "blue"
},
showlegend: true
};
Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
i++;
});
$("#plot_worker_cpu_ram_button").remove();
$("#worker_cpu_ram_pre").data("loaded", "true");
}
function load_log_file(log_nr, filename) {
var pre_id = `single_run_${log_nr}_pre`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
fetch(url)
.then(response => response.json())
.then(data => {
if (data.data) {
$("#" + pre_id).html(data.data);
$("#" + pre_id).data("loaded", true);
} else {
log(`No 'data' key found in response.`);
}
$("#spinner_log_" + log_nr).remove();
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#spinner_log_" + log_nr).remove();
});
}
}
function load_debug_log () {
var pre_id = `here_debuglogs_go`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
fetch(url)
.then(response => response.json())
.then(data => {
$("#debug_log_spinner").remove();
if (data.data) {
try {
$("#" + pre_id).html(data.data);
} catch (err) {
$("#" + pre_id).text(`Error loading data: ${err}`);
}
$("#" + pre_id).data("loaded", true);
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
} else {
log(`No 'data' key found in response.`);
}
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#debug_log_spinner").remove();
});
}
}
function plotBoxplot() {
if ($("#plotBoxplot").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough numeric columns for Boxplot");
return;
}
var resultIndex = tab_results_headers_json.findIndex(function(header) {
return result_names.includes(header.toLowerCase());
});
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
var plotDiv = document.getElementById("plotBoxplot");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'box',
name: col,
boxmean: 'sd',
marker: {
color: 'rgb(0, 255, 0)'
},
};
});
let layout = {
title: 'Boxplot of Numerical Columns',
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Value")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotBoxplot").data("loaded", "true");
}
function plotHeatmap() {
if ($("#plotHeatmap").data("loaded") === "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col => {
if (special_col_names.includes(col) || result_names.includes(col)) {
return false;
}
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.every(row => {
let value = parseFloat(row[index]);
return !isNaN(value) && isFinite(value);
});
});
if (numericColumns.length < 2) {
console.error("Not enough valid numeric columns for Heatmap");
return;
}
var columnData = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.map(row => parseFloat(row[index]));
});
var dataMatrix = numericColumns.map((_, i) =>
numericColumns.map((_, j) => {
let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
return values.reduce((a, b) => a + b, 0) / values.length;
})
);
var trace = {
z: dataMatrix,
x: numericColumns,
y: numericColumns,
colorscale: 'Viridis',
type: 'heatmap'
};
var layout = {
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
var plotDiv = document.getElementById("plotHeatmap");
plotDiv.innerHTML = "";
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotHeatmap").data("loaded", "true");
}
function plotHistogram() {
if ($("#plotHistogram").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Histogram");
return;
}
var plotDiv = document.getElementById("plotHistogram");
plotDiv.innerHTML = "";
const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
let traces = numericColumns.map((col, index) => {
let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
return {
x: data,
type: 'histogram',
name: col,
opacity: 0.7,
marker: {
color: colorPalette[index % colorPalette.length]
},
autobinx: true
};
});
let layout = {
title: 'Histogram of Numerical Columns',
xaxis: {
title: get_axis_title_data("Value")
},
yaxis: {
title: get_axis_title_data("Frequency")
},
showlegend: true,
barmode: 'overlay'
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotHistogram").data("loaded", "true");
}
function plotViolin() {
if ($("#plotViolin").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Violin Plot");
return;
}
var plotDiv = document.getElementById("plotViolin");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'violin',
name: col,
box: {
visible: true
},
line: {
color: 'rgb(0, 255, 0)'
},
marker: {
color: 'rgb(0, 255, 0)'
},
meanline: {
visible: true
},
};
});
let layout = {
title: 'Violin Plot of Numerical Columns',
yaxis: {
title: get_axis_title_data("Value")
},
xaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotViolin").data("loaded", "true");
}
function plotExitCodesPieChart() {
if ($("#plotExitCodesPieChart").data("loaded") == "true") {
return;
}
var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
counts[exitCode] = (counts[exitCode] || 0) + 1;
return counts;
}, {});
var labels = Object.keys(exitCodeCounts);
var values = Object.values(exitCodeCounts);
var plotDiv = document.getElementById("plotExitCodesPieChart");
plotDiv.innerHTML = "";
var trace = {
labels: labels,
values: values,
type: 'pie',
hoverinfo: 'label+percent',
textinfo: 'label+value',
marker: {
colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
}
};
var layout = {
title: 'Exit Code Distribution',
showlegend: true
};
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotExitCodesPieChart").data("loaded", "true");
}
function plotResultEvolution() {
if ($("#plotResultEvolution").data("loaded") == "true") {
return;
}
result_names.forEach(resultName => {
var relevantColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
);
var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
var resultIndex = tab_results_headers_json.indexOf(resultName);
let data = tab_results_csv_json.map(row => ({
x: row[xColumnIndex],
y: parseFloat(row[resultIndex])
}));
data.sort((a, b) => a.x - b.x);
let xData = data.map(item => item.x);
let yData = data.map(item => item.y);
let trace = {
x: xData,
y: yData,
mode: 'lines+markers',
name: resultName,
line: {
shape: 'linear'
},
marker: {
size: get_marker_size()
}
};
let layout = {
title: `Evolution of ${resultName} over time`,
xaxis: {
title: get_axis_title_data("Trial-Index")
},
yaxis: {
title: get_axis_title_data(resultName)
},
showlegend: true
};
let subDiv = document.createElement("div");
document.getElementById("plotResultEvolution").appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
});
$("#plotResultEvolution").data("loaded", "true");
}
function plotResultPairs() {
if ($("#plotResultPairs").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotResultPairs");
plotDiv.innerHTML = "";
for (let i = 0; i < result_names.length; i++) {
for (let j = i + 1; j < result_names.length; j++) {
let xName = result_names[i];
let yName = result_names[j];
let xIndex = tab_results_headers_json.indexOf(xName);
let yIndex = tab_results_headers_json.indexOf(yName);
let data = tab_results_csv_json
.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
status: row[tab_results_headers_json.indexOf("trial_status")]
}));
let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: colors
},
text: data.map(d => `Status: ${d.status}`),
type: 'scatter',
showlegend: false
};
let layout = {
xaxis: {
title: get_axis_title_data(xName)
},
yaxis: {
title: get_axis_title_data(yName)
},
showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
upArrow.style.display = "block";
} else {
upArrow.style.display = "none";
}
if (scrollPosition + windowHeight < pageHeight) {
downArrow.style.display = "block";
} else {
downArrow.style.display = "none";
}
}
window.addEventListener("scroll", checkScrollPosition);
upArrow.addEventListener("click", function () {
window.scrollTo({ top: 0, behavior: 'smooth' });
});
downArrow.addEventListener("click", function () {
window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
});
checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
function plotGPUUsage() {
if ($("#tab_gpu_usage").data("loaded") === "true") {
return;
}
Object.keys(gpu_usage).forEach(node => {
const nodeData = gpu_usage[node];
var timestamps = [];
var gpuUtilizations = [];
var temperatures = [];
nodeData.forEach(entry => {
try {
var timestamp = new Date(entry[0]* 1000);
var utilization = parseFloat(entry[1]);
var temperature = parseFloat(entry[2]);
if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
timestamps.push(timestamp);
gpuUtilizations.push(utilization);
temperatures.push(temperature);
} else {
console.warn("Invalid data point:", entry);
}
} catch (error) {
console.error("Error processing GPU data entry:", error, entry);
}
});
var trace1 = {
x: timestamps,
y: gpuUtilizations,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Utilization (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: temperatures,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Temperature (°C)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'GPU Usage Over Time - ' + node,
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("GPU Utilization (%)"),
overlaying: 'y',
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("GPU Temperature (°C)"),
overlaying: 'y',
side: 'right',
position: 0.85,
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var divId = 'gpu_usage_plot_' + node;
if (!document.getElementById(divId)) {
var div = document.createElement('div');
div.id = divId;
div.className = 'gpu-usage-plot';
document.getElementById('tab_gpu_usage').appendChild(div);
}
var plotData = [trace1, trace2];
Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
});
$("#tab_gpu_usage").data("loaded", "true");
}
function plotResultsDistributionByGenerationMethod() {
if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
return;
}
var res_col = result_names[0];
var gen_method_col = "generation_method";
var data = {};
tab_results_csv_json.forEach(row => {
var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
var result = row[tab_results_headers_json.indexOf(res_col)];
if (!data[gen_method]) {
data[gen_method] = [];
}
data[gen_method].push(result);
});
var traces = Object.keys(data).map(method => {
return {
y: data[method],
type: 'box',
name: method,
boxpoints: 'outliers', // Zeigt nur Ausreißer außerhalb der Whiskers
jitter: 0.5, // Erhöht die Streuung der Punkte für bessere Sichtbarkeit
pointpos: 0 // Position der Punkte innerhalb der Box
};
});
var layout = {
title: 'Distribution of Results by Generation Method',
yaxis: {
title: get_axis_title_data(res_col)
},
xaxis: {
title: "Generation Method"
},
boxmode: 'group' // Gruppiert die Boxplots nach Generation Method
};
Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
}
function plotJobStatusDistribution() {
if ($("#plotJobStatusDistribution").data("loaded") === "true") {
return;
}
var status_col = "trial_status";
var status_counts = {};
tab_results_csv_json.forEach(row => {
var status = row[tab_results_headers_json.indexOf(status_col)];
if (status) {
status_counts[status] = (status_counts[status] || 0) + 1;
}
});
var statuses = Object.keys(status_counts);
var counts = Object.values(status_counts);
var colors = statuses.map((status, i) =>
status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
);
var trace = {
x: statuses,
y: counts,
type: 'bar',
marker: { color: colors }
};
var layout = {
title: 'Distribution of Job Status',
xaxis: { title: 'Trial Status' },
yaxis: { title: 'Nr. of jobs' }
};
Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_method"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
el.textContent.includes("Sobol") ? "orange" :
el.textContent.includes("SAASBO") ? "pink" :
el.textContent.includes("Uniform") ? "lightblue" :
el.textContent.includes("Legacy_GPEI") ? "Sienna" :
el.textContent.includes("BO_MIXED") ? "Aqua" :
el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
el.textContent.includes("BoTorch") ? "yellow" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_trial_status () {
document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
el.textContent.includes("FAILED") ? "red" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_run_time() {
let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
if (values.length === 0) return;
let min = Math.min(...values);
let max = Math.max(...values);
let range = max - min || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value)) return;
let ratio = (value - min) / range;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_results() {
result_names.forEach((name, index) => {
let minMax = result_min_max[index];
let selector_query = `[data-column-id="${name}"]`;
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
if (values.length === 0) return;
let logValues = values.map(v => Math.log(v));
let logMin = Math.min(...logValues);
let logMax = Math.max(...logValues);
let logRange = logMax - logMin || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value) || value <= 0) return;
let logValue = Math.log(value);
let ratio = (logValue - logMin) / logRange;
if (minMax === "max") ratio = 1 - ratio;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
});
}
function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
let colorMap = {};
uniqueValues.forEach((value, index) => {
let hue = Math.round((360 / uniqueValues.length) * index);
colorMap[value] = `hsl(${hue}, 70%, 60%)`;
});
cells.forEach(el => {
let value = el.textContent.trim();
if (colorMap[value]) {
el.style.backgroundColor = colorMap[value];
el.classList.add("invert_in_dark_mode");
}
});
});
}
function colorize_table_entries () {
setTimeout(() => {
if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
_colorize_table_entries_by_trial_status();
_colorize_table_entries_by_results();
_colorize_table_entries_by_run_time();
_colorize_table_entries_by_generation_method();
_colorize_table_entries_by_generation_node_or_hostname();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
}, 300);
}
function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
if (searchInput) {
searchInput.addEventListener("input", colorize_table_entries);
}
}
function updatePreWidths() {
var width = window.innerWidth * 0.95;
var pres = document.getElementsByTagName('pre');
for (var i = 0; i < pres.length; i++) {
pres[i].style.width = width + 'px';
}
}
window.addEventListener('load', updatePreWidths);
window.addEventListener('resize', updatePreWidths);
$(document).ready(function() {
colorize_table_entries();
add_up_down_arrows_for_scrolling();
add_colorize_to_gridjs_table();
});
$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_reference_samples</th><th>recent_samples_proportion</th><th>threshold</th><th>result </th></tr></thead><tbody><tr><td> 50</td><td>0.421754</td><td>0.513596</td><td>0.249534 </td></tr></tbody></table><h2>Experiment parameters: </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type </th></tr></thead><tbody><tr><td> n_reference_samples</td><td>range</td><td>50</td><td>500</td><td></td><td>int </td></tr><tr><td> recent_samples_proport…</td><td>range</td><td>0.1</td><td>1</td><td></td><td>float </td></tr><tr><td> threshold</td><td>range</td><td>0.5</td><td>0.8</td><td></td><td>float </td></tr></tbody></table><br><h2>Number of evaluations:</h2>
<table>
<tbody>
<tr>
<th>Failed</th>
<th>Succeeded</th>
<th>Running</th>
<th>Total</th>
</tr>
<tr>
<td>0</td>
<td>497</td>
<td>4</td>
<td>501</td>
</tr>
</tbody>
</table>
<h1> Results</h1>
<div id='tab_results_csv_table'></div>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_reference_samples,recent_samples_proportion,threshold
0,0_0,COMPLETED,Sobol,0.305520984081042001889727544039,444,0.305429597198963176385433371252,0.798646718263626098632812500000
1,1_0,COMPLETED,Sobol,0.293402074288470759988456393330,348,0.508871104195714019091667523753,0.702787497267127081457260828756
2,2_0,COMPLETED,Sobol,0.293585383502170782499263168575,269,0.383724621124565623553337445628,0.778670352511107943804802289378
3,3_0,COMPLETED,Sobol,0.288336951278340580806514026335,241,0.768091368023306131362915039062,0.723576524574309654092019172822
4,4_0,COMPLETED,Sobol,0.300774240231548528612393056392,455,0.855721276439726397100571375631,0.574687444977462269513068804372
5,5_0,COMPLETED,Sobol,0.276834298118668553634336149116,163,0.595524707809090614318847656250,0.504377554263919614108147015941
6,6_0,COMPLETED,Sobol,0.302009165460685014004127424414,500,0.915496782865375280380249023438,0.767477884050458691866936078441
7,7_0,COMPLETED,Sobol,0.275707911239749137166654691100,153,0.229093782790005223715112947502,0.628426977805793263165412554372
8,8_0,COMPLETED,Sobol,0.298725277375783915267959400808,401,0.480900513101369164736809125316,0.544215989019721790853623133444
9,9_0,COMPLETED,Sobol,0.287563917028461180080967096728,265,0.468744014296680666653571734059,0.572022718377411365509033203125
10,10_0,COMPLETED,Sobol,0.297623010130246057336478315847,400,0.509753129351884104458747515309,0.700509583763778254095200281881
11,11_0,COMPLETED,Sobol,0.301151712493970102890727957856,448,0.408616165351122662130478602194,0.646389809250831559594985264994
12,12_0,COMPLETED,Sobol,0.261028702363724107726739021018,70,0.828558709193021103445175867819,0.729435470420867204666137695312
13,13_0,COMPLETED,Sobol,0.299264351181862009454448525503,324,0.597467388212680972081614072522,0.756832037214189790041984906566
14,14_0,COMPLETED,Sobol,0.305048239266763165922213829617,446,0.504960533976554937218850227509,0.778471415862441062927246093750
15,15_0,COMPLETED,Sobol,0.294060540279787785422627166554,347,0.627729649469256356653090733744,0.567370143532752946313735264994
16,16_0,COMPLETED,Sobol,0.284808248914616535252264384326,208,0.940473605412989854812622070312,0.686745359096676111221313476562
17,17_0,COMPLETED,Sobol,0.285338880849011045093277516571,205,0.374514772836119003152077766572,0.668042282387614205774184483744
18,18_0,COMPLETED,Sobol,0.273431017848528745695091402013,157,0.649232530221343084875229578756,0.545012427959591150283813476562
19,19_0,COMPLETED,Sobol,0.296098649300530647465734546131,369,0.723707222659140780862685460306,0.765740038361400365829467773438
20,20_0,COMPLETED,BoTorch,0.252332368547998120433817348385,50,0.723144175337181227547489470453,0.663116372411854060331393156957
21,21_0,COMPLETED,BoTorch,0.254984322238302030250167717895,50,0.652135686940745307182965007087,0.780094687355876725831649309839
22,22_0,COMPLETED,BoTorch,0.252901591895803190546132555028,50,1.000000000000000000000000000000,0.673941589985301181542354242993
23,23_0,COMPLETED,BoTorch,0.259183550410033713973234625882,50,1.000000000000000000000000000000,0.800000000000000044408920985006
24,24_0,COMPLETED,BoTorch,0.251995899662325162360332342359,50,0.508534901607298861492267860740,0.670272325599055696088157674239
25,25_0,COMPLETED,BoTorch,0.252467438494934914849920915003,50,0.824338589032663437983217136207,0.595549766455523044150766054372
26,26_0,COMPLETED,BoTorch,0.251361553304389806484664404707,50,1.000000000000000000000000000000,0.516929498217924332514883190015
27,27_0,COMPLETED,BoTorch,0.253181379643029447201740822493,50,0.343906433180010628891665191986,0.791493297315619903642414101341
28,28_0,COMPLETED,BoTorch,0.253942354076218013148036334314,50,0.604146941036584061990311056434,0.726047030443378926811703877320
29,29_0,COMPLETED,BoTorch,0.256833092136999563415145075851,50,1.000000000000000000000000000000,0.753681629929233709574987187807
30,30_0,COMPLETED,BoTorch,0.260730824891461598902253626875,74,0.890459114159984910230605237302,0.744651553714873792877426694758
31,31_0,COMPLETED,BoTorch,0.256028702363724103285846922518,50,0.824736117650769262610310761374,0.731842666010375952900801621581
32,32_0,COMPLETED,BoTorch,0.251173420164013561084459524864,50,0.100000000000000005551115123126,0.500000000000000000000000000000
33,33_0,COMPLETED,BoTorch,0.260144717800289471121288897848,74,0.772597457566617240587447668076,0.692630479834633216995598559151
34,34_0,COMPLETED,BoTorch,0.251802942595272583048426895402,50,0.510954554067603039513301155239,0.564019180352643090259334712755
35,35_0,COMPLETED,BoTorch,0.252755668113844644828702712402,50,0.857293251850912318978714665718,0.687996066517518212179993497557
36,36_0,COMPLETED,BoTorch,0.252280511336227641905338714423,50,1.000000000000000000000000000000,0.639642688677367621252756180183
37,37_0,COMPLETED,BoTorch,0.268199469368065601670991782157,94,0.700353817611247597696433331294,0.800000000000000044408920985006
38,38_0,COMPLETED,BoTorch,0.256291606367583191250503205083,50,0.895116830691361275995632240665,0.766940468894461324644851174526
39,39_0,COMPLETED,BoTorch,0.251232513265798318435884084465,50,0.900752740361539072644347925234,0.582515833725933962483622963191
40,40_0,COMPLETED,BoTorch,0.258111432706222831967579622869,50,0.814680011608184706517477025045,0.800000000000000044408920985006
41,41_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.579726293092297173537019716605
42,42_0,COMPLETED,BoTorch,0.251508683068017324657716926595,50,0.725604991148563138736449218413,0.500000000000000000000000000000
43,43_0,COMPLETED,BoTorch,0.251449589966232567306292366993,50,0.811883031469977978211716163059,0.544302078522298060647699458059
44,44_0,COMPLETED,BoTorch,0.257468644476604002768738155282,67,0.786115453940889552519877270242,0.668258841296488936833952720917
45,45_0,COMPLETED,BoTorch,0.253704775687409567197505566583,50,0.241624122136533003857294943373,0.730846862635321059542548027821
46,46_0,COMPLETED,BoTorch,0.252662807525325661117676645517,50,0.100000000000000005551115123126,0.580943391493934369051999055955
47,47_0,COMPLETED,BoTorch,0.251301254220935854633012240811,50,0.396884263871063458495314080210,0.500000000000000000000000000000
48,48_0,COMPLETED,BoTorch,0.252456584659913163548594638996,50,0.246922780228676475022808745052,0.584344643926275386647262166662
49,49_0,COMPLETED,BoTorch,0.251554510371442385796569851664,50,1.000000000000000000000000000000,0.500000000000000000000000000000
50,50_0,COMPLETED,BoTorch,0.252524119633381616267797653563,50,0.100000000000000005551115123126,0.664584522849250070031246195867
51,51_0,COMPLETED,BoTorch,0.253333333333333299286493911495,50,0.100000000000000005551115123126,0.800000000000000044408920985006
52,52_0,COMPLETED,BoTorch,0.254797395079594757305585517315,50,0.100000000000000005551115123126,0.718330539296517245340112367558
53,53_0,COMPLETED,BoTorch,0.259088277858176563306358275440,77,1.000000000000000000000000000000,0.500000000000000000000000000000
54,54_0,COMPLETED,BoTorch,0.252498794018330974253672138730,50,0.100000000000000005551115123126,0.622375611990742427792611124460
55,55_0,COMPLETED,BoTorch,0.254119633381572618269217400666,50,0.100000000000000005551115123126,0.663164821450404562419578269328
56,56_0,COMPLETED,BoTorch,0.252036903039073778565182237799,50,0.349595841374436555604177101486,0.683479622673925257814175893145
57,57_0,COMPLETED,BoTorch,0.254219729860106102847794318222,59,0.718467310643101653155895292002,0.500000000000000000000000000000
58,58_0,COMPLETED,BoTorch,0.258960443801254269757805559493,71,0.100000000000000005551115123126,0.500000000000000000000000000000
59,59_0,COMPLETED,BoTorch,0.251410998552822007034990292595,50,0.601896537418541544006700405589,0.500000000000000000000000000000
60,60_0,COMPLETED,BoTorch,0.252711046792088778190077391628,57,0.960628327325242503675895022752,0.528914786128193314951317915984
61,61_0,COMPLETED,BoTorch,0.253137964302942553018738180981,50,0.120820440797913986052947166172,0.606957357168174338468702444516
62,62_0,COMPLETED,BoTorch,0.251995899662325162360332342359,51,0.405560908370096173669594463718,0.500000000000000000000000000000
63,63_0,COMPLETED,BoTorch,0.258034249879401822447277936590,74,0.191538501447182263337509766643,0.575161269058075408366903502611
64,64_0,COMPLETED,BoTorch,0.252039315002411945521032521356,50,0.301786131289731085480809724686,0.500000000000000000000000000000
65,65_0,COMPLETED,BoTorch,0.252074288470815255358559170418,50,0.107462310525573881414551635771,0.556020489456461541877274612489
66,66_0,COMPLETED,BoTorch,0.254000241196333798043838214653,51,0.108128846752074486703421030143,0.596509307403561006566405922058
67,67_0,COMPLETED,BoTorch,0.261086589483839892622540901357,85,0.733578529366938569822309545998,0.500000000000000000000000000000
68,68_0,COMPLETED,BoTorch,0.257269657501206006067206999433,67,0.472944291480063783161824630952,0.500000000000000000000000000000
69,69_0,COMPLETED,BoTorch,0.252262421611191500758764050261,50,1.000000000000000000000000000000,0.548466769281154675752532057231
70,70_0,COMPLETED,BoTorch,0.253197057404727421392465203098,55,0.100000000000000005551115123126,0.714617781309147881430021698179
71,71_0,COMPLETED,BoTorch,0.252035697057404695087257096020,50,0.860472804840584526431257472723,0.500000000000000000000000000000
72,72_0,COMPLETED,BoTorch,0.252221418234442884553914154822,50,0.762411789569095166285706000053,0.500000000000000000000000000000
73,73_0,COMPLETED,BoTorch,0.255148335745296717114172224683,50,0.309413431032257602026902532089,0.792546817859768881753268487955
74,74_0,COMPLETED,BoTorch,0.251699228171731737013772089995,50,0.404559820117709412201634222583,0.671573203373752147804509604612
75,75_0,COMPLETED,BoTorch,0.252273275446213252060090326268,50,0.798005637053934568925228631997,0.549180774244112490656277714152
76,76_0,COMPLETED,BoTorch,0.254142547033285093327492631943,50,0.100000000000000005551115123126,0.728218622364646406275312529033
77,32_0,COMPLETED,BoTorch,0.252320308731307285654565930599,50,0.100000000000000005551115123126,0.500000000000000000000000000000
78,78_0,COMPLETED,BoTorch,0.252141823444283597055459722469,50,0.101032386639650723081373939749,0.581540975063708498460357532167
79,79_0,COMPLETED,BoTorch,0.252191268692715908628088072874,50,0.559330810394753186187699611764,0.583864368201500449906404810463
80,80_0,COMPLETED,BoTorch,0.251655812831644953853071910999,50,0.405907345398553665027918668784,0.500000000000000000000000000000
81,81_0,COMPLETED,BoTorch,0.250520984081041953039914460533,50,0.341235100216555675700647043413,0.528287564882613969174940393714
82,82_0,COMPLETED,BoTorch,0.251714905933429822226798933116,50,0.289059908492218964326525565411,0.500000000000000000000000000000
83,83_0,COMPLETED,BoTorch,0.252173178967679656459210946196,50,0.246903237003261166737644316527,0.584357754855903688273599527747
84,84_0,COMPLETED,BoTorch,0.251652194886637703419296485663,50,0.432225571037495703841102567822,0.534860916562768129445260001376
85,85_0,COMPLETED,BoTorch,0.258045103714423573748604212597,73,0.334149964889375095111745395116,0.500000000000000000000000000000
86,86_0,COMPLETED,BoTorch,0.250323203087313039816308446461,50,0.263618942299450542687822007792,0.500000000000000000000000000000
87,49_0,COMPLETED,BoTorch,0.251450795947901539761915046256,50,1.000000000000000000000000000000,0.500000000000000000000000000000
88,32_0,COMPLETED,BoTorch,0.252268451519536918148389759153,50,0.100000000000000005551115123126,0.500000000000000000000000000000
89,89_0,COMPLETED,BoTorch,0.251442354076218066438741516322,50,1.000000000000000000000000000000,0.553166901500531715818453903921
90,90_0,COMPLETED,BoTorch,0.251508683068017324657716926595,50,0.718566964967315979784245882911,0.553503417233016659082522892277
91,32_0,COMPLETED,BoTorch,0.252720694645441334991176063340,50,0.100000000000000005551115123126,0.500000000000000000000000000000
92,92_0,COMPLETED,BoTorch,0.251601543656536419391045455995,50,0.647157886174812513324638985068,0.583045015936292321256928516959
93,32_0,COMPLETED,BoTorch,0.252020019295706720896532715415,50,0.100000000000000005551115123126,0.500000000000000000000000000000
94,94_0,COMPLETED,BoTorch,0.252368547998070402726966676710,50,0.543615943101882659327372948610,0.524497559153069059512120020372
95,49_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.500000000000000000000000000000
96,49_0,COMPLETED,BoTorch,0.251357935359382556050888979371,50,1.000000000000000000000000000000,0.500000000000000000000000000000
97,49_0,COMPLETED,BoTorch,0.251367583212735112851987651084,50,1.000000000000000000000000000000,0.500000000000000000000000000000
98,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000
99,99_0,COMPLETED,BoTorch,0.251494211287988433944917687768,50,1.000000000000000000000000000000,0.548143530284457614065729558206
100,100_0,COMPLETED,BoTorch,0.251447178002894400350442083436,50,0.512211278341243358980250377499,0.609524129777032963595218006958
101,101_0,COMPLETED,BoTorch,0.251466473709599624974941889377,50,0.528434527503533923997736110323,0.675932928146487865816993689805
102,102_0,COMPLETED,BoTorch,0.251892185238784316325677536952,50,0.506172487329796472188547795668,0.591503236837691126481786341174
103,103_0,COMPLETED,BoTorch,0.251388084901109531976715061319,50,0.386240144451517330992373899790,0.500000000000000000000000000000
104,104_0,COMPLETED,BoTorch,0.251164978292329976738983532414,50,0.363147119901957249332724586566,0.500000000000000000000000000000
105,105_0,COMPLETED,BoTorch,0.250789917993246458394196451991,50,0.370130213921177175784293922334,0.500000000000000000000000000000
106,106_0,COMPLETED,BoTorch,0.252533767486734173068896325276,50,0.915187038391401030956728845922,0.559458837524642760286042175721
107,107_0,COMPLETED,BoTorch,0.251700434153400820491697231773,50,0.310525474986082350437044397040,0.500000000000000000000000000000
108,108_0,COMPLETED,BoTorch,0.251482151471297599165666269982,50,1.000000000000000000000000000000,0.547121163311953595531633709470
109,109_0,COMPLETED,BoTorch,0.251490593342981183511142262432,50,1.000000000000000000000000000000,0.554494648742221429849053038197
110,110_0,COMPLETED,BoTorch,0.251642547033285146618197813950,50,0.351432865117274451094431242382,0.519515478476362813076150359848
111,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000
112,112_0,COMPLETED,BoTorch,0.252185238784370491238462363981,50,0.508781014142472898242886003572,0.670458293735125776535710429016
113,113_0,COMPLETED,BoTorch,0.251397732754462088777813733032,50,1.000000000000000000000000000000,0.547459954415160932050810060900
114,114_0,COMPLETED,BoTorch,0.252689339122045386609727302130,50,0.334499016205316579419815070651,0.500000000000000000000000000000
115,115_0,COMPLETED,BoTorch,0.250705499276410948006343915040,50,0.339272640320934348867609742229,0.500000000000000000000000000000
116,49_0,COMPLETED,BoTorch,0.251374819102749613719538501755,50,1.000000000000000000000000000000,0.500000000000000000000000000000
117,49_0,COMPLETED,BoTorch,0.251330197780993747080913180980,50,1.000000000000000000000000000000,0.500000000000000000000000000000
118,49_0,COMPLETED,BoTorch,0.251575012059816693898994799383,50,1.000000000000000000000000000000,0.500000000000000000000000000000
119,119_0,COMPLETED,BoTorch,0.252331162566329036955892206606,50,0.460946065966412299452770184871,0.605387249222629875156087564392
120,120_0,COMPLETED,BoTorch,0.251947660395561934265629133733,50,0.536165641818900695625416119583,0.621598748946203039089652975235
121,49_0,COMPLETED,BoTorch,0.251603955619874586346895739553,50,1.000000000000000000000000000000,0.500000000000000000000000000000
122,122_0,COMPLETED,BoTorch,0.252422817173178937188993131713,50,0.766886666096983860008151623333,0.593025631460632363101126429683
123,49_0,COMPLETED,BoTorch,0.251302460202604938110937382589,50,1.000000000000000000000000000000,0.500000000000000000000000000000
124,124_0,COMPLETED,BoTorch,0.252244331886155359612189386098,50,0.557835692256274606748434052861,0.619899672605909390910028378130
125,49_0,COMPLETED,BoTorch,0.251388084901109531976715061319,50,1.000000000000000000000000000000,0.500000000000000000000000000000
126,126_0,COMPLETED,BoTorch,0.251324167872648329691287472087,50,0.335456322976191989582162022998,0.500000000000000000000000000000
127,49_0,COMPLETED,BoTorch,0.251453207911239706717765329813,50,1.000000000000000000000000000000,0.500000000000000000000000000000
128,128_0,COMPLETED,BoTorch,0.252630246020260518236000280012,50,0.121172213570191728004843412236,0.587100507360714840565663052985
129,129_0,COMPLETED,BoTorch,0.252430053063193438056543982384,50,0.503593042103052335178858811560,0.627345925627251665979144945595
130,130_0,COMPLETED,BoTorch,0.252793053545586121622079645022,50,0.550399998403406476299437599664,0.697532488795094329780965836107
131,131_0,COMPLETED,BoTorch,0.252310660877954617831164796371,50,0.494585803067729257165296985477,0.619084781955730023561557118228
132,49_0,COMPLETED,BoTorch,0.251338639652677331426389173430,50,1.000000000000000000000000000000,0.500000000000000000000000000000
133,133_0,COMPLETED,BoTorch,0.283087313072841295635839742317,189,0.425342917814850784985480913747,0.611657321732491210397597569681
134,134_0,COMPLETED,BoTorch,0.251868065605402757789477163897,50,0.342714373103922498309259481175,0.500000000000000000000000000000
135,135_0,COMPLETED,BoTorch,0.264916787264833586412748900329,96,0.897528963307195559728768330388,0.697450228229425039927491525305
136,136_0,COMPLETED,BoTorch,0.252842498794018322172405532910,50,0.462910517238185326149846332555,0.614627597504567635411376613774
137,137_0,COMPLETED,BoTorch,0.302928123492522960447104196646,491,0.865237909887539791142785361444,0.783414345822994073742506770941
138,49_0,COMPLETED,BoTorch,0.251359141341051639528814121149,50,1.000000000000000000000000000000,0.500000000000000000000000000000
139,139_0,COMPLETED,BoTorch,0.288209117221418287257961310388,231,0.268733718153089329305771570944,0.760840942244976781161369672191
140,49_0,COMPLETED,BoTorch,0.251377231066087780675388785312,50,1.000000000000000000000000000000,0.500000000000000000000000000000
141,141_0,COMPLETED,BoTorch,0.255704293294741868969310871762,57,0.543619520651145382572622111184,0.697341626743621878503631705826
142,142_0,COMPLETED,BoTorch,0.251690786300048263690598560061,50,1.000000000000000000000000000000,0.521446287426022614397425058996
143,143_0,COMPLETED,BoTorch,0.251382054992764114587089352426,50,1.000000000000000000000000000000,0.521917893307950686576646148751
144,144_0,COMPLETED,BoTorch,0.261052821997105666262939394073,76,0.893324286709682557905409794330,0.540606580259798907839297044120
145,145_0,COMPLETED,BoTorch,0.251304872165943105066787666146,50,0.340601703755768203674136884729,0.500000000000000000000000000000
146,146_0,COMPLETED,BoTorch,0.252582006753497401163599533902,50,0.816425129810199767810274806834,0.607291751826386172474769864493
147,147_0,COMPLETED,BoTorch,0.254169078630004818819543288555,56,0.584123401304225464691910474357,0.717028551053389806568816311483
148,148_0,COMPLETED,BoTorch,0.253681862035697092139230335306,50,0.381543826778766970164724625647,0.680889482027213777115548509755
149,49_0,COMPLETED,BoTorch,0.251646164978292286029670776770,50,1.000000000000000000000000000000,0.500000000000000000000000000000
150,150_0,COMPLETED,BoTorch,0.252026049204052138286158424307,50,0.339693000203483863597142544677,0.500000000000000000000000000000
151,151_0,COMPLETED,BoTorch,0.284767245537867808025112026371,190,0.527237577363848686218261718750,0.717803246807307004928588867188
152,152_0,COMPLETED,BoTorch,0.258572117703810944178144382022,73,0.920934926539291986991031535581,0.547321384955335665800646438583
153,153_0,COMPLETED,BoTorch,0.257685721177038140616843975295,74,0.906013477338800110594263514940,0.542320124778088885619808934280
154,154_0,COMPLETED,BoTorch,0.250962373371924729603676951228,50,0.276452012372551902963380143774,0.660016498504453319462470517465
155,49_0,COMPLETED,BoTorch,0.251435118186203565571190665651,50,1.000000000000000000000000000000,0.500000000000000000000000000000
156,156_0,COMPLETED,BoTorch,0.253064399421128793932211920037,50,0.340028095636878302521211026033,0.500000000000000000000000000000
157,157_0,COMPLETED,BoTorch,0.252709840810419694712152249849,50,0.100000000000000005551115123126,0.767562921840241108029090355558
158,49_0,COMPLETED,BoTorch,0.251314520019295661867886337859,50,1.000000000000000000000000000000,0.500000000000000000000000000000
159,49_0,COMPLETED,BoTorch,0.251314520019295661867886337859,50,1.000000000000000000000000000000,0.500000000000000000000000000000
160,160_0,COMPLETED,BoTorch,0.252604920405209876221874765179,50,0.766570525627019994097111066367,0.605870747656967911787262437429
161,161_0,COMPLETED,BoTorch,0.251097443318861524019780517847,50,0.347129110110642902231603557084,0.500000000000000000000000000000
162,49_0,COMPLETED,BoTorch,0.251315726000964745345811479638,50,1.000000000000000000000000000000,0.500000000000000000000000000000
163,163_0,COMPLETED,BoTorch,0.251587071876507528678246217169,51,0.650654112129341322123821100831,0.633951864513824348001946873410
164,164_0,COMPLETED,BoTorch,0.252083936324167923181960304646,50,0.100000000000000005551115123126,0.560565887599899670412639807182
165,165_0,COMPLETED,BoTorch,0.277680897250361824468711802183,155,0.496894221689799242547280755389,0.552302184659011263789807344438
166,166_0,COMPLETED,BoTorch,0.258174143753014950775082070322,74,0.905304960216311016552026558202,0.538042376024265411871283504297
167,49_0,COMPLETED,BoTorch,0.251449589966232567306292366993,50,1.000000000000000000000000000000,0.500000000000000000000000000000
168,168_0,COMPLETED,BoTorch,0.251383260974433198065014494205,50,0.357425099760597664300121323322,0.500000000000000000000000000000
169,169_0,COMPLETED,BoTorch,0.251075735648818132439430428349,50,0.562644323221510478383322606533,0.638035700168082309247097327898
170,170_0,COMPLETED,BoTorch,0.276395320791123944026423941978,152,0.255092379450798045770198996252,0.517569698113948151174668055319
171,171_0,COMPLETED,BoTorch,0.251357935359382556050888979371,50,0.366717189035585811041073611705,0.500000000000000000000000000000
172,172_0,COMPLETED,BoTorch,0.251871683550410008223252589232,50,0.361340662863280592809189784020,0.500000000000000000000000000000
173,49_0,COMPLETED,BoTorch,0.251445972021225316872516941658,50,1.000000000000000000000000000000,0.500000000000000000000000000000
174,174_0,COMPLETED,BoTorch,0.296993487698986924350208482792,328,0.251555677317082893029720480627,0.701927577704191230090202680003
175,175_0,COMPLETED,BoTorch,0.279448866377231097679612048523,175,0.123433384040146337801147069513,0.541190282904247421669197137817
176,176_0,COMPLETED,BoTorch,0.264456102267245585224486603693,93,0.440465000259538608240461599053,0.530974039488364524252972387330
177,177_0,COMPLETED,BoTorch,0.251515918958031825525267777266,50,0.404368779577207737574440216122,0.500000000000000000000000000000
178,178_0,COMPLETED,BoTorch,0.296158948383984599317386710027,335,0.328728231590496355885022694565,0.541447449674491987359203903907
179,49_0,COMPLETED,BoTorch,0.251547274481427884929019000992,50,1.000000000000000000000000000000,0.500000000000000000000000000000
180,49_0,COMPLETED,BoTorch,0.251328991799324663602988039202,50,1.000000000000000000000000000000,0.500000000000000000000000000000
181,49_0,COMPLETED,BoTorch,0.251386878919440448498789919540,50,1.000000000000000000000000000000,0.500000000000000000000000000000
182,49_0,COMPLETED,BoTorch,0.251413410516160173990840576153,50,1.000000000000000000000000000000,0.500000000000000000000000000000
183,183_0,COMPLETED,BoTorch,0.250560781476121596789141676709,50,0.320680022736107239733627238820,0.521644708558210767890273018565
184,49_0,COMPLETED,BoTorch,0.251391702846116782410490486654,50,1.000000000000000000000000000000,0.500000000000000000000000000000
185,49_0,COMPLETED,BoTorch,0.251369995176073279807837934641,50,1.000000000000000000000000000000,0.500000000000000000000000000000
186,186_0,COMPLETED,BoTorch,0.278564881813796461074161925353,158,0.574378353434689903345145012281,0.556240660920693796853697676852
187,187_0,COMPLETED,BoTorch,0.278480463096960950686309388402,155,0.576519214656641798555369859969,0.563019247069350692314060324861
188,49_0,COMPLETED,BoTorch,0.251345875542691721271637561586,50,1.000000000000000000000000000000,0.500000000000000000000000000000
189,49_0,COMPLETED,BoTorch,0.251360347322720723006739262928,50,1.000000000000000000000000000000,0.500000000000000000000000000000
190,49_0,COMPLETED,BoTorch,0.251460443801254207585316180484,50,1.000000000000000000000000000000,0.500000000000000000000000000000
191,49_0,COMPLETED,BoTorch,0.251348287506029888227487845143,50,1.000000000000000000000000000000,0.500000000000000000000000000000
192,49_0,COMPLETED,BoTorch,0.251351905451037138661263270478,50,1.000000000000000000000000000000,0.500000000000000000000000000000
193,49_0,COMPLETED,BoTorch,0.251348287506029888227487845143,50,1.000000000000000000000000000000,0.500000000000000000000000000000
194,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000
195,49_0,COMPLETED,BoTorch,0.251373613121080530241613359976,50,1.000000000000000000000000000000,0.500000000000000000000000000000
196,196_0,COMPLETED,BoTorch,0.251373613121080530241613359976,50,0.999999999999999888977697537484,0.500000000000000000000000000000
197,197_0,COMPLETED,BoTorch,0.265085624698504607188453974231,95,0.812039849720895268170295366872,0.729513344448059819491447797191
198,198_0,COMPLETED,BoTorch,0.277319102749638224381101281324,150,0.695445684135409747383960166189,0.545450451672552905080237906077
199,199_0,COMPLETED,BoTorch,0.293128316449589920722473834758,369,0.562304641019254702882790297735,0.714826373893722277763629335823
200,200_0,COMPLETED,BoTorch,0.288236854799807096227937108779,278,0.522633547987788937838615765941,0.711401487234979867935180664062
201,201_0,COMPLETED,BoTorch,0.250968403280270146993302660121,50,0.881645372230559609683098187816,0.579951639752835035324096679688
202,49_0,COMPLETED,BoTorch,0.251766763145200189732975104562,50,1.000000000000000000000000000000,0.500000000000000000000000000000
203,49_0,COMPLETED,BoTorch,0.251355523396044389095038695814,50,1.000000000000000000000000000000,0.500000000000000000000000000000
204,204_0,RUNNING,BoTorch,,73,0.608640702348202444760261187184,0.750569714233279228210449218750
205,205_0,COMPLETED,BoTorch,0.259301736613603450720688670117,73,0.729538020677864551544189453125,0.529738754127174638064445844066
206,206_0,COMPLETED,BoTorch,0.255412445730824888556753649027,57,0.875585343111211833466711595975,0.723723907253296383501606214850
207,207_0,COMPLETED,BoTorch,0.265888808490110983839826985786,103,0.500345596391707725381081672822,0.508012700453400634081901898753
208,208_0,COMPLETED,BoTorch,0.294546550892426428625014978024,366,0.378925083018839425896828743134,0.641226308513432763369621625316
209,209_0,COMPLETED,BoTorch,0.252034491075735611609331954241,50,0.313665793690479532962456232781,0.522880117065096849948702129041
210,210_0,COMPLETED,BoTorch,0.252036903039073778565182237799,50,0.313867609092131738535158547165,0.525154920182485551727324946114
211,211_0,COMPLETED,BoTorch,0.268277858176555694669218610215,102,0.533779872395098275994484993134,0.726147207338363021023042165325
212,212_0,COMPLETED,BoTorch,0.281494211287988460590270278772,195,0.144490762334316985571192049065,0.585501269903033971786499023438
213,213_0,COMPLETED,BoTorch,0.252205740472744799340887311700,50,0.719497866090387128146232953441,0.631079064216464757919311523438
214,49_0,COMPLETED,BoTorch,0.251505065123010185246243963775,50,1.000000000000000000000000000000,0.500000000000000000000000000000
215,215_0,COMPLETED,BoTorch,0.257274481427882339978907566547,68,0.677391667863296964569030933490,0.592357463650894366935517609818
216,49_0,COMPLETED,BoTorch,0.251455619874577873673615613370,50,1.000000000000000000000000000000,0.500000000000000000000000000000
217,217_0,COMPLETED,BoTorch,0.253780752532561493239882111084,50,0.834201061827886447552771187475,0.634106695610154647013700923708
218,218_0,COMPLETED,BoTorch,0.256015436565364185028670362954,62,0.885037713684141635894775390625,0.535661337058991215975822797191
219,49_0,RUNNING,BoTorch,,50,1.000000000000000000000000000000,0.500000000000000000000000000000
220,220_0,COMPLETED,BoTorch,0.253400868306801752005696926062,50,0.272383149347841913012757686374,0.726599773571164120333776281768
221,221_0,COMPLETED,BoTorch,0.253016160154365676859811173927,50,0.272175176703347121431875166309,0.726611954605530852013828280178
222,222_0,COMPLETED,BoTorch,0.252144235407621764011310006026,50,0.407428847847306596641203668696,0.634223804858913831061784094345
223,49_0,COMPLETED,BoTorch,0.251345875542691721271637561586,50,1.000000000000000000000000000000,0.500000000000000000000000000000
224,224_0,COMPLETED,BoTorch,0.252238301977809942222563677205,50,0.540634313016997047895983996568,0.642230741774745927941125955840
225,49_0,COMPLETED,BoTorch,0.251344669561022637793712419807,50,1.000000000000000000000000000000,0.500000000000000000000000000000
226,49_0,COMPLETED,BoTorch,0.251355523396044389095038695814,50,1.000000000000000000000000000000,0.500000000000000000000000000000
227,49_0,COMPLETED,BoTorch,0.251532802701398994216219762166,50,1.000000000000000000000000000000,0.500000000000000000000000000000
228,228_0,COMPLETED,BoTorch,0.251292812349252270287536248361,50,0.376597473663913384633872283302,0.621272645944888624036650526250
229,229_0,COMPLETED,BoTorch,0.252919681620839331692707219190,50,0.638449047655448298499436532438,0.640020329041448121643043123186
230,230_0,COMPLETED,BoTorch,0.251958514230583685566955409740,50,0.628657027610797891625793454295,0.637703860573932579391964736715
231,49_0,COMPLETED,BoTorch,0.251331403762662830558838322759,50,1.000000000000000000000000000000,0.500000000000000000000000000000
232,49_0,COMPLETED,BoTorch,0.251454413892908790195690471592,50,1.000000000000000000000000000000,0.500000000000000000000000000000
233,233_0,COMPLETED,BoTorch,0.251626869271587061405170970829,50,1.000000000000000000000000000000,0.522680301652058698635983091663
234,234_0,COMPLETED,BoTorch,0.250554751567776179399515967816,50,0.334516068035660474322412483161,0.523658365062494191022324230289
235,49_0,COMPLETED,BoTorch,0.251320549927641079257512046752,50,1.000000000000000000000000000000,0.500000000000000000000000000000
236,236_0,COMPLETED,BoTorch,0.251729377713458712939598171943,50,0.419435216470962379808895548194,0.500000000000000000000000000000
237,49_0,RUNNING,BoTorch,,50,1.000000000000000000000000000000,0.500000000000000000000000000000
238,49_0,COMPLETED,BoTorch,0.251304872165943105066787666146,50,1.000000000000000000000000000000,0.500000000000000000000000000000
239,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000
240,240_0,COMPLETED,BoTorch,0.250891220453449137473000973841,50,0.426824203149736791651491785160,0.500000000000000000000000000000
241,49_0,COMPLETED,BoTorch,0.251378437047756864153313927090,50,1.000000000000000000000000000000,0.500000000000000000000000000000
242,242_0,COMPLETED,BoTorch,0.251636517124939729228572105058,50,0.435300350969221483410365181044,0.640204128896388224845281911257
243,243_0,COMPLETED,BoTorch,0.251214423540762177289309420303,50,0.388517069086351418683022984624,0.500000000000000000000000000000
244,244_0,COMPLETED,BoTorch,0.252451760733236829636894071882,50,0.323140383945104137097814600565,0.523904298951193947431193009834
245,245_0,COMPLETED,BoTorch,0.250940665701881338023326861730,50,0.321300731777231451946619245064,0.524587547384697372443440599454
246,49_0,COMPLETED,BoTorch,0.251366377231066140396364971821,50,1.000000000000000000000000000000,0.500000000000000000000000000000
247,49_0,COMPLETED,BoTorch,0.251351905451037138661263270478,50,1.000000000000000000000000000000,0.500000000000000000000000000000
248,49_0,COMPLETED,BoTorch,0.251318137964302912301661763195,50,1.000000000000000000000000000000,0.500000000000000000000000000000
249,249_0,COMPLETED,BoTorch,0.252392667631451961263167049765,50,0.357893535285238595378132231417,0.623917419872544121162150076998
250,250_0,COMPLETED,BoTorch,0.278118668596237350598698867543,153,0.430355031007679911247976178856,0.621694231706615973997998025879
251,251_0,COMPLETED,BoTorch,0.251607573564881836780671164888,50,0.383084185083425898810105536541,0.500000000000000000000000000000
252,49_0,COMPLETED,BoTorch,0.251489387361312100033217120654,50,1.000000000000000000000000000000,0.500000000000000000000000000000
253,49_0,COMPLETED,BoTorch,0.251472503617945042364567598270,50,1.000000000000000000000000000000,0.500000000000000000000000000000
254,49_0,COMPLETED,BoTorch,0.251642547033285146618197813950,50,1.000000000000000000000000000000,0.500000000000000000000000000000
255,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000
256,256_0,COMPLETED,BoTorch,0.250779064158224818115172638500,50,0.886064076616317475298956196639,0.588395452912066074802055481996
257,49_0,COMPLETED,BoTorch,0.251540038591413384061468150321,50,1.000000000000000000000000000000,0.500000000000000000000000000000
258,49_0,COMPLETED,BoTorch,0.251436324167872649049115807429,50,1.000000000000000000000000000000,0.500000000000000000000000000000
259,49_0,COMPLETED,BoTorch,0.251374819102749613719538501755,50,1.000000000000000000000000000000,0.500000000000000000000000000000
260,49_0,COMPLETED,BoTorch,0.251391702846116782410490486654,50,1.000000000000000000000000000000,0.500000000000000000000000000000
261,49_0,COMPLETED,BoTorch,0.251350699469368055183338128700,50,1.000000000000000000000000000000,0.500000000000000000000000000000
262,262_0,COMPLETED,BoTorch,0.252297395079594810596290699323,50,0.336735949722954841512034818152,0.523920643391982188141753340460
263,49_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.500000000000000000000000000000
264,264_0,COMPLETED,BoTorch,0.251213217559093093811384278524,50,0.338561593741065869345163719117,0.523138814727089451750430271204
265,265_0,COMPLETED,BoTorch,0.251443560057887149916666658100,50,0.341438401383369727337679933044,0.523115100489205397948921927309
266,49_0,COMPLETED,BoTorch,0.251439942112879899482891232765,50,1.000000000000000000000000000000,0.500000000000000000000000000000
267,49_0,COMPLETED,BoTorch,0.251514712976362742047342635487,50,1.000000000000000000000000000000,0.500000000000000000000000000000
268,49_0,COMPLETED,BoTorch,0.251363965267727973440514688264,50,1.000000000000000000000000000000,0.500000000000000000000000000000
269,49_0,COMPLETED,BoTorch,0.251392908827785865888415628433,50,1.000000000000000000000000000000,0.500000000000000000000000000000
270,49_0,COMPLETED,BoTorch,0.251437530149541732527040949208,50,1.000000000000000000000000000000,0.500000000000000000000000000000
271,49_0,COMPLETED,BoTorch,0.251380849011095031109164210648,50,1.000000000000000000000000000000,0.500000000000000000000000000000
272,49_0,COMPLETED,BoTorch,0.251373613121080530241613359976,50,1.000000000000000000000000000000,0.500000000000000000000000000000
273,49_0,COMPLETED,BoTorch,0.251406174626145673123289725481,50,1.000000000000000000000000000000,0.500000000000000000000000000000
274,49_0,COMPLETED,BoTorch,0.251429088277858148181564956758,50,1.000000000000000000000000000000,0.500000000000000000000000000000
275,49_0,COMPLETED,BoTorch,0.251429088277858148181564956758,50,1.000000000000000000000000000000,0.500000000000000000000000000000
276,49_0,COMPLETED,BoTorch,0.251367583212735112851987651084,50,1.000000000000000000000000000000,0.500000000000000000000000000000
277,277_0,COMPLETED,BoTorch,0.264933671008200644081398422713,94,0.509158919838748280639606491604,0.525822675579071363571870278975
278,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000
279,279_0,COMPLETED,BoTorch,0.252723106608779501947026346897,50,0.331958254155426124576422353130,0.521162249483033246377772229607
280,49_0,COMPLETED,BoTorch,0.251359141341051639528814121149,50,1.000000000000000000000000000000,0.500000000000000000000000000000
281,49_0,COMPLETED,BoTorch,0.251415822479498340946690859710,50,1.000000000000000000000000000000,0.500000000000000000000000000000
282,49_0,COMPLETED,BoTorch,0.251464061746261458019091605820,50,1.000000000000000000000000000000,0.500000000000000000000000000000
283,49_0,COMPLETED,BoTorch,0.251322961890979246213362330309,50,1.000000000000000000000000000000,0.500000000000000000000000000000
284,49_0,COMPLETED,BoTorch,0.251330197780993747080913180980,50,1.000000000000000000000000000000,0.500000000000000000000000000000
285,49_0,COMPLETED,BoTorch,0.251424264351181814269864389644,50,1.000000000000000000000000000000,0.500000000000000000000000000000
286,286_0,COMPLETED,BoTorch,0.251456825856246957151540755149,50,0.338891192458694701095112122857,0.521165620101966431398921031359
287,49_0,COMPLETED,BoTorch,0.251426676314519981225714673201,50,1.000000000000000000000000000000,0.500000000000000000000000000000
288,49_0,COMPLETED,BoTorch,0.251306078147612188544712807925,50,1.000000000000000000000000000000,0.500000000000000000000000000000
289,49_0,COMPLETED,BoTorch,0.251376025084418697197463643533,50,1.000000000000000000000000000000,0.500000000000000000000000000000
290,49_0,COMPLETED,BoTorch,0.251536420646406133627692724986,50,1.000000000000000000000000000000,0.500000000000000000000000000000
291,291_0,COMPLETED,BoTorch,0.258896526772793067472377970262,69,0.736267914228911357632512135751,0.587919343787918324828467575571
292,292_0,COMPLETED,BoTorch,0.251807766521948916960127462517,50,0.434115100073588111584399484855,0.500000000000000000000000000000
293,293_0,COMPLETED,BoTorch,0.250700675349734725116945810441,50,0.449220954454323084803490928607,0.500000000000000000000000000000
294,294_0,COMPLETED,BoTorch,0.251218041485769427723084845638,50,0.334877103582512880564081569901,0.517370703139367282830107797054
295,49_0,COMPLETED,BoTorch,0.251379643029425947631239068869,50,1.000000000000000000000000000000,0.500000000000000000000000000000
296,296_0,COMPLETED,BoTorch,0.284559816690786338000407340587,232,0.858447136604702287776547109388,0.502636864340298195052980645414
297,49_0,COMPLETED,BoTorch,0.251354317414375305617113554035,50,1.000000000000000000000000000000,0.500000000000000000000000000000
298,49_0,COMPLETED,BoTorch,0.251321755909310162735437188530,50,1.000000000000000000000000000000,0.500000000000000000000000000000
299,49_0,COMPLETED,BoTorch,0.251321755909310162735437188530,50,1.000000000000000000000000000000,0.500000000000000000000000000000
300,300_0,COMPLETED,BoTorch,0.250688615533043890337694392656,50,0.828624249929255540791928069666,0.597374754398358520646183933422
301,301_0,COMPLETED,BoTorch,0.251918716835504152840030656080,50,0.433667399807162401437210519362,0.500000000000000000000000000000
302,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000
303,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000
304,304_0,COMPLETED,BoTorch,0.254131693198263342026166355936,51,0.514381740116149455133154333453,0.725166554925076756177304559969
305,305_0,COMPLETED,BoTorch,0.261857211770381126392237547407,89,0.426329301492391232741852036270,0.500000000000000000000000000000
306,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000
307,307_0,COMPLETED,BoTorch,0.272392667631451979026735443767,128,0.393594465497881240700905891572,0.660716956108808539660515180003
308,49_0,COMPLETED,BoTorch,0.251382054992764114587089352426,50,1.000000000000000000000000000000,0.500000000000000000000000000000
309,309_0,COMPLETED,BoTorch,0.252257597684515166847063483146,50,0.456351340070217048605627496727,0.500000000000000000000000000000
310,310_0,COMPLETED,BoTorch,0.251529184756391743782444336830,50,0.443011532699750909536362541985,0.500000000000000000000000000000
311,311_0,COMPLETED,BoTorch,0.251039556198745739123978637508,50,0.427438063722970662539069053310,0.500000000000000000000000000000
312,312_0,COMPLETED,BoTorch,0.251957308248914602089030267962,50,0.410750494790746767392874971847,0.500000000000000000000000000000
313,49_0,COMPLETED,BoTorch,0.251384466956102281542939635983,50,1.000000000000000000000000000000,0.500000000000000000000000000000
314,49_0,COMPLETED,BoTorch,0.251430294259527231659490098536,50,1.000000000000000000000000000000,0.500000000000000000000000000000
315,315_0,COMPLETED,BoTorch,0.253857935359382502760183797363,58,0.578571700250729792358583836176,0.558180085475421750018654165615
316,49_0,COMPLETED,BoTorch,0.251335021707670080992613748094,50,1.000000000000000000000000000000,0.500000000000000000000000000000
317,317_0,COMPLETED,BoTorch,0.251372407139411446763688218198,50,1.000000000000000000000000000000,0.528079923473708134906701161526
318,49_0,COMPLETED,BoTorch,0.251442354076218066438741516322,50,1.000000000000000000000000000000,0.500000000000000000000000000000
319,49_0,COMPLETED,BoTorch,0.251386878919440448498789919540,50,1.000000000000000000000000000000,0.500000000000000000000000000000
320,49_0,COMPLETED,BoTorch,0.251567776169802193031443948712,50,1.000000000000000000000000000000,0.500000000000000000000000000000
321,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000
322,49_0,COMPLETED,BoTorch,0.251321755909310162735437188530,50,1.000000000000000000000000000000,0.500000000000000000000000000000
323,49_0,COMPLETED,BoTorch,0.251402556681138422689514300146,50,1.000000000000000000000000000000,0.500000000000000000000000000000
324,49_0,COMPLETED,BoTorch,0.251402556681138422689514300146,50,1.000000000000000000000000000000,0.500000000000000000000000000000
325,49_0,COMPLETED,BoTorch,0.251431500241196315137415240315,50,1.000000000000000000000000000000,0.500000000000000000000000000000
326,49_0,COMPLETED,BoTorch,0.251423058369512730791939247865,50,1.000000000000000000000000000000,0.500000000000000000000000000000
327,49_0,COMPLETED,BoTorch,0.251436324167872649049115807429,50,1.000000000000000000000000000000,0.500000000000000000000000000000
328,49_0,COMPLETED,BoTorch,0.251452001929570623239840188035,50,1.000000000000000000000000000000,0.500000000000000000000000000000
329,329_0,COMPLETED,BoTorch,0.251575012059816693898994799383,50,1.000000000000000000000000000000,0.572794482800278115242065268831
330,49_0,COMPLETED,BoTorch,0.251353111432706222139188412257,50,1.000000000000000000000000000000,0.500000000000000000000000000000
331,49_0,COMPLETED,BoTorch,0.251351905451037138661263270478,50,1.000000000000000000000000000000,0.500000000000000000000000000000
332,332_0,COMPLETED,BoTorch,0.251541244573082467539393292100,50,1.000000000000000000000000000000,0.574189837261398050571870044223
333,49_0,COMPLETED,BoTorch,0.251320549927641079257512046752,50,1.000000000000000000000000000000,0.500000000000000000000000000000
334,49_0,COMPLETED,BoTorch,0.251320549927641079257512046752,50,1.000000000000000000000000000000,0.500000000000000000000000000000
335,335_0,COMPLETED,BoTorch,0.251882537385431759524578865239,50,0.377275544647041383150565252436,0.634693950816361818212385514926
336,336_0,COMPLETED,BoTorch,0.250826097443318851709648242831,50,0.347007360465096947876872945926,0.519107233024286407463421255670
337,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000
338,49_0,COMPLETED,BoTorch,0.251362759286058889962589546485,50,1.000000000000000000000000000000,0.500000000000000000000000000000
339,49_0,COMPLETED,BoTorch,0.251374819102749613719538501755,50,1.000000000000000000000000000000,0.500000000000000000000000000000
340,49_0,COMPLETED,BoTorch,0.251373613121080530241613359976,50,1.000000000000000000000000000000,0.500000000000000000000000000000
341,49_0,COMPLETED,BoTorch,0.251472503617945042364567598270,50,1.000000000000000000000000000000,0.500000000000000000000000000000
342,342_0,COMPLETED,BoTorch,0.254166666666666651863693004998,52,0.705545808622103032270445055474,0.704248412788929578631780259457
343,49_0,COMPLETED,BoTorch,0.251486975397973933077366837097,50,1.000000000000000000000000000000,0.500000000000000000000000000000
344,49_0,COMPLETED,BoTorch,0.251413410516160173990840576153,50,1.000000000000000000000000000000,0.500000000000000000000000000000
345,49_0,COMPLETED,BoTorch,0.251432706222865398615340382094,50,1.000000000000000000000000000000,0.500000000000000000000000000000
346,49_0,COMPLETED,BoTorch,0.251409792571152923557065150817,50,1.000000000000000000000000000000,0.500000000000000000000000000000
347,49_0,COMPLETED,BoTorch,0.251976603955619826713530073903,50,1.000000000000000000000000000000,0.500000000000000000000000000000
348,49_0,COMPLETED,BoTorch,0.251390496864447698932565344876,50,1.000000000000000000000000000000,0.500000000000000000000000000000
349,349_0,COMPLETED,BoTorch,0.251390496864447698932565344876,50,0.326003987846205767198171088239,0.518536790089199506326167465886
350,49_0,COMPLETED,BoTorch,0.251363965267727973440514688264,50,1.000000000000000000000000000000,0.500000000000000000000000000000
351,49_0,COMPLETED,BoTorch,0.252080318379160672748184879310,50,1.000000000000000000000000000000,0.500000000000000000000000000000
352,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000
353,353_0,COMPLETED,BoTorch,0.263311625663289916587928018998,91,0.407975186128169298171997070312,0.572589845489710591586174359691
354,49_0,COMPLETED,BoTorch,0.251379643029425947631239068869,50,1.000000000000000000000000000000,0.500000000000000000000000000000
355,49_0,COMPLETED,BoTorch,0.251312108055957494912036054302,50,1.000000000000000000000000000000,0.500000000000000000000000000000
356,49_0,COMPLETED,BoTorch,0.251397732754462088777813733032,50,1.000000000000000000000000000000,0.500000000000000000000000000000
357,49_0,RUNNING,BoTorch,,50,1.000000000000000000000000000000,0.500000000000000000000000000000
358,49_0,COMPLETED,BoTorch,0.251327785817655580125062897423,50,1.000000000000000000000000000000,0.500000000000000000000000000000
359,359_0,COMPLETED,BoTorch,0.251740231548480464240924447950,50,0.329808756082748955051897610247,0.520559175718262046039797041885
360,49_0,COMPLETED,BoTorch,0.251359141341051639528814121149,50,1.000000000000000000000000000000,0.500000000000000000000000000000
361,49_0,COMPLETED,BoTorch,0.251484563434635766121516553540,50,1.000000000000000000000000000000,0.500000000000000000000000000000
362,49_0,COMPLETED,BoTorch,0.251359141341051639528814121149,50,1.000000000000000000000000000000,0.500000000000000000000000000000
363,49_0,COMPLETED,BoTorch,0.251359141341051639528814121149,50,1.000000000000000000000000000000,0.500000000000000000000000000000
364,49_0,COMPLETED,BoTorch,0.251338639652677331426389173430,50,1.000000000000000000000000000000,0.500000000000000000000000000000
365,365_0,COMPLETED,BoTorch,0.251940424505547544420380745578,50,0.123631102168718656764134777859,0.544434961989420096450942310184
366,49_0,COMPLETED,BoTorch,0.251391702846116782410490486654,50,1.000000000000000000000000000000,0.500000000000000000000000000000
367,367_0,COMPLETED,BoTorch,0.268289917993246529448470028001,105,0.463625407312065362930297851562,0.708491245470941111150864344381
368,368_0,COMPLETED,BoTorch,0.250979257115291898294628936128,50,0.327889366115563185566372794710,0.519296617759852696671885041724
369,49_0,COMPLETED,BoTorch,0.251310902074288522456413375039,50,1.000000000000000000000000000000,0.500000000000000000000000000000
370,49_0,COMPLETED,BoTorch,0.251319343945971995779586904973,50,1.000000000000000000000000000000,0.500000000000000000000000000000
371,49_0,COMPLETED,BoTorch,0.251342257597684470837862136250,50,1.000000000000000000000000000000,0.500000000000000000000000000000
372,372_0,COMPLETED,BoTorch,0.250488422575976810158238095028,50,0.327738276475278145216663006067,0.519809992430224521164916495763
373,373_0,COMPLETED,BoTorch,0.251207187650747676421758569631,50,0.508438171343688405379168671061,0.556993980238750796551983057725
374,374_0,COMPLETED,BoTorch,0.253458755426917536901498806401,50,0.100000000000000005551115123126,0.753471114222934001958265071153
375,375_0,COMPLETED,BoTorch,0.251117945007235943144507928082,50,0.334445732391481342915540153626,0.520270660469996593100461268477
376,376_0,COMPLETED,BoTorch,0.251125180897250332989756316238,50,0.329865301027407964440385512717,0.520325837989092043756045313785
377,377_0,COMPLETED,BoTorch,0.255305113362276903110625880799,50,0.101262745368288289982139360745,0.752675319962081257330055450439
378,378_0,COMPLETED,BoTorch,0.252437288953207938924094833055,50,1.000000000000000000000000000000,0.566792399685001058173838828225
379,379_0,COMPLETED,BoTorch,0.253429811866859644453597866232,55,0.178471425268799055441348855311,0.606161984521895624844489702809
380,49_0,COMPLETED,BoTorch,0.251474915581283209320417881827,50,1.000000000000000000000000000000,0.500000000000000000000000000000
381,49_0,COMPLETED,BoTorch,0.251355523396044389095038695814,50,1.000000000000000000000000000000,0.500000000000000000000000000000
382,49_0,COMPLETED,BoTorch,0.251391702846116782410490486654,50,1.000000000000000000000000000000,0.500000000000000000000000000000
383,49_0,COMPLETED,BoTorch,0.251488181379643016555291978875,50,1.000000000000000000000000000000,0.500000000000000000000000000000
384,384_0,COMPLETED,BoTorch,0.252680897250361802264251309680,51,0.364245615345538786833401445620,0.672357374614316771754829460406
385,385_0,COMPLETED,BoTorch,0.252094790159189563460984118137,50,0.317564938543839048179506789893,0.518357216733121739515866011061
386,386_0,COMPLETED,BoTorch,0.251766763145200189732975104562,50,0.319892777414326423546242494922,0.519688492246499933457926090341
387,49_0,COMPLETED,BoTorch,0.251330197780993747080913180980,50,1.000000000000000000000000000000,0.500000000000000000000000000000
388,388_0,COMPLETED,BoTorch,0.259822720694645403760603130650,69,0.434382828883713267842381355877,0.683587023895084855418247116177
389,49_0,COMPLETED,BoTorch,0.251561746261456775641818239819,50,1.000000000000000000000000000000,0.500000000000000000000000000000
390,49_0,COMPLETED,BoTorch,0.251376025084418697197463643533,50,1.000000000000000000000000000000,0.500000000000000000000000000000
391,49_0,COMPLETED,BoTorch,0.251332609744331914036763464537,50,1.000000000000000000000000000000,0.500000000000000000000000000000
392,392_0,COMPLETED,BoTorch,0.251172214182344477606534383085,50,0.337347589695743688142925975626,0.524320934202875621465977928892
393,393_0,COMPLETED,BoTorch,0.254768451519536864857684577146,62,0.182018505036831701460187105113,0.508170366846027010687691927160
394,49_0,COMPLETED,BoTorch,0.251441148094548982960816374543,50,1.000000000000000000000000000000,0.500000000000000000000000000000
395,49_0,COMPLETED,BoTorch,0.251331403762662830558838322759,50,1.000000000000000000000000000000,0.500000000000000000000000000000
396,49_0,COMPLETED,BoTorch,0.251541244573082467539393292100,50,1.000000000000000000000000000000,0.500000000000000000000000000000
397,49_0,COMPLETED,BoTorch,0.251570188133140359987294232269,50,1.000000000000000000000000000000,0.500000000000000000000000000000
398,398_0,COMPLETED,BoTorch,0.250687409551374806859769250877,50,0.285710091236978769302368164062,0.523175721708685204092148524069
399,399_0,COMPLETED,BoTorch,0.251572600096478526943144515826,50,0.618499628454446837011460047506,0.513840959593653723302963953756
400,400_0,COMPLETED,BoTorch,0.278392426435118189864681426116,154,0.344786846604970609320162111544,0.557995953474535899552222417697
401,401_0,COMPLETED,BoTorch,0.263473227206946436496082242229,85,0.572245974186807893069328656566,0.536112042050808645932136187184
402,402_0,COMPLETED,BoTorch,0.251523154848046326392818627937,50,0.351960038269307884384318185766,0.524532149794632696071516875236
403,49_0,COMPLETED,BoTorch,0.251513506994693658569417493709,50,1.000000000000000000000000000000,0.500000000000000000000000000000
404,49_0,COMPLETED,BoTorch,0.251368789194404196329912792862,50,1.000000000000000000000000000000,0.500000000000000000000000000000
405,49_0,COMPLETED,BoTorch,0.251419440424505591380466285045,50,1.000000000000000000000000000000,0.500000000000000000000000000000
406,49_0,COMPLETED,BoTorch,0.251443560057887149916666658100,50,1.000000000000000000000000000000,0.500000000000000000000000000000
407,407_0,COMPLETED,BoTorch,0.265583695127834085170093203487,90,0.706029953047340175231738612638,0.740050616156303853188092034543
408,49_0,COMPLETED,BoTorch,0.251511095031355491613567210152,50,1.000000000000000000000000000000,0.500000000000000000000000000000
409,49_0,COMPLETED,BoTorch,0.251466473709599624974941889377,50,1.000000000000000000000000000000,0.500000000000000000000000000000
410,49_0,COMPLETED,BoTorch,0.251477327544621376276268165384,50,1.000000000000000000000000000000,0.500000000000000000000000000000
411,411_0,COMPLETED,BoTorch,0.251514712976362742047342635487,50,0.341099376161345446689665550366,0.524204401133920305788649329770
412,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000
413,49_0,COMPLETED,BoTorch,0.251302460202604938110937382589,50,1.000000000000000000000000000000,0.500000000000000000000000000000
414,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000
415,49_0,COMPLETED,BoTorch,0.251380849011095031109164210648,50,1.000000000000000000000000000000,0.500000000000000000000000000000
416,416_0,COMPLETED,BoTorch,0.250593342981186739670818042214,50,0.322582618416136168715269150198,0.525419156054855562842931249179
417,417_0,COMPLETED,BoTorch,0.251849975880366616642902499734,50,0.316867364915205451669777403367,0.524422635368591127935644635727
418,49_0,COMPLETED,BoTorch,0.251476121562952292798343023605,50,1.000000000000000000000000000000,0.500000000000000000000000000000
419,49_0,COMPLETED,BoTorch,0.251390496864447698932565344876,50,1.000000000000000000000000000000,0.500000000000000000000000000000
420,49_0,COMPLETED,BoTorch,0.251418234442836507902541143267,50,1.000000000000000000000000000000,0.500000000000000000000000000000
421,49_0,COMPLETED,BoTorch,0.251418234442836507902541143267,50,1.000000000000000000000000000000,0.500000000000000000000000000000
422,422_0,COMPLETED,BoTorch,0.251663048721659454720622761670,50,1.000000000000000000000000000000,0.582455746457651524217169480835
423,423_0,COMPLETED,BoTorch,0.254056922334780499461714953213,52,0.739283017293395960933821697836,0.705416982116536916969096182584
424,49_0,COMPLETED,BoTorch,0.251337433671008247948464031651,50,1.000000000000000000000000000000,0.500000000000000000000000000000
425,49_0,COMPLETED,BoTorch,0.251443560057887149916666658100,50,1.000000000000000000000000000000,0.500000000000000000000000000000
426,49_0,COMPLETED,BoTorch,0.251366377231066140396364971821,50,1.000000000000000000000000000000,0.500000000000000000000000000000
427,427_0,COMPLETED,BoTorch,0.252553063193439508715698593733,50,1.000000000000000000000000000000,0.577082589518987942867056517571
428,49_0,COMPLETED,BoTorch,0.251343463579353554315787278028,50,1.000000000000000000000000000000,0.500000000000000000000000000000
429,429_0,COMPLETED,BoTorch,0.252058610709117170145532327297,50,0.317865566343900729506799507362,0.524030044245835657790166806080
430,49_0,COMPLETED,BoTorch,0.251523154848046326392818627937,50,1.000000000000000000000000000000,0.500000000000000000000000000000
431,49_0,COMPLETED,BoTorch,0.251356729377713472572963837592,50,1.000000000000000000000000000000,0.500000000000000000000000000000
432,49_0,COMPLETED,BoTorch,0.251546068499758801451093859214,50,1.000000000000000000000000000000,0.500000000000000000000000000000
433,49_0,COMPLETED,BoTorch,0.251326579835986496647137755645,50,1.000000000000000000000000000000,0.500000000000000000000000000000
434,49_0,COMPLETED,BoTorch,0.251777616980221941034301380569,50,1.000000000000000000000000000000,0.500000000000000000000000000000
435,49_0,COMPLETED,BoTorch,0.251672696575012011521721433382,50,1.000000000000000000000000000000,0.500000000000000000000000000000
436,49_0,COMPLETED,BoTorch,0.251388084901109531976715061319,50,1.000000000000000000000000000000,0.500000000000000000000000000000
437,49_0,COMPLETED,BoTorch,0.251395320791123921821963449474,50,1.000000000000000000000000000000,0.500000000000000000000000000000
438,49_0,COMPLETED,BoTorch,0.251474915581283209320417881827,50,1.000000000000000000000000000000,0.500000000000000000000000000000
439,49_0,COMPLETED,BoTorch,0.251409792571152923557065150817,50,1.000000000000000000000000000000,0.500000000000000000000000000000
440,440_0,COMPLETED,BoTorch,0.251799324650265332614651470067,50,1.000000000000000000000000000000,0.585254528204475210628743298003
441,49_0,COMPLETED,BoTorch,0.251339845634346303882011852693,50,1.000000000000000000000000000000,0.500000000000000000000000000000
442,49_0,COMPLETED,BoTorch,0.251413410516160173990840576153,50,1.000000000000000000000000000000,0.500000000000000000000000000000
443,49_0,COMPLETED,BoTorch,0.251383260974433198065014494205,50,1.000000000000000000000000000000,0.500000000000000000000000000000
444,444_0,COMPLETED,BoTorch,0.254463338157259966187950794847,52,0.758599611941763063427401903027,0.723681730896888031345781655546
445,49_0,COMPLETED,BoTorch,0.251530390738060827260369478608,50,1.000000000000000000000000000000,0.500000000000000000000000000000
446,49_0,COMPLETED,BoTorch,0.251530390738060827260369478608,50,1.000000000000000000000000000000,0.500000000000000000000000000000
447,447_0,COMPLETED,BoTorch,0.257676073323685472793442841066,56,0.769516202509516911156595142529,0.764395443931254248148832175502
448,49_0,COMPLETED,BoTorch,0.251408586589483840079140009038,50,1.000000000000000000000000000000,0.500000000000000000000000000000
449,449_0,COMPLETED,BoTorch,0.249534491075735664900037136249,50,0.421753624267876214837258430634,0.513595570158213332589980382181
450,450_0,COMPLETED,BoTorch,0.250760974433188565946295511822,50,0.324184863024038927292735934316,0.524196617966436795299500772671
451,49_0,COMPLETED,BoTorch,0.251511095031355491613567210152,50,1.000000000000000000000000000000,0.500000000000000000000000000000
452,49_0,COMPLETED,BoTorch,0.251342257597684470837862136250,50,1.000000000000000000000000000000,0.500000000000000000000000000000
453,49_0,COMPLETED,BoTorch,0.251314520019295661867886337859,50,1.000000000000000000000000000000,0.500000000000000000000000000000
454,49_0,COMPLETED,BoTorch,0.251456825856246957151540755149,50,1.000000000000000000000000000000,0.500000000000000000000000000000
455,49_0,COMPLETED,BoTorch,0.251424264351181814269864389644,50,1.000000000000000000000000000000,0.500000000000000000000000000000
456,456_0,COMPLETED,BoTorch,0.276675108538350200682032209443,158,0.648730381452036430900420782564,0.532822475961984731718246166565
457,49_0,COMPLETED,BoTorch,0.251365171249397056918439830042,50,1.000000000000000000000000000000,0.500000000000000000000000000000
458,49_0,COMPLETED,BoTorch,0.251518330921369992481118060823,50,1.000000000000000000000000000000,0.500000000000000000000000000000
459,459_0,COMPLETED,BoTorch,0.252114085865894788085483924078,50,0.252317400738965524897849945773,0.543508429767719181135987582820
460,460_0,COMPLETED,BoTorch,0.252449348769898662681043788325,50,0.361456154274081953303721093107,0.641854147791773410602900185040
461,461_0,COMPLETED,BoTorch,0.252456584659913163548594638996,52,0.202426617871969943829313365313,0.550424197316169760973991742503
462,49_0,COMPLETED,BoTorch,0.251458031837916040629465896927,50,1.000000000000000000000000000000,0.500000000000000000000000000000
463,49_0,COMPLETED,BoTorch,0.251476121562952292798343023605,50,1.000000000000000000000000000000,0.500000000000000000000000000000
464,49_0,COMPLETED,BoTorch,0.251414616497829257468765717931,50,1.000000000000000000000000000000,0.500000000000000000000000000000
465,465_0,COMPLETED,BoTorch,0.252458996623251330504444922553,51,0.134935280401202756239698032914,0.660540461167679060494606346765
466,466_0,COMPLETED,BoTorch,0.253719247467438457910304805409,56,0.133662553285813473502585679853,0.635395418911348652812876025564
467,467_0,COMPLETED,BoTorch,0.250176073323685521643255924573,50,0.369856802436601683758965464222,0.524582138891190652429941110313
468,468_0,COMPLETED,BoTorch,0.251105885190545108365256510297,50,0.372651302105680315435165539384,0.524496365581944012923543141369
469,49_0,COMPLETED,BoTorch,0.251402556681138422689514300146,50,1.000000000000000000000000000000,0.500000000000000000000000000000
470,470_0,COMPLETED,BoTorch,0.251244573082489153215135502251,50,0.373888261111739450548441254796,0.524896081385635926430666131637
471,49_0,COMPLETED,BoTorch,0.251559334298118719708270418778,50,1.000000000000000000000000000000,0.500000000000000000000000000000
472,49_0,COMPLETED,BoTorch,0.251378437047756864153313927090,50,1.000000000000000000000000000000,0.500000000000000000000000000000
473,49_0,COMPLETED,BoTorch,0.251371201157742363285763076419,50,1.000000000000000000000000000000,0.500000000000000000000000000000
474,49_0,COMPLETED,BoTorch,0.251341051616015387359936994471,50,1.000000000000000000000000000000,0.500000000000000000000000000000
475,49_0,COMPLETED,BoTorch,0.251396526772793005299888591253,50,1.000000000000000000000000000000,0.500000000000000000000000000000
476,476_0,COMPLETED,BoTorch,0.250366618427399933999311087973,50,0.385011005755664492689049893670,0.522602750613219035891177099984
477,477_0,COMPLETED,BoTorch,0.261108297153883284202890990855,86,0.744514949433505490716811436869,0.570286335889250040054321289062
478,49_0,COMPLETED,BoTorch,0.251427882296189064703639814979,50,1.000000000000000000000000000000,0.500000000000000000000000000000
479,479_0,COMPLETED,BoTorch,0.250528219971056453907465311204,50,0.391172553480237827905341418955,0.522766627547402773501517003751
480,480_0,COMPLETED,BoTorch,0.250594548962855712126440721477,50,0.396629973393557433425371527846,0.524016282386301046614107690402
481,481_0,COMPLETED,BoTorch,0.249911963338157239178372037713,50,0.393122961913602964045821863692,0.524252679710148439262695774232
482,49_0,COMPLETED,BoTorch,0.251566570188133109553518806933,50,1.000000000000000000000000000000,0.500000000000000000000000000000
483,483_0,COMPLETED,BoTorch,0.251620839363241644015545261936,50,0.396527269384645997263305616798,0.524410569836330830995052565413
484,49_0,COMPLETED,BoTorch,0.251394114809454949366340770212,50,1.000000000000000000000000000000,0.500000000000000000000000000000
485,485_0,COMPLETED,BoTorch,0.251000964785335289874979025626,50,0.397109511868172115889308315673,0.524555872193372074718809017213
486,486_0,COMPLETED,BoTorch,0.259878195851423021700554727431,78,0.793729635048657677920402875316,0.583341975975781634744521397806
487,49_0,COMPLETED,BoTorch,0.251394114809454949366340770212,50,1.000000000000000000000000000000,0.500000000000000000000000000000
488,49_0,COMPLETED,BoTorch,0.251357935359382556050888979371,50,1.000000000000000000000000000000,0.500000000000000000000000000000
489,489_0,COMPLETED,BoTorch,0.254815484804631009474462643993,62,0.564778322912752650530876508128,0.522606668435037113873420366872
490,490_0,COMPLETED,BoTorch,0.251794500723588998702950902953,51,0.147923926170915376321346457189,0.539880184177309319082382899069
491,49_0,COMPLETED,BoTorch,0.251397732754462088777813733032,50,1.000000000000000000000000000000,0.500000000000000000000000000000
492,492_0,COMPLETED,BoTorch,0.253263386396526790633743075887,54,0.174169476330280292852847878748,0.546222475264221407620368609059
493,49_0,COMPLETED,BoTorch,0.251534008683067966671842441428,50,1.000000000000000000000000000000,0.500000000000000000000000000000
494,494_0,COMPLETED,BoTorch,0.251515918958031825525267777266,50,0.407874216568157033080410656112,0.525486180735032171185139304725
495,495_0,COMPLETED,BoTorch,0.251888567293777176914204574132,50,0.419650814452644960184102274070,0.525669264920565648679939840804
496,496_0,COMPLETED,BoTorch,0.252047756874095529866508513805,50,0.416637460689971539373743780743,0.527253421683711365197666509630
497,497_0,COMPLETED,BoTorch,0.250143511818620378761579559068,50,0.572022732254117705075202593434,0.550174748525023460388183593750
498,498_0,COMPLETED,BoTorch,0.291204775687409544993045074079,291,0.920848842617221108319824907085,0.735204365011294269649511079479
499,499_0,COMPLETED,BoTorch,0.287357694163048682511885090207,257,0.537422461435198783874511718750,0.629534575622528791427612304688
500,500_0,COMPLETED,BoTorch,0.251239749155812819303434935136,50,0.417325344358840943925770261558,0.527318303893845707897014563059
</pre>
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<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> CPU/RAM-Usage (main)</h1>
<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
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<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727627245,473.546875,49.7
1727627245,473.5859375,47.2
1727627246,473.65234375,49.9
1727627246,473.65234375,39.4
1727627246,473.65234375,56.5
1727627246,473.65234375,49.6
1727627246,473.65234375,40.0
1727627292,478.390625,49.8
1727627292,478.390625,55.6
1727627292,478.390625,49.6
1727627292,478.390625,57.8
1727627293,478.390625,49.7
1727627293,478.390625,36.4
1727627293,478.390625,53.4
1727627293,478.390625,41.2
1727627295,478.390625,49.8
1727627295,478.390625,37.5
1727627295,478.390625,55.5
1727627295,478.390625,39.4
1727627296,478.390625,49.7
1727627296,478.390625,56.2
1727627296,478.390625,50.9
1727627296,478.390625,47.2
1727627297,478.390625,49.7
1727627297,478.390625,54.3
1727627297,478.390625,49.5
1727627297,478.390625,40.6
1727627299,478.390625,49.8
1727627299,478.390625,39.4
1727627299,478.390625,48.6
1727627299,478.390625,57.8
1727627300,478.390625,49.7
1727627300,478.390625,56.5
1727627300,478.390625,46.8
1727627300,478.390625,56.5
1727627302,478.390625,49.7
1727627302,478.390625,56.5
1727627302,478.390625,49.5
1727627302,478.390625,53.2
1727627303,478.390625,49.7
1727627303,478.390625,47.7
1727627303,478.390625,50.5
1727627303,478.390625,41.9
1727627304,478.390625,49.8
1727627304,478.390625,41.7
1727627304,478.390625,52.9
1727627304,478.390625,39.4
1727627306,478.390625,49.7
1727627306,478.390625,38.2
1727627306,478.390625,54.2
1727627306,478.390625,39.4
1727627307,478.390625,49.8
1727627307,478.390625,36.4
1727627307,478.390625,54.2
1727627307,478.390625,40.6
1727627308,478.390625,49.7
1727627308,478.390625,35.3
1727627308,478.390625,54.2
1727627308,478.390625,38.7
1727627310,478.390625,49.8
1727627310,478.390625,52.3
1727627310,478.390625,49.6
1727627310,478.390625,54.5
1727627311,478.390625,49.8
1727627311,478.390625,52.3
1727627311,478.390625,49.5
1727627311,478.390625,40.0
1727627313,478.40625,49.7
1727627313,478.40625,54.3
1727627313,478.40625,48.1
1727627313,478.40625,57.8
1727627314,478.40625,49.7
1727627314,478.40625,56.5
1727627314,478.40625,49.0
1727627314,478.40625,57.8
1727627315,478.40625,49.7
1727627315,478.40625,55.3
1727627315,478.40625,48.1
1727627315,478.40625,54.3
1727627317,478.40625,49.7
1727627317,478.40625,37.5
1727627317,478.40625,53.4
1727627317,478.40625,41.2
1727627318,478.40625,49.7
1727627318,478.40625,46.3
1727627318,478.40625,50.4
1727627318,478.40625,50.0
1727627320,478.41796875,49.8
1727627320,478.41796875,55.6
1727627320,478.41796875,50.3
1727627320,478.41796875,39.4
1727627321,478.41796875,49.8
1727627321,478.41796875,38.2
1727627321,478.41796875,53.7
1727627321,478.41796875,38.7
1727627322,478.41796875,49.7
1727627322,478.41796875,54.2
1727627322,478.41796875,47.7
1727627322,478.41796875,44.1
1727627324,478.41796875,49.8
1727627324,478.41796875,48.9
1727627324,478.41796875,48.6
1727627324,478.41796875,48.6
1727627325,478.41796875,49.7
1727627325,478.41796875,38.2
1727627325,478.41796875,54.1
1727627325,478.41796875,37.5
1727627327,478.41796875,49.7
1727627327,478.41796875,53.2
1727627327,478.41796875,48.6
1727627327,478.41796875,55.6
1727627328,478.41796875,49.7
1727627328,478.41796875,38.2
1727627328,478.41796875,53.3
1727627328,478.41796875,39.4
1727627329,478.41796875,49.7
1727627329,478.41796875,51.1
1727627329,478.41796875,49.5
1727627329,478.41796875,39.4
1727627331,478.41796875,49.7
1727627331,478.41796875,53.2
1727627331,478.41796875,50.4
1727627331,478.41796875,44.1
1727627332,478.41796875,49.8
1727627332,478.41796875,41.2
1727627332,478.41796875,50.9
1727627332,478.41796875,50.0
1727627334,478.41796875,49.7
1727627334,478.41796875,54.3
1727627334,478.41796875,49.5
1727627334,478.41796875,52.4
1727627335,478.41796875,49.7
1727627335,478.41796875,53.1
1727627335,478.41796875,49.0
1727627335,478.41796875,51.3
1727627336,478.41796875,49.7
1727627336,478.41796875,55.1
1727627336,478.41796875,48.5
1727627336,478.41796875,48.7
1727627338,478.41796875,49.7
1727627338,478.41796875,36.4
1727627338,478.41796875,53.7
1727627338,478.41796875,39.4
1727627339,478.41796875,49.8
1727627339,478.41796875,54.3
1727627339,478.41796875,48.1
1727627339,478.41796875,44.1
1727627341,478.41796875,49.8
1727627341,478.41796875,56.2
1727627341,478.41796875,46.7
1727627341,478.41796875,50.0
1727627342,478.41796875,49.8
1727627342,478.41796875,53.2
1727627342,478.41796875,48.1
1727627342,478.41796875,53.5
1727627343,478.41796875,49.7
1727627343,478.41796875,56.5
1727627343,478.41796875,48.6
1727627343,478.41796875,40.6
1727627345,478.41796875,49.8
1727627345,478.41796875,38.2
1727627345,478.41796875,52.9
1727627345,478.41796875,40.6
1727627346,478.41796875,49.7
1727627346,478.41796875,55.3
1727627346,478.41796875,48.6
1727627346,478.41796875,55.6
1727627347,478.41796875,49.7
1727627347,478.41796875,54.2
1727627347,478.41796875,49.1
1727627347,478.41796875,55.3
1727627349,478.41796875,49.7
1727627349,478.41796875,51.0
1727627349,478.41796875,49.5
1727627349,478.41796875,40.6
1727627350,478.41796875,49.7
1727627350,478.41796875,54.3
1727627350,478.41796875,49.5
1727627350,478.41796875,40.6
1727627352,478.421875,49.8
1727627352,478.421875,38.2
1727627352,478.421875,52.9
1727627352,478.421875,38.7
1727627353,478.421875,49.7
1727627353,478.421875,55.3
1727627353,478.421875,48.1
1727627353,478.421875,56.5
1727627354,478.421875,49.8
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1727627417,478.44140625,52.1
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1727627493,478.4453125,51.4
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1727627499,478.4453125,56.5
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1727627506,478.4453125,49.7
1727627506,478.4453125,36.4
1727627506,478.4453125,51.4
1727627506,478.4453125,51.2
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1727627507,478.4453125,54.3
1727627507,478.4453125,48.1
1727627507,478.4453125,56.8
1727627509,478.4453125,49.7
1727627509,478.4453125,55.3
1727627509,478.4453125,48.5
1727627509,478.4453125,40.6
1727627510,478.4453125,49.8
1727627510,478.4453125,37.5
1727627510,478.4453125,50.0
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1727627511,478.4453125,49.7
1727627511,478.4453125,36.4
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1727627513,478.4453125,49.7
1727627513,478.4453125,54.3
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1727627513,478.4453125,47.4
1727627514,478.4453125,49.7
1727627514,478.4453125,56.5
1727627514,478.4453125,48.1
1727627514,478.4453125,57.8
1727627516,478.4453125,49.7
1727627516,478.4453125,37.5
1727627516,478.4453125,49.1
1727627516,478.4453125,56.8
1727627517,478.4453125,49.7
1727627517,478.4453125,55.6
1727627517,478.4453125,50.0
1727627517,478.4453125,38.2
1727627518,478.4453125,49.7
1727627518,478.4453125,54.3
1727627518,478.4453125,48.6
1727627518,478.4453125,56.8
1727627520,478.4453125,49.8
1727627520,478.4453125,38.2
1727627520,478.4453125,48.6
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1727627521,478.4453125,49.7
1727627521,478.4453125,55.3
1727627521,478.4453125,47.7
1727627521,478.4453125,56.8
1727627522,478.4453125,49.8
1727627522,478.4453125,38.2
1727627522,478.4453125,53.4
1727627522,478.4453125,40.6
1727627524,478.4453125,49.8
1727627524,478.4453125,36.4
1727627524,478.4453125,54.2
1727627524,478.4453125,39.4
1727627525,478.4453125,49.7
1727627525,478.4453125,56.5
1727627525,478.4453125,49.0
1727627525,478.4453125,40.6
1727627527,478.4453125,49.8
1727627527,478.4453125,37.1
1727627527,478.4453125,54.6
1727627527,478.4453125,38.7
1727627528,478.4453125,49.7
1727627528,478.4453125,38.2
1727627528,478.4453125,53.8
1727627528,478.4453125,37.5
1727627529,478.4453125,49.7
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1727628102,520.140625,37.5
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1727628103,520.140625,40.0
1727628103,520.140625,53.1
1727628103,520.140625,42.5
1727628105,520.140625,49.8
1727628105,520.140625,56.2
1727628105,520.140625,47.1
1727628105,520.140625,55.6
1727628107,520.140625,49.8
1727628107,520.140625,54.3
1727628107,520.140625,48.5
1727628107,520.140625,55.6
1727628108,520.140625,49.8
1727628108,520.140625,55.3
1727628108,520.140625,47.7
1727628108,520.140625,56.5
1727628110,520.140625,49.8
1727628110,520.140625,52.0
1727628110,520.140625,49.6
1727628110,520.140625,41.2
1727628112,520.140625,49.8
1727628112,520.140625,53.2
1727628112,520.140625,47.4
1727628112,520.140625,55.6
1727628113,520.140625,49.8
1727628113,520.140625,54.3
1727628113,520.140625,49.2
1727628113,520.140625,40.6
1727628115,520.140625,49.8
1727628115,520.140625,56.0
1727628115,520.140625,48.5
1727628115,520.140625,45.9
1727628117,520.140625,49.8
1727628117,520.140625,40.0
1727628117,520.140625,51.7
1727628117,520.140625,56.5
1727628118,520.140625,49.8
1727628118,520.140625,54.3
1727628118,520.140625,47.0
1727628118,520.140625,58.7
1727628120,520.140625,49.8
1727628120,520.140625,54.2
1727628120,520.140625,51.4
1727628120,520.140625,40.6
1727628122,520.140625,49.8
1727628122,520.140625,37.5
1727628122,520.140625,52.8
1727628122,520.140625,40.6
1727628123,520.140625,49.8
1727628123,520.140625,56.5
1727628123,520.140625,49.6
1727628123,520.140625,42.9
1727628125,520.140625,49.8
1727628125,520.140625,42.9
1727628125,520.140625,52.9
1727628125,520.140625,40.6
1727628127,520.140625,49.8
1727628127,520.140625,55.6
1727628127,520.140625,48.1
1727628127,520.140625,59.6
1727628128,520.140625,49.8
1727628128,520.140625,54.3
1727628128,520.140625,48.9
1727628128,520.140625,44.1
1727628130,520.140625,49.8
1727628130,520.140625,56.5
1727628130,520.140625,49.2
1727628130,520.140625,40.6
1727628132,520.140625,49.8
1727628132,520.140625,54.3
1727628132,520.140625,49.6
1727628132,520.140625,56.8
1727628133,520.140625,49.8
1727628133,520.140625,38.2
1727628133,520.140625,53.5
1727628133,520.140625,41.2
1727628135,520.140625,49.8
1727628135,520.140625,54.3
1727628135,520.140625,48.5
1727628135,520.140625,56.8
1727628137,520.140625,49.8
1727628137,520.140625,56.5
1727628137,520.140625,49.6
1727628137,520.140625,40.6
1727628138,520.140625,49.8
1727628138,520.140625,55.3
1727628138,520.140625,47.7
1727628138,520.140625,58.7
1727628140,520.140625,49.8
1727628140,520.140625,55.3
1727628140,520.140625,48.9
1727628140,520.140625,40.6
1727628142,520.140625,49.8
1727628142,520.140625,55.3
1727628142,520.140625,48.5
1727628142,520.140625,48.6
1727628143,520.140625,49.8
1727628143,520.140625,39.4
1727628143,520.140625,52.4
1727628143,520.140625,40.6
1727628145,520.140625,49.8
1727628145,520.140625,55.3
1727628145,520.140625,48.1
1727628145,520.140625,56.8
1727628147,520.140625,49.8
1727628147,520.140625,38.2
1727628147,520.140625,53.5
1727628147,520.140625,42.4
1727628148,520.140625,49.8
1727628148,520.140625,56.5
1727628148,520.140625,48.1
1727628148,520.140625,57.8
1727628150,520.140625,49.8
1727628150,520.140625,54.3
1727628150,520.140625,48.9
1727628150,520.140625,56.8
1727628152,520.140625,49.8
1727628152,520.140625,42.5
1727628152,520.140625,51.8
1727628152,520.140625,50.0
1727628153,520.140625,49.8
1727628153,520.140625,53.2
1727628153,520.140625,50.7
1727628153,520.140625,50.0
1727628155,520.140625,49.8
1727628155,520.140625,54.2
1727628155,520.140625,48.5
1727628155,520.140625,56.5
1727628157,520.140625,49.8
1727628157,520.140625,53.2
1727628157,520.140625,48.9
1727628157,520.140625,51.2
1727628159,520.140625,49.8
1727628159,520.140625,37.5
1727628159,520.140625,52.8
1727628159,520.140625,41.2
1727628160,520.140625,49.8
1727628160,520.140625,39.4
1727628160,520.140625,53.1
1727628160,520.140625,40.6
1727628162,520.140625,49.8
1727628162,520.140625,36.4
1727628162,520.140625,50.0
1727628162,520.140625,55.6
1727628164,520.140625,49.8
1727628164,520.140625,54.2
1727628164,520.140625,51.4
1727628164,520.140625,41.2
1727628165,520.140625,49.8
1727628165,520.140625,53.2
1727628165,520.140625,51.3
1727628165,520.140625,41.2
1727628167,520.140625,49.8
1727628167,520.140625,54.2
1727628167,520.140625,46.7
1727628167,520.140625,56.5
1727628169,520.140625,49.8
1727628169,520.140625,54.3
1727628169,520.140625,47.0
1727628169,520.140625,58.7
1727628170,520.140625,49.8
1727628170,520.140625,53.1
1727628170,520.140625,48.1
1727628170,520.140625,47.2
1727628172,520.140625,49.8
1727628172,520.140625,55.1
1727628172,520.140625,50.0
1727628172,520.140625,39.4
1727628174,520.140625,49.8
1727628174,520.140625,36.4
1727628174,520.140625,52.0
1727628174,520.140625,53.5
1727628175,520.140625,49.8
1727628175,520.140625,55.6
1727628175,520.140625,48.5
1727628175,520.140625,57.8
1727628177,520.140625,49.8
1727628177,520.140625,54.3
1727628177,520.140625,47.8
1727628177,520.140625,55.6
1727628179,520.140625,49.8
1727628179,520.140625,53.1
1727628179,520.140625,47.8
1727628179,520.140625,55.6
1727628180,520.140625,49.8
1727628180,520.140625,55.3
1727628180,520.140625,47.7
1727628180,520.140625,55.6
1727628182,520.140625,49.8
1727628182,520.140625,54.3
1727628182,520.140625,50.0
1727628182,520.140625,37.1
1727628184,520.140625,49.8
1727628184,520.140625,54.3
1727628184,520.140625,48.1
1727628184,520.140625,55.6
1727628185,520.140625,49.8
1727628185,520.140625,51.0
1727628185,520.140625,52.7
1727628185,520.140625,38.7
1727628187,520.140625,49.8
1727628187,520.140625,38.2
1727628187,520.140625,49.2
1727628187,520.140625,57.8
1727628189,520.140625,49.8
1727628189,520.140625,38.2
1727628189,520.140625,53.1
1727628189,520.140625,40.6
1727628190,520.140625,49.8
1727628190,520.140625,54.3
1727628190,520.140625,48.5
1727628190,520.140625,56.5
1727628194,520.140625,49.7
1727628194,520.140625,54.5
1727628194,520.140625,48.9
1727628194,520.140625,55.6
1727628195,520.140625,49.8
1727628195,520.140625,56.5
1727628195,520.140625,48.9
1727628195,520.140625,56.8
1727628197,520.140625,49.8
1727628197,520.140625,53.5
1727628197,520.140625,48.5
1727628197,520.140625,56.8
1727628198,520.140625,49.8
1727628198,520.140625,37.5
1727628198,520.140625,51.8
1727628198,520.140625,48.7
1727628200,520.140625,49.8
1727628200,520.140625,55.3
1727628200,520.140625,48.5
1727628200,520.140625,48.6
1727628202,520.140625,49.8
1727628202,520.140625,56.3
1727628202,520.140625,48.8
1727628202,520.140625,56.8
1727628204,520.140625,49.8
1727628204,520.140625,55.3
1727628204,520.140625,48.1
1727628204,520.140625,56.8
1727628205,520.140625,49.8
1727628205,520.140625,55.6
1727628205,520.140625,48.9
1727628205,520.140625,55.6
1727628207,520.140625,49.8
1727628207,520.140625,54.2
1727628207,520.140625,48.1
1727628207,520.140625,56.8
1727628209,520.140625,49.8
1727628209,520.140625,47.4
1727628209,520.140625,50.7
1727628209,520.140625,44.4
1727628210,520.140625,49.8
1727628210,520.140625,56.8
1727628210,520.140625,49.2
1727628210,520.140625,57.8
1727628212,520.140625,49.8
1727628212,520.140625,55.3
1727628212,520.140625,48.5
1727628212,520.140625,56.5
1727628213,520.140625,49.8
1727628213,520.140625,55.3
1727628214,520.140625,46.7
1727628214,520.140625,56.5
1727628215,520.140625,49.8
1727628215,520.140625,43.2
1727628215,520.140625,52.4
1727628215,520.140625,40.6
1727628217,520.140625,49.8
1727628217,520.140625,42.4
1727628217,520.140625,52.4
1727628217,520.140625,42.4
1727628219,520.140625,49.8
1727628219,520.140625,56.5
1727628219,520.140625,48.5
1727628219,520.140625,56.8
1727628220,520.140625,49.8
1727628220,520.140625,54.3
1727628220,520.140625,48.9
1727628220,520.140625,57.8
1727628222,520.140625,49.8
1727628222,520.140625,38.2
1727628222,520.140625,53.1
1727628222,520.140625,40.6
1727628224,520.140625,49.8
1727628224,520.140625,40.0
1727628224,520.140625,50.0
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1727628225,520.140625,41.2
1727628225,520.140625,48.9
1727628225,520.140625,57.8
1727628227,520.140625,49.8
1727628227,520.140625,38.2
1727628227,520.140625,52.1
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1727628229,520.140625,49.8
1727628229,520.140625,51.0
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1727628232,520.140625,49.8
1727628232,520.140625,53.2
1727628232,520.140625,48.9
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1727628241,520.140625,36.4
1727628242,520.140625,49.8
1727628242,520.140625,36.1
1727628242,520.140625,52.9
1727628242,520.140625,45.9
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1727628246,520.140625,50.4
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1727628249,520.140625,43.2
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1727628271,520.140625,39.4
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1727628274,520.140625,52.7
1727628274,520.140625,38.2
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1727628279,520.140625,49.8
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1727628286,520.140625,54.5
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1727628288,520.140625,45.9
1727628288,520.140625,51.0
1727628288,520.140625,40.6
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1727628291,520.140625,57.4
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1727628293,520.140625,55.3
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1727628293,520.140625,56.8
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1727628295,520.140625,48.9
1727628295,520.140625,48.7
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1727628296,520.140625,56.5
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1727628296,520.140625,56.8
1727628298,520.140625,49.8
1727628298,520.140625,56.5
1727628298,520.140625,48.5
1727628298,520.140625,55.1
1727628300,520.140625,49.8
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1727628301,520.140625,55.6
1727628301,520.140625,48.9
1727628301,520.140625,56.8
1727628303,520.140625,49.8
1727628303,520.140625,37.5
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1727628305,520.140625,56.5
1727628305,520.140625,48.5
1727628305,520.140625,50.0
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1727628306,520.140625,50.7
1727628306,520.140625,44.1
1727628308,520.140625,49.8
1727628308,520.140625,56.3
1727628308,520.140625,47.0
1727628308,520.140625,56.8
1727628310,520.140625,49.8
1727628310,520.140625,54.3
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1727628310,520.140625,55.6
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1727628312,520.140625,56.5
1727628312,520.140625,48.5
1727628312,520.140625,56.8
1727628313,520.140625,49.8
1727628313,520.140625,54.2
1727628313,520.140625,47.8
1727628313,520.140625,56.8
1727628315,520.140625,49.8
1727628315,520.140625,47.6
1727628315,520.140625,51.0
1727628315,520.140625,53.2
1727628317,520.140625,49.8
1727628317,520.140625,55.6
1727628317,520.140625,49.6
1727628317,520.140625,51.3
1727628318,520.140625,49.8
1727628318,520.140625,54.3
1727628318,520.140625,47.8
1727628318,520.140625,58.7
1727628320,520.140625,49.8
1727628320,520.140625,55.3
1727628320,520.140625,48.5
1727628320,520.140625,45.9
1727628322,520.140625,49.8
1727628322,520.140625,54.2
1727628322,520.140625,49.2
1727628322,520.140625,56.8
1727628323,520.140625,49.8
1727628323,520.140625,39.4
1727628323,520.140625,48.9
1727628323,520.140625,59.1
1727628325,520.140625,49.8
1727628325,520.140625,53.2
1727628325,520.140625,48.9
1727628325,520.140625,56.5
1727628327,520.140625,49.8
1727628327,520.140625,47.5
1727628327,520.140625,50.0
1727628327,520.140625,57.8
1727628328,520.140625,49.8
1727628328,520.140625,55.3
1727628328,520.140625,50.0
1727628328,520.140625,40.6
1727628330,520.140625,49.8
1727628330,520.140625,55.6
1727628330,520.140625,48.5
1727628330,520.140625,57.8
1727628332,520.140625,49.8
1727628332,520.140625,54.3
1727628332,520.140625,48.9
1727628332,520.140625,54.2
1727628333,520.140625,49.8
1727628333,520.140625,55.3
1727628333,520.140625,48.5
1727628333,520.140625,54.2
1727628335,520.140625,49.8
1727628335,520.140625,38.2
1727628335,520.140625,52.0
1727628335,520.140625,38.7
1727628337,520.140625,49.8
1727628337,520.140625,48.8
1727628337,520.140625,50.7
1727628337,520.140625,42.4
1727628339,520.140625,49.8
1727628339,520.140625,55.6
1727628339,520.140625,48.9
1727628339,520.140625,56.8
1727628340,520.140625,49.8
1727628340,520.140625,36.4
1727628340,520.140625,49.6
1727628340,520.140625,54.8
1727628342,520.140625,49.8
1727628342,520.140625,42.1
1727628342,520.140625,52.3
1727628342,520.140625,38.7
1727628344,520.140625,49.8
1727628344,520.140625,38.2
1727628344,520.140625,52.4
1727628344,520.140625,53.3
1727628345,520.140625,49.8
1727628345,520.140625,48.8
1727628345,520.140625,49.3
1727628345,520.140625,55.8
1727628347,520.140625,49.8
1727628347,520.140625,51.0
1727628347,520.140625,48.9
1727628347,520.140625,55.6
1727628349,520.140625,49.8
1727628349,520.140625,54.3
1727628349,520.140625,47.5
1727628349,520.140625,55.6
1727628350,520.140625,49.8
1727628350,520.140625,56.5
1727628350,520.140625,47.8
1727628350,520.140625,56.8
1727628352,520.140625,49.8
1727628352,520.140625,54.3
1727628352,520.140625,47.0
1727628352,520.140625,57.8
1727628354,520.140625,49.8
1727628354,520.140625,38.2
1727628354,520.140625,53.1
1727628354,520.140625,40.6
1727628355,520.140625,49.8
1727628355,520.140625,51.1
1727628355,520.140625,50.8
1727628355,520.140625,40.6
1727628357,520.140625,49.8
1727628357,520.140625,52.9
1727628357,520.140625,49.6
1727628357,520.140625,56.8
1727628359,520.140625,49.8
1727628359,520.140625,54.3
1727628359,520.140625,48.1
1727628359,520.140625,57.8
1727628360,520.140625,49.8
1727628360,520.140625,56.5
1727628360,520.140625,48.5
1727628360,520.140625,55.6
1727628362,520.140625,49.8
1727628362,520.140625,56.3
1727628362,520.140625,47.4
1727628362,520.140625,56.8
1727628364,520.140625,49.8
1727628364,520.140625,54.3
1727628364,520.140625,49.6
1727628364,520.140625,58.7
1727628365,520.140625,49.8
1727628365,520.140625,55.3
1727628365,520.140625,48.5
1727628365,520.140625,55.6
1727628367,520.140625,49.8
1727628367,520.140625,56.5
1727628367,520.140625,48.8
1727628367,520.140625,55.6
1727628369,520.140625,49.8
1727628369,520.140625,38.2
1727628369,520.140625,52.4
1727628369,520.140625,38.7
1727628370,520.140625,49.8
1727628370,520.140625,55.6
1727628370,520.140625,49.2
1727628370,520.140625,56.8
1727628372,520.140625,49.8
1727628372,520.140625,50.9
1727628372,520.140625,51.0
1727628372,520.140625,52.4
1727628374,520.140625,49.8
1727628374,520.140625,54.3
1727628374,520.140625,48.9
1727628374,520.140625,58.1
1727628375,520.140625,49.8
1727628375,520.140625,55.3
1727628375,520.140625,49.2
1727628375,520.140625,57.4
1727628377,520.140625,49.8
1727628377,520.140625,48.8
1727628377,520.140625,50.4
1727628377,520.140625,38.7
1727628379,520.140625,49.8
1727628379,520.140625,38.2
1727628379,520.140625,53.8
1727628379,520.140625,38.2
1727628380,520.140625,49.8
1727628380,520.140625,38.2
1727628380,520.140625,51.4
1727628380,520.140625,37.5
1727628382,520.140625,49.8
1727628382,520.140625,52.3
1727628382,520.140625,47.4
1727628382,520.140625,57.4
1727628384,520.140625,49.8
1727628384,520.140625,55.3
1727628384,520.140625,47.0
1727628384,520.140625,55.3
1727628385,520.140625,49.8
1727628385,520.140625,39.4
1727628385,520.140625,49.2
1727628385,520.140625,58.7
1727628387,520.140625,49.8
1727628387,520.140625,55.3
1727628387,520.140625,48.8
1727628387,520.140625,57.8
1727628389,520.140625,49.8
1727628389,520.140625,58.7
1727628389,520.140625,48.5
1727628389,520.140625,56.5
1727628390,520.140625,49.8
1727628390,520.140625,54.3
1727628390,520.140625,50.0
1727628390,520.140625,54.3
1727628392,520.140625,49.8
1727628392,520.140625,41.2
1727628392,520.140625,52.4
1727628392,520.140625,39.4
1727628394,520.140625,49.8
1727628394,520.140625,46.7
1727628394,520.140625,50.7
1727628394,520.140625,41.9
1727628395,520.140625,49.8
1727628395,520.140625,39.4
1727628395,520.140625,51.3
1727628395,520.140625,40.6
1727628397,520.140625,49.8
1727628397,520.140625,38.2
1727628397,520.140625,52.4
1727628397,520.140625,40.6
1727628399,520.140625,49.8
1727628399,520.140625,54.3
1727628399,520.140625,48.5
1727628399,520.140625,56.5
1727628400,520.140625,49.8
1727628400,520.140625,54.3
1727628400,520.140625,50.3
1727628400,520.140625,48.7
1727628402,520.140625,49.8
1727628402,520.140625,54.3
1727628402,520.140625,49.6
1727628402,520.140625,40.6
1727628404,525.140625,49.8
1727628404,525.140625,51.0
1727628404,525.140625,48.9
1727628404,525.140625,55.6
1727628405,525.140625,49.8
1727628405,525.140625,55.3
1727628405,525.140625,51.7
1727628405,525.140625,39.4
1727628407,525.140625,49.8
1727628407,525.140625,39.4
1727628407,525.140625,52.0
1727628407,525.140625,40.6
1727628409,525.140625,49.8
1727628409,525.140625,39.4
1727628409,525.140625,49.6
1727628409,525.140625,53.7
1727628411,525.140625,49.8
1727628411,525.140625,55.3
1727628411,525.140625,47.0
1727628411,525.140625,56.8
1727628412,525.140625,49.8
1727628412,525.140625,54.3
1727628412,525.140625,51.4
1727628412,525.140625,39.4
1727628414,525.140625,49.8
1727628414,525.140625,49.1
1727628414,525.140625,48.7
1727628414,525.140625,55.6
1727628416,525.140625,49.8
1727628416,525.140625,55.3
1727628416,525.140625,52.1
1727628416,525.140625,41.2
1727628417,525.140625,49.8
1727628417,525.140625,37.1
1727628417,525.140625,52.3
1727628417,525.140625,38.7
1727628419,525.140625,49.8
1727628419,525.140625,56.5
1727628419,525.140625,47.8
1727628419,525.140625,56.8
1727628421,525.140625,49.8
1727628421,525.140625,54.2
1727628421,525.140625,48.9
1727628421,525.140625,55.6
1727628422,525.140625,49.8
1727628422,525.140625,54.3
1727628422,525.140625,47.8
1727628422,525.140625,54.3
1727628424,525.140625,49.8
1727628424,525.140625,54.3
1727628424,525.140625,50.7
1727628424,525.140625,40.6
1727628426,525.140625,49.8
1727628426,525.140625,51.2
1727628426,525.140625,48.2
1727628426,525.140625,54.3
1727628427,525.140625,49.8
1727628427,525.140625,53.2
1727628427,525.140625,47.8
1727628427,525.140625,55.6
1727628429,525.140625,49.8
1727628429,525.140625,54.2
1727628429,525.140625,47.4
1727628429,525.140625,55.6
1727628431,525.140625,49.8
1727628431,525.140625,54.2
1727628431,525.140625,46.3
1727628431,525.140625,57.8
1727628432,525.140625,49.8
1727628432,525.140625,55.1
1727628432,525.140625,49.4
1727628432,525.140625,53.2
1727628434,525.140625,49.8
1727628434,525.140625,53.2
1727628434,525.140625,51.7
1727628434,525.140625,40.6
1727628436,525.140625,49.8
1727628436,525.140625,53.2
1727628436,525.140625,50.7
1727628436,525.140625,39.4
1727628437,525.140625,49.8
1727628437,525.140625,54.3
1727628437,525.140625,48.1
1727628437,525.140625,56.5
1727628439,525.140625,49.8
1727628439,525.140625,37.5
1727628439,525.140625,51.3
1727628439,525.140625,52.4
1727628441,525.140625,49.8
1727628441,525.140625,51.0
1727628441,525.140625,48.9
1727628441,525.140625,55.6
1727628443,525.140625,49.8
1727628443,525.140625,54.0
1727628443,525.140625,46.3
1727628443,525.140625,55.6
1727628444,525.140625,49.8
1727628444,525.140625,54.3
1727628444,525.140625,47.7
1727628444,525.140625,57.4
1727628446,525.140625,49.8
1727628446,525.140625,54.2
1727628446,525.140625,48.5
1727628446,525.140625,54.3
1727628448,525.140625,49.8
1727628448,525.140625,51.2
1727628448,525.140625,49.3
1727628448,525.140625,42.4
1727628449,525.140625,49.8
1727628449,525.140625,50.0
1727628449,525.140625,50.4
1727628449,525.140625,39.4
1727628451,525.140625,49.8
1727628451,525.140625,37.5
1727628451,525.140625,52.0
1727628451,525.140625,39.4
1727628453,525.140625,49.8
1727628453,525.140625,50.0
1727628453,525.140625,48.9
1727628453,525.140625,50.0
1727628454,525.140625,49.8
1727628454,525.140625,54.3
1727628454,525.140625,48.5
1727628454,525.140625,55.6
1727628456,525.140625,49.8
1727628456,525.140625,54.2
1727628456,525.140625,47.7
1727628456,525.140625,58.7
1727628458,525.140625,49.8
1727628458,525.140625,53.3
1727628458,525.140625,48.1
1727628458,525.140625,57.4
1727628459,525.140625,49.8
1727628459,525.140625,52.4
1727628459,525.140625,48.2
1727628459,525.140625,54.2
1727628461,525.140625,49.8
1727628461,525.140625,39.4
1727628461,525.140625,52.8
1727628461,525.140625,40.6
1727628463,525.140625,49.8
1727628463,525.140625,36.4
1727628463,525.140625,52.3
1727628463,525.140625,40.6
1727628464,525.140625,49.8
1727628464,525.140625,55.6
1727628464,525.140625,48.5
1727628464,525.140625,56.8
1727628466,525.140625,49.8
1727628466,525.140625,55.6
1727628466,525.140625,49.4
1727628466,525.140625,56.5
1727628468,525.140625,49.8
1727628468,525.140625,46.3
1727628468,525.140625,51.7
1727628468,525.140625,39.4
1727628469,525.140625,49.8
1727628469,525.140625,53.2
1727628469,525.140625,47.7
1727628469,525.140625,57.8
1727628471,525.140625,49.8
1727628471,525.140625,52.0
1727628471,525.140625,50.0
1727628471,525.140625,37.5
1727628473,525.140625,49.8
1727628473,525.140625,54.3
1727628473,525.140625,46.7
1727628473,525.140625,57.8
1727628474,525.140625,49.8
1727628474,525.140625,47.6
1727628474,525.140625,52.1
1727628474,525.140625,38.7
1727628476,525.140625,49.8
1727628476,525.140625,37.5
1727628476,525.140625,51.7
1727628476,525.140625,45.7
1727628478,525.140625,49.8
1727628478,525.140625,37.1
1727628478,525.140625,52.7
1727628478,525.140625,44.1
1727628479,525.140625,49.8
1727628479,525.140625,38.2
1727628479,525.140625,53.1
1727628479,525.140625,38.7
1727628481,525.140625,49.9
1727628481,525.140625,40.0
1727628481,525.140625,53.1
1727628481,525.140625,38.7
1727628483,525.140625,49.8
1727628483,525.140625,55.6
1727628483,525.140625,48.1
1727628483,525.140625,57.4
1727628484,525.140625,49.8
1727628484,525.140625,38.2
1727628484,525.140625,52.7
1727628484,525.140625,38.7
1727628486,525.140625,49.8
1727628486,525.140625,51.2
1727628486,525.140625,51.0
1727628486,525.140625,40.0
1727628488,525.140625,49.8
1727628488,525.140625,54.2
1727628488,525.140625,51.3
1727628488,525.140625,41.9
1727628489,525.140625,49.8
1727628489,525.140625,54.3
1727628489,525.140625,52.3
1727628489,525.140625,40.6
1727628491,525.140625,49.8
1727628491,525.140625,36.4
1727628491,525.140625,52.2
1727628491,525.140625,50.0
1727628493,525.140625,49.8
1727628493,525.140625,37.5
1727628493,525.140625,52.4
1727628493,525.140625,42.4
1727628494,525.140625,49.8
1727628494,525.140625,56.3
1727628494,525.140625,48.5
1727628494,525.140625,40.6
1727628496,525.140625,49.8
1727628496,525.140625,39.4
1727628496,525.140625,51.7
1727628496,525.140625,37.5
1727628498,525.140625,49.8
1727628498,525.140625,54.3
1727628498,525.140625,48.1
1727628498,525.140625,59.6
1727628499,525.140625,49.8
1727628499,525.140625,39.4
1727628499,525.140625,52.7
1727628499,525.140625,37.5
1727628501,525.140625,49.8
1727628501,525.140625,55.3
1727628501,525.140625,48.5
1727628501,525.140625,55.3
1727628503,525.140625,49.8
1727628503,525.140625,53.2
1727628503,525.140625,48.5
1727628503,525.140625,57.4
1727628504,525.140625,49.8
1727628504,525.140625,52.4
1727628504,525.140625,50.7
1727628504,525.140625,48.6
1727628506,525.140625,49.8
1727628506,525.140625,58.3
1727628506,525.140625,48.8
1727628506,525.140625,38.7
1727628508,525.140625,49.8
1727628508,525.140625,39.4
1727628508,525.140625,50.0
1727628508,525.140625,56.8
1727628509,525.140625,49.8
1727628509,525.140625,37.5
1727628509,525.140625,52.9
1727628509,525.140625,47.2
1727628511,525.140625,49.8
1727628511,525.140625,51.0
1727628511,525.140625,48.1
1727628511,525.140625,55.6
1727628513,525.140625,49.8
1727628513,525.140625,38.2
1727628513,525.140625,52.1
1727628513,525.140625,41.2
1727628515,525.140625,49.8
1727628515,525.140625,54.3
1727628515,525.140625,48.5
1727628515,525.140625,57.4
1727628516,525.140625,49.8
1727628516,525.140625,41.7
1727628516,525.140625,52.0
1727628516,525.140625,38.7
1727628518,525.140625,49.8
1727628518,525.140625,54.3
1727628518,525.140625,48.1
1727628518,525.140625,55.3
1727628520,525.140625,49.8
1727628520,525.140625,55.3
1727628520,525.140625,48.5
1727628520,525.140625,58.7
1727628521,525.140625,49.8
1727628521,525.140625,54.2
1727628521,525.140625,51.7
1727628521,525.140625,40.6
1727628523,525.140625,49.8
1727628523,525.140625,54.3
1727628523,525.140625,50.7
1727628523,525.140625,40.6
1727628524,525.140625,49.8
1727628524,525.140625,56.5
1727628524,525.140625,48.1
1727628524,525.140625,41.2
1727628526,525.140625,49.8
1727628526,525.140625,37.5
1727628526,525.140625,51.4
1727628526,525.140625,45.9
1727628528,525.140625,49.8
1727628528,525.140625,54.3
1727628528,525.140625,47.8
1727628528,525.140625,58.7
1727628530,525.140625,49.8
1727628530,525.140625,47.5
1727628530,525.140625,50.0
1727628530,525.140625,50.0
1727628531,525.140625,49.8
1727628531,525.140625,57.4
1727628531,525.140625,48.8
1727628531,525.140625,40.0
1727628533,525.140625,49.8
1727628533,525.140625,37.5
1727628533,525.140625,52.8
1727628533,525.140625,40.6
1727628535,525.140625,49.8
1727628535,525.140625,38.2
1727628535,525.140625,52.4
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1727628989,535.609375,41.7
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1727629006,535.609375,42.4
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1727629014,535.609375,49.9
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1727629014,535.609375,50.6
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1727629016,535.609375,46.8
1727629016,535.609375,57.4
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1727630533,541.27734375,49.9
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1727630824,542.48828125,42.9
1727630827,542.47265625,49.9
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1727630829,543.23046875,50.6
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1727630837,542.484375,49.9
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1727630837,542.484375,59.6
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1727630842,542.48046875,39.4
1727630842,542.48046875,50.8
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1727631486,453.56640625,56.5
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1727631494,453.56640625,49.9
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1727631505,453.56640625,51.5
1727631505,453.56640625,39.4
1727631507,453.55859375,49.9
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1727631516,453.5625,49.9
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1727631524,453.5703125,49.9
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1727639929,468.6953125,51.3
1727639929,468.6953125,42.4
1727639942,468.62109375,50.0
1727639942,468.62109375,54.3
1727639942,468.62109375,49.0
1727639942,468.62109375,57.8
1727639955,468.69921875,50.0
1727639955,468.69921875,53.2
1727639955,468.69921875,50.3
1727639955,468.69921875,37.5
1727639969,468.63671875,50.0
1727639969,468.63671875,45.2
1727639969,468.63671875,50.6
1727639969,468.63671875,55.6
1727639982,468.70703125,50.0
1727639982,468.70703125,40.6
1727639982,468.70703125,50.7
1727639982,468.70703125,40.6
1727639995,468.6328125,50.0
1727639995,468.6328125,52.0
1727639995,468.6328125,50.4
1727639995,468.6328125,38.2
1727640008,468.70703125,50.0
1727640008,468.70703125,55.6
1727640008,468.70703125,49.4
1727640008,468.70703125,50.0
1727640021,468.640625,50.0
1727640021,468.640625,52.1
1727640022,468.640625,48.8
1727640022,468.640625,55.6
1727640038,468.7265625,50.0
1727640038,468.7265625,54.3
1727640039,468.7265625,49.6
1727640039,468.7265625,54.3
1727640129,458.05859375,50.1
1727640129,458.05859375,55.3
1727640129,458.05859375,50.2
1727640129,458.05859375,37.5
1727640313,453.7109375,50.2
1727640313,453.7109375,40.0
1727640313,453.7109375,51.2
1727640313,453.7109375,39.4
1727640563,458.0390625,50.2
1727640563,458.0390625,38.2
1727640563,458.0390625,50.7
1727640563,458.0390625,40.6
1727640646,465.57421875,50.2
1727640646,465.57421875,55.6
1727640646,465.57421875,50.6
1727640646,465.57421875,38.7
1727640692,464.1015625,50.1
1727640692,464.1015625,56.3
1727640692,464.1015625,50.0
1727640692,464.1015625,41.2
1727640707,463.203125,50.0
1727640707,463.203125,51.2
1727640707,463.203125,49.3
1727640707,463.203125,56.8
1727640721,463.2109375,50.0
1727640721,463.2109375,56.5
1727640721,463.2109375,50.3
1727640721,463.2109375,48.7
1727640735,464.10546875,50.0
1727640735,464.10546875,54.3
1727640735,464.10546875,50.0
1727640735,464.10546875,55.6
1727640749,463.21484375,50.0
1727640749,463.21484375,55.3
1727640749,463.21484375,50.4
1727640749,463.21484375,37.5
1727640762,463.21875,50.0
1727640762,463.21875,36.4
1727640762,463.21875,50.3
1727640762,463.21875,57.8
1727640776,463.21875,50.0
1727640776,463.21875,55.3
1727640776,463.21875,50.3
1727640776,463.21875,38.7
1727640790,464.10546875,50.0
1727640790,464.10546875,54.3
1727640790,464.10546875,48.7
1727640790,464.10546875,56.8
1727640842,462.4921875,50.1
1727640842,462.4921875,54.3
1727640842,462.4921875,49.3
1727640842,462.4921875,60.4
1727640888,470.45703125,50.1
1727640888,470.45703125,53.2
1727640888,470.45703125,48.8
1727640888,470.45703125,55.3
1727640902,470.46484375,50.0
1727640902,470.46484375,53.3
1727640902,470.46484375,49.0
1727640902,470.46484375,57.4
1727640916,470.4609375,50.0
1727640916,470.4609375,56.2
1727640916,470.4609375,49.9
1727640916,470.4609375,47.2
1727640930,470.46484375,50.0
1727640930,470.46484375,46.2
1727640930,470.46484375,50.1
1727640930,470.46484375,57.8
1727640944,470.484375,50.0
1727640944,470.484375,36.4
1727640944,470.484375,51.2
1727640944,470.484375,38.7
1727640957,470.48828125,50.0
1727640957,470.48828125,38.2
1727640957,470.48828125,50.1
1727640957,470.48828125,58.7
1727640971,470.48828125,50.0
1727640971,470.48828125,54.3
1727640971,470.48828125,49.9
1727640971,470.48828125,48.7
1727641031,468.52734375,50.1
1727641031,468.52734375,55.3
1727641031,468.52734375,49.0
1727641031,468.52734375,57.8
1727641188,464.91015625,50.2
1727641188,464.91015625,54.2
1727641188,464.91015625,50.1
1727641188,464.91015625,42.9
1727641390,474.421875,50.2
1727641390,474.421875,38.2
1727641390,474.421875,51.1
1727641390,474.421875,41.2
1727641593,466.80078125,50.2
1727641593,466.80078125,47.5
1727641593,466.80078125,50.9
1727641593,466.80078125,39.4
1727641673,456.14453125,50.1
1727641673,456.14453125,54.3
1727641673,456.14453125,49.6
1727641673,456.14453125,56.8
1727641689,456.15625,50.0
1727641689,456.15625,56.2
1727641689,456.15625,50.3
1727641689,456.15625,38.7
1727641704,456.171875,50.0
1727641704,456.171875,55.1
1727641704,456.171875,49.6
1727641704,456.171875,55.6
1727641718,456.1796875,50.0
1727641718,456.1796875,43.2
1727641718,456.1796875,51.3
1727641718,456.1796875,38.7
1727641737,458.19140625,50.0
1727641737,458.19140625,39.4
1727641737,458.19140625,50.1
1727641737,458.19140625,56.5
1727641788,466.64453125,50.2
1727641788,466.64453125,51.0
1727641788,466.64453125,49.6
1727641788,466.64453125,41.2
1727641803,466.65234375,50.0
1727641803,466.65234375,55.1
1727641803,466.65234375,49.4
1727641803,466.65234375,55.6
1727641817,466.65625,50.0
1727641817,466.65625,56.2
1727641817,466.65625,49.2
1727641817,466.65625,52.4
1727641836,466.65625,50.0
1727641836,466.65625,54.2
1727641836,466.65625,49.5
1727641836,466.65625,57.8
1727641898,464.63671875,50.1
1727641898,464.63671875,44.7
1727641898,464.63671875,50.3
1727641898,464.63671875,47.2
1727642031,476.91796875,50.2
1727642031,476.91796875,55.3
1727642031,476.91796875,50.1
1727642031,476.91796875,40.0
1727642205,467.1953125,50.2
1727642206,467.1953125,37.5
1727642206,467.1953125,50.3
1727642206,467.1953125,56.8
1727642415,479.34375,50.2
1727642415,479.34375,36.4
1727642415,479.34375,49.7
1727642415,479.34375,57.8
1727642579,471.1328125,50.2
1727642579,471.1328125,55.3
1727642579,471.1328125,50.3
1727642579,471.1328125,40.6
1727642699,477.421875,50.2
1727642699,477.421875,55.3
1727642699,477.421875,49.5
1727642699,477.421875,57.8
1727642826,481.421875,50.2
1727642826,481.421875,54.2
1727642826,481.421875,50.3
1727642826,481.421875,39.4
1727642886,480.35546875,50.1
1727642886,480.35546875,39.4
1727642886,480.35546875,49.9
1727642886,480.35546875,56.8
1727643029,481.80859375,50.2
1727643029,481.80859375,55.3
1727643029,481.80859375,49.7
1727643029,481.80859375,50.0
1727643188,466.54296875,50.2
1727643188,466.54296875,35.5
1727643188,466.54296875,50.6
1727643188,466.54296875,50.0
1727643383,474.04296875,50.2
1727643383,474.04296875,55.3
1727643383,474.04296875,50.4
1727643383,474.04296875,39.4
1727643532,463.59375,50.2
1727643532,463.59375,48.8
1727643532,463.59375,49.4
1727643532,463.59375,57.4
1727643638,463.85546875,50.2
1727643638,463.85546875,53.2
1727643638,463.85546875,49.6
1727643638,463.85546875,48.7
1727643697,477.78515625,50.1
1727643697,477.78515625,36.4
1727643697,477.78515625,50.5
1727643697,477.78515625,58.7
1727643762,485.05078125,50.1
1727643762,485.05078125,55.6
1727643762,485.05078125,49.2
1727643762,485.05078125,56.8
1727643911,473.3203125,50.2
1727643911,473.3203125,55.3
1727643911,473.3203125,49.6
1727643911,473.3203125,55.6
1727644014,492.51953125,50.2
1727644014,492.51953125,53.2
1727644014,492.51953125,50.1
1727644014,492.51953125,55.6
1727644125,494.359375,50.2
1727644125,494.359375,43.2
1727644125,494.359375,50.1
1727644125,494.359375,55.6
1727644238,473.4921875,50.1
1727644238,473.4921875,54.3
1727644238,473.4921875,49.6
1727644238,473.4921875,56.8
1727644445,474.8203125,50.2
1727644445,474.8203125,54.3
1727644445,474.8203125,49.9
1727644445,474.8203125,52.5
1727644621,475.421875,50.2
1727644621,475.421875,36.4
1727644621,475.421875,51.2
1727644621,475.421875,39.4
1727644881,477.796875,50.2
1727644881,477.796875,38.2
1727644881,477.796875,49.9
1727644881,477.796875,55.6
1727644993,498.171875,50.2
1727644993,498.171875,52.1
1727644993,498.171875,50.5
1727644993,498.171875,39.4
1727645106,503.30078125,50.2
1727645106,503.30078125,56.3
1727645106,503.30078125,49.9
1727645106,503.30078125,39.4
1727645177,472.24609375,50.1
1727645177,472.24609375,41.7
1727645177,472.24609375,50.9
1727645177,472.24609375,39.4
1727645332,488.02734375,50.2
1727645332,488.02734375,38.2
1727645333,488.02734375,50.9
1727645333,488.02734375,38.7
1727645478,475.46875,50.2
1727645478,475.46875,55.3
1727645478,475.46875,49.5
1727645478,475.46875,56.5
1727645556,479.1640625,50.1
1727645556,479.1640625,57.4
1727645556,479.1640625,49.4
1727645556,479.1640625,56.5
1727645757,482.95703125,50.2
1727645757,482.95703125,46.2
1727645757,482.95703125,50.7
1727645757,482.95703125,41.2
1727646076,479.6875,50.0
1727646076,479.6875,46.3
1727646076,479.6875,49.7
1727646076,479.6875,55.6
1727646411,492.45703125,50.2
1727646411,492.45703125,54.2
1727646411,492.45703125,49.4
1727646411,492.45703125,56.8
1727646642,488.1875,50.2
1727646642,488.1875,37.1
1727646642,488.1875,50.7
1727646642,488.1875,38.2
1727646769,510.515625,50.1
1727646769,510.515625,56.5
1727646769,510.515625,49.7
1727646769,510.515625,57.8
1727646994,517.63671875,50.2
1727646994,517.63671875,38.2
1727646994,517.63671875,50.5
1727646994,517.63671875,56.5
1727647183,504.90625,50.2
1727647183,504.90625,55.3
1727647183,504.90625,50.0
1727647183,504.90625,55.6
1727647370,500.24609375,50.2
1727647370,500.24609375,36.4
1727647370,500.24609375,51.0
1727647370,500.24609375,38.7
1727647659,504.1875,50.2
1727647659,504.1875,56.5
1727647659,504.1875,49.1
1727647659,504.1875,53.2
1727648042,486.5625,50.2
1727648042,486.5625,36.4
1727648042,486.5625,50.7
1727648042,486.5625,47.2
1727648337,516.95703125,50.2
1727648337,516.95703125,52.3
1727648337,516.95703125,50.1
1727648337,516.95703125,50.0
1727648595,517.453125,50.2
1727648595,517.453125,54.3
1727648595,517.453125,49.5
1727648595,517.453125,54.3
1727648688,492.91015625,50.1
1727648688,492.91015625,38.2
1727648688,492.91015625,49.9
1727648688,492.91015625,56.8
1727649025,518.2109375,50.2
1727649025,518.2109375,57.1
1727649025,518.2109375,50.0
1727649025,518.2109375,38.2
1727649460,531.84765625,50.1
1727649460,531.84765625,55.3
1727649460,531.84765625,49.1
1727649460,531.84765625,56.5
1727649787,520.16796875,50.2
1727649787,520.16796875,55.6
1727650441,521.15625,49.7
1727650441,521.15625,53.2
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<h1> Parallel Plot</h1>
<div class="invert_in_dark_mode" id="parallel-plot"></div>
<h1> Job Status Distribution</h1>
<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
<h1> Boxplots</h1>
<div class="invert_in_dark_mode" id="plotBoxplot"></div>
<h1> Violin</h1>
<div class="invert_in_dark_mode" id="plotViolin"></div>
<h1> Histogram</h1>
<div class="invert_in_dark_mode" id="plotHistogram"></div>
<h1> Heatmap</h1>
<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
<h1>Correlation Heatmap Explanation</h1>
<p>
This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
</p>
<h2>How It Works</h2>
<p>
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
</p>
<ul>
<li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
<li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
<li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
</ul>
<h2>Color Scale: Yellow to Purple (Viridis)</h2>
<p>
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
</p>
<ul>
<li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
<li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
<li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
<li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
</ul>
<h2>What the Heatmap Shows</h2>
<p>
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
</p>
</body>
</html>