| n_reference_samples | recent_samples_proportion | threshold | result |
|---|---|---|---|
| 383 | 0.87306 | 0.8 | 0.263906 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_reference_samples | range | 50 | 500 | int | |
| recent_samples_proport… | range | 0.1 | 1 | float | |
| threshold | range | 0.5 | 0.8 | float |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 0 | 499 | 7 | 506 |
trial_index,arm_name,trial_status,generation_method,result,n_reference_samples,recent_samples_proportion,threshold 0,0_0,COMPLETED,Sobol,0.289624407974446551605751665193,279,0.891164058446884177477897992503,0.516926014050841375890854578756 1,1_0,COMPLETED,Sobol,0.287256305760546282179745958274,277,0.796329740807414032666144976247,0.553851592727005459515510210622 2,2_0,COMPLETED,Sobol,0.287641810772111483629487338476,56,0.564008093904703855514526367188,0.777804055716842412948608398438 3,3_0,COMPLETED,Sobol,0.281969379887652804583808574534,176,0.199219920486211787835628683752,0.711541084758937403265122156881 4,4_0,COMPLETED,Sobol,0.288357748650732492734505285625,459,0.128826317191123973504573996252,0.683132903557270854122407399700 5,5_0,COMPLETED,Sobol,0.283841832800969307015748199774,97,0.237775728758424537145899080315,0.772070944309234619140625000000 6,6_0,COMPLETED,Sobol,0.290560634431104691799419015297,403,0.592548271361738598805857236584,0.589519310090690806802626866556 7,7_0,COMPLETED,Sobol,0.290725850864632651138208530028,451,0.159694225434213882275358287188,0.624980535637587375497048469697 8,8_0,COMPLETED,Sobol,0.283731688511950630449121035781,386,0.186210354324430227279663085938,0.606148756481707073895393023122 9,9_0,COMPLETED,Sobol,0.295021478136358594746013750409,330,0.131327967438846837655574972814,0.689751045312732458114624023438 10,10_0,COMPLETED,Sobol,0.289789624407974399922238717409,346,0.146652578376233577728271484375,0.736295207403600215911865234375 11,11_0,COMPLETED,Sobol,0.291772221610309467898503044125,237,0.779963809158653043063225140941,0.720785484369844242635849695944 12,12_0,COMPLETED,Sobol,0.286430223592906707530403309647,63,0.513835391122847817690910687816,0.657586010452359870370742100931 13,13_0,COMPLETED,Sobol,0.300088115431214941253301731194,423,0.968054596427828095706047406566,0.696664147078990936279296875000 14,14_0,COMPLETED,Sobol,0.291882365899328144465130208118,346,0.803905521146953128130974164378,0.681365316361188910754265180003 15,15_0,COMPLETED,Sobol,0.281804163454124956267321522319,113,0.762007247097790219036994585622,0.619599760323762849267836827494 16,16_0,COMPLETED,Sobol,0.286760656459962515185679876595,390,0.650765308737754843981804242503,0.509744476806372359689589757181 17,17_0,COMPLETED,Sobol,0.284172265668025114671024766722,354,0.956087445653974965509291905619,0.714265111647546357964699836884 18,18_0,COMPLETED,Sobol,0.289954840841502359261028232140,214,0.226930457167327404022216796875,0.726172034256160281451286664378 19,19_0,COMPLETED,Sobol,0.287586738627602200857324987737,50,0.372523916605860039297226649069,0.715504896827042102813720703125 20,20_0,COMPLETED,BoTorch,0.288137460072695250623553420155,50,0.801743443852676596073081327631,0.540452633372169310987942481006 21,21_0,COMPLETED,BoTorch,0.277343319748871053320726787206,97,0.100000000000000005551115123126,0.624729871840003392335916032607 22,22_0,COMPLETED,BoTorch,0.284117193523515831898862415983,56,1.000000000000000000000000000000,0.649486102295221967573013444053 23,23_0,COMPLETED,BoTorch,0.280262143407864261490658464027,141,0.470512614417974095459840100375,0.564499219695059339940712561656 24,24_0,COMPLETED,BoTorch,0.283180967066857580682892603363,50,0.100000000000000005551115123126,0.800000000000000044408920985006 25,25_0,COMPLETED,BoTorch,0.292763520211477001886635207484,349,0.127297051678502609606269402320,0.500000000000000000000000000000 26,26_0,COMPLETED,BoTorch,0.277783896904945426520328055631,140,1.000000000000000000000000000000,0.564564511779196087637444634311 27,27_0,COMPLETED,BoTorch,0.287862099350148725740439203946,50,0.899798597715675940733603965782,0.737071712894347363942415540805 28,28_0,COMPLETED,BoTorch,0.287972243639167291284763905423,50,1.000000000000000000000000000000,0.557236292659133436444562903489 29,29_0,COMPLETED,BoTorch,0.282630245621764530916664170945,50,0.342702119779559111201194809837,0.500000000000000000000000000000 30,30_0,COMPLETED,BoTorch,0.288908470095825542500733718043,500,0.100000000000000005551115123126,0.500000000000000000000000000000 31,31_0,COMPLETED,BoTorch,0.279050556228659596413876897714,105,0.100000000000000005551115123126,0.697349459957396056175582543801 32,32_0,COMPLETED,BoTorch,0.289183830818372067383847934252,393,0.100000000000000005551115123126,0.508758417289519870863045980514 33,33_0,COMPLETED,BoTorch,0.279876638396299171063219546340,149,0.100000000000000005551115123126,0.676926164921466511081860062404 34,34_0,COMPLETED,BoTorch,0.281033153430994553367838761915,147,0.352689434361582510035759696621,0.639532277146965477676587852329 35,35_0,COMPLETED,BoTorch,0.275801299702610469566366191430,153,0.660061439632941282518174830329,0.500000000000000000000000000000 36,36_0,COMPLETED,BoTorch,0.283896904945478589787910550513,50,0.752864104031171543773837129265,0.648979577944798879940435654134 37,37_0,COMPLETED,BoTorch,0.285824430003304374992012526491,102,0.830370083122697555388924683939,0.688815222221741740860068148322 38,38_0,COMPLETED,BoTorch,0.292267870910893234892569125805,500,0.517269410043618482752947329573,0.500000000000000000000000000000 39,39_0,COMPLETED,BoTorch,0.281418658442559754817580142117,50,0.100000000000000005551115123126,0.698637980430236171791591459623 40,40_0,COMPLETED,BoTorch,0.279491133384733969613478166139,157,0.919310758947713968503023806988,0.500000000000000000000000000000 41,41_0,COMPLETED,BoTorch,0.279546205529243363407942979393,107,0.100000000000000005551115123126,0.514477506174741527900096116355 42,42_0,COMPLETED,BoTorch,0.282905606344311055799778387154,141,0.824542545677123039915557001223,0.500000000000000000000000000000 43,43_0,COMPLETED,BoTorch,0.281638947020596996928532007587,169,0.485213483805112533175929456775,0.500000000000000000000000000000 44,44_0,COMPLETED,BoTorch,0.280702720563938745712562194967,162,0.857264041744994487181941167364,0.567150924444416837388871499570 45,45_0,COMPLETED,BoTorch,0.282685317766273813688826521684,174,0.865947602399560367736341959244,0.500000000000000000000000000000 46,46_0,COMPLETED,BoTorch,0.278830267650622354302925032243,163,0.100000000000000005551115123126,0.500000000000000000000000000000 47,47_0,COMPLETED,BoTorch,0.282134596321180763922598089266,88,0.100000000000000005551115123126,0.500000000000000000000000000000 48,48_0,COMPLETED,BoTorch,0.277618680471417578203841003415,159,1.000000000000000000000000000000,0.670156931037201419876225827466 49,49_0,COMPLETED,BoTorch,0.284667914968608881665090848401,131,0.964775461149469548693957676733,0.500000000000000000000000000000 50,50_0,COMPLETED,BoTorch,0.284062121379006549126700065244,166,1.000000000000000000000000000000,0.523538748169329437587293796241 51,51_0,COMPLETED,BoTorch,0.282409957043727288805712305475,150,0.895772707180448768404801285214,0.606021793766657990154556046036 52,52_0,COMPLETED,BoTorch,0.277894041193964103086955219624,115,0.100000000000000005551115123126,0.579229492328251716060094622662 53,53_0,COMPLETED,BoTorch,0.285879502147813657764174877229,155,0.778645814750002274173823479941,0.500000000000000000000000000000 54,54_0,COMPLETED,BoTorch,0.281528802731578320361904843594,104,0.344790234989131638698722781555,0.500000000000000000000000000000 55,55_0,COMPLETED,BoTorch,0.281198369864522512706628276646,169,0.729309840698723399476932627294,0.500000000000000000000000000000 56,56_0,COMPLETED,BoTorch,0.277949113338473385859117570362,137,0.100000000000000005551115123126,0.539134108423303004542503913399 57,57_0,COMPLETED,BoTorch,0.281308514153541189273255440639,137,0.332452673083033889422921447476,0.500000000000000000000000000000 58,58_0,COMPLETED,BoTorch,0.282079524176671481150435738527,125,0.100000000000000005551115123126,0.628846958941705946877220867464 59,59_0,COMPLETED,BoTorch,0.285328780702720607997946444812,153,1.000000000000000000000000000000,0.799425505444962825052357402456 60,60_0,COMPLETED,BoTorch,0.275030289679480066666883431026,153,0.598139552546557595746890001465,0.600108510738485478874792988790 61,61_0,COMPLETED,BoTorch,0.280978081286485270595676411176,138,0.100000000000000005551115123126,0.500000000000000000000000000000 62,62_0,COMPLETED,BoTorch,0.281033153430994553367838761915,133,0.100000000000000005551115123126,0.800000000000000044408920985006 63,63_0,COMPLETED,BoTorch,0.289018614384844108045058419521,145,0.446657079522024047513184541458,0.800000000000000044408920985006 64,64_0,COMPLETED,BoTorch,0.291607005176781619582015991909,156,0.683439246111532283656231356872,0.755922336173302844564148017525 65,65_0,COMPLETED,BoTorch,0.281969379887652804583808574534,147,0.100000000000000005551115123126,0.582915451697777253770027527935 66,66_0,COMPLETED,BoTorch,0.279821566251789888291057195602,124,0.283535641492037748800925101023,0.594005294001025641037472269090 67,67_0,COMPLETED,BoTorch,0.280757792708448028484724545706,120,0.197567830373741482041793915414,0.537675287353794773004267426586 68,68_0,COMPLETED,BoTorch,0.281528802731578320361904843594,141,0.416696188482252094509306061809,0.500000000000000000000000000000 69,69_0,COMPLETED,BoTorch,0.281088225575503947162303575169,129,0.100000000000000005551115123126,0.699143854074393278352772540529 70,70_0,COMPLETED,BoTorch,0.278499834783566435625346002780,125,0.100000000000000005551115123126,0.522943711585440729194829145854 71,71_0,COMPLETED,BoTorch,0.276241876858684842765967459854,126,0.100000000000000005551115123126,0.589314744833681292135452167713 72,72_0,COMPLETED,BoTorch,0.281694019165106279700694358326,73,0.100000000000000005551115123126,0.601001536135989500309051436489 73,73_0,COMPLETED,BoTorch,0.281473730587069037589742492855,168,0.395413665049338058921080119035,0.569241035680599027912762721826 74,74_0,COMPLETED,BoTorch,0.283180967066857580682892603363,140,0.645654732849099244340607128834,0.572350880373848291959859579947 75,75_0,COMPLETED,BoTorch,0.282244740610199329466922790743,159,0.646448755631037541569128279662,0.569466049017948505728270447435 76,76_0,COMPLETED,BoTorch,0.278830267650622354302925032243,176,1.000000000000000000000000000000,0.609506905769382378856846571580 77,77_0,COMPLETED,BoTorch,0.277728824760436143748165704892,136,1.000000000000000000000000000000,0.623141711529160868821008989471 78,78_0,COMPLETED,BoTorch,0.285989646436832223308499578707,196,0.100000000000000005551115123126,0.500000000000000000000000000000 79,79_0,COMPLETED,BoTorch,0.283456327789404105566006819572,50,0.100000000000000005551115123126,0.500000000000000000000000000000 80,80_0,COMPLETED,BoTorch,0.283401255644894822793844468833,50,0.100000000000000005551115123126,0.566756900244500316787821247999 81,81_0,COMPLETED,BoTorch,0.283125894922348297910730252624,167,0.258458763596267737661094088253,0.564775181152750271884599442274 82,82_0,COMPLETED,BoTorch,0.282740389910783096460988872423,194,0.286850907074828187504067500413,0.504527680255935195852146080142 83,83_0,COMPLETED,BoTorch,0.279050556228659596413876897714,187,0.100000000000000005551115123126,0.544939498567801439143920561037 84,84_0,COMPLETED,BoTorch,0.287531666483092807062860174483,218,0.100000000000000005551115123126,0.533253841634868774868039054127 85,85_0,COMPLETED,BoTorch,0.278169401916510627970069435833,175,0.822366476223294617931003358535,0.613299254536257665293419449881 86,86_0,COMPLETED,BoTorch,0.284117193523515831898862415983,86,0.100000000000000005551115123126,0.667065606852710879515200304013 87,87_0,COMPLETED,BoTorch,0.285879502147813657764174877229,175,0.566697434739261041158897569403,0.573756627997540791241704027925 88,88_0,COMPLETED,BoTorch,0.280592576274920180168237493490,155,0.507639005828601552750001246750,0.563206736610502134432465481950 89,89_0,COMPLETED,BoTorch,0.285383852847229890770108795550,153,1.000000000000000000000000000000,0.627645731124489514307640547486 90,90_0,COMPLETED,BoTorch,0.278554906928075829419810816034,111,0.100000000000000005551115123126,0.647911238618280593826170843386 91,91_0,COMPLETED,BoTorch,0.278995484084150202619412084459,169,1.000000000000000000000000000000,0.648269300139687310036151757231 92,92_0,COMPLETED,BoTorch,0.279270844806696727502526300668,106,0.204881163444568081821728355862,0.603062110959838304502511618921 93,93_0,COMPLETED,BoTorch,0.280592576274920180168237493490,156,1.000000000000000000000000000000,0.596234821278724180793062714656 94,94_0,COMPLETED,BoTorch,0.279821566251789888291057195602,102,0.100000000000000005551115123126,0.575733639758434612154758269753 95,95_0,COMPLETED,BoTorch,0.279270844806696727502526300668,140,0.100000000000000005551115123126,0.629480158745430951050536805269 96,96_0,COMPLETED,BoTorch,0.282630245621764530916664170945,146,1.000000000000000000000000000000,0.672932905304021167225414501445 97,97_0,COMPLETED,BoTorch,0.278279546205529193514394137310,103,0.100000000000000005551115123126,0.800000000000000044408920985006 98,98_0,COMPLETED,BoTorch,0.285493997136248456314433497027,126,1.000000000000000000000000000000,0.619025403251991068742654533708 99,99_0,COMPLETED,BoTorch,0.281253442009031795478790627385,112,0.350429475797578660056785793131,0.583888343469668891216883821471 100,100_0,COMPLETED,BoTorch,0.276902742592796569098823056265,147,1.000000000000000000000000000000,0.645935380995855679131523174874 101,101_0,COMPLETED,BoTorch,0.282024452032162087355970925273,179,0.755580169805030954499613926600,0.579296420340779216751059266244 102,102_0,COMPLETED,BoTorch,0.287586738627602200857324987737,183,1.000000000000000000000000000000,0.647020022197494326654521046294 103,103_0,COMPLETED,BoTorch,0.277343319748871053320726787206,137,0.272426633484075086943931864880,0.567132029990135788644067815767 104,104_0,COMPLETED,BoTorch,0.285053419980174083114832228603,204,1.000000000000000000000000000000,0.598490366596845735358556339634 105,105_0,COMPLETED,BoTorch,0.283070822777839015138567901886,152,1.000000000000000000000000000000,0.614593440376986377415846618533 106,106_0,COMPLETED,BoTorch,0.281749091309615562472856709064,117,0.559933035129034029075967282552,0.518328787245505018255187223986 107,107_0,COMPLETED,BoTorch,0.287476594338583524290697823744,218,1.000000000000000000000000000000,0.527725613458486453311024888535 108,108_0,COMPLETED,BoTorch,0.281694019165106279700694358326,129,0.182432777657681566285674534811,0.573961679878187136516487498739 109,109_0,COMPLETED,BoTorch,0.282465029188236571577874656214,132,0.214593935761159343433845947402,0.604675191583381010929088006378 110,110_0,COMPLETED,BoTorch,0.282299812754708723261387603998,140,0.215783266746549240444608130929,0.526736118009531684158730513445 111,111_0,COMPLETED,BoTorch,0.279270844806696727502526300668,84,0.100000000000000005551115123126,0.800000000000000044408920985006 112,112_0,COMPLETED,BoTorch,0.279931710540808453835381897079,121,0.461099321040784437819581853546,0.520238375675113884355482696265 113,113_0,COMPLETED,BoTorch,0.280262143407864261490658464027,152,0.242451434744108240693094558083,0.500000000000000000000000000000 114,114_0,COMPLETED,BoTorch,0.281528802731578320361904843594,133,0.100000000000000005551115123126,0.568533926371933340249142929679 115,115_0,COMPLETED,BoTorch,0.278995484084150202619412084459,116,0.100000000000000005551115123126,0.552902456056725388755523908912 116,116_0,COMPLETED,BoTorch,0.279546205529243363407942979393,114,0.100000000000000005551115123126,0.500000000000000000000000000000 117,117_0,COMPLETED,BoTorch,0.279766494107280494496592382347,155,0.100000000000000005551115123126,0.553111818141280853211583234952 118,118_0,COMPLETED,BoTorch,0.285769357858794981197547713236,111,0.173870024645234877436195120026,0.500000000000000000000000000000 119,119_0,COMPLETED,BoTorch,0.281914307743143521811646223796,158,0.334927417987588516012920081266,0.510832448987396881001643578202 120,120_0,COMPLETED,BoTorch,0.283621544222932064904796334304,118,0.268408705336400044139111287222,0.644332027836468590464846784016 121,121_0,COMPLETED,BoTorch,0.282465029188236571577874656214,122,0.708557093278831318095001279289,0.556082995274509994665379508660 122,122_0,COMPLETED,BoTorch,0.280867936997466705051351709699,106,0.100000000000000005551115123126,0.732072635940252292030550052004 123,123_0,COMPLETED,BoTorch,0.283511399933913388338169170311,121,0.566909573171175185102299565187,0.541865272891536631227893394680 124,124_0,COMPLETED,BoTorch,0.275636083269082510227576676698,149,0.754695824747960486433839832898,0.588425420491383244225858106802 125,125_0,COMPLETED,BoTorch,0.283125894922348297910730252624,129,0.589246593185611011733726627426,0.500000000000000000000000000000 126,126_0,COMPLETED,BoTorch,0.281418658442559754817580142117,110,0.100000000000000005551115123126,0.609115066559503426368848977290 127,127_0,COMPLETED,BoTorch,0.282850534199801773027616036416,203,0.531040033459246552105526006926,0.500000000000000000000000000000 128,128_0,COMPLETED,BoTorch,0.281143297720013229934465925908,165,0.100000000000000005551115123126,0.624624212429148006897605682752 129,129_0,COMPLETED,BoTorch,0.279325916951206121296991113923,121,0.100000000000000005551115123126,0.685114631577661836736581335572 130,130_0,COMPLETED,BoTorch,0.279601277673752646180105330131,87,0.100000000000000005551115123126,0.751111028744832598391667488613 131,131_0,COMPLETED,BoTorch,0.279766494107280494496592382347,142,0.614650447696278301457084580761,0.540367909531072276863028491789 132,132_0,COMPLETED,BoTorch,0.280592576274920180168237493490,146,0.549776497456099355609637768794,0.500000000000000000000000000000 133,133_0,COMPLETED,BoTorch,0.281143297720013229934465925908,127,0.100000000000000005551115123126,0.752156689269240175121922220569 134,134_0,COMPLETED,BoTorch,0.279546205529243363407942979393,111,0.100000000000000005551115123126,0.742716400990437897178253479069 135,135_0,COMPLETED,BoTorch,0.277618680471417578203841003415,158,0.100000000000000005551115123126,0.600584001707562875616019937297 136,136_0,COMPLETED,BoTorch,0.281253442009031795478790627385,142,0.826278419562594956815360092151,0.552684077132715168012566664402 137,137_0,COMPLETED,BoTorch,0.278885339795131637075087382982,105,1.000000000000000000000000000000,0.530364569594345525160861143377 138,138_0,COMPLETED,BoTorch,0.275911443991629035110690892907,117,0.100000000000000005551115123126,0.728469742401629316574940276041 139,139_0,COMPLETED,BoTorch,0.281198369864522512706628276646,136,0.611143793025807013918893062510,0.540528177830530864866886986420 140,140_0,COMPLETED,BoTorch,0.282134596321180763922598089266,201,0.608590575169650760400941180706,0.538717043023511754640253457183 141,141_0,COMPLETED,BoTorch,0.280372287696882938057285628020,130,0.100000000000000005551115123126,0.659927403001376200464278554136 142,142_0,COMPLETED,BoTorch,0.277673752615926860976003354153,135,0.100000000000000005551115123126,0.597870434712020837331181155605 143,143_0,COMPLETED,BoTorch,0.285989646436832223308499578707,126,1.000000000000000000000000000000,0.574420502534266352867575733399 144,144_0,COMPLETED,BoTorch,0.275085361823989460461348244280,157,0.100000000000000005551115123126,0.775100145791817185525474087626 145,145_0,COMPLETED,BoTorch,0.284778059257627447209415549878,150,0.401954412510997616259089681989,0.551410232145369882950092232932 146,146_0,COMPLETED,BoTorch,0.287091089327018433863258906058,147,0.241245701405028684094489221934,0.626640860581471947909903974505 147,147_0,COMPLETED,BoTorch,0.283070822777839015138567901886,147,0.100000000000000005551115123126,0.736845088012871629601363565598 148,148_0,COMPLETED,BoTorch,0.280482431985901503601610329497,102,0.100000000000000005551115123126,0.665105291605398774024138219829 149,149_0,COMPLETED,BoTorch,0.277728824760436143748165704892,95,0.100000000000000005551115123126,0.703587212337524525729293145559 150,150_0,COMPLETED,BoTorch,0.274975217534970783894721080287,144,0.798855281559089580589727574989,0.607419363917430454868906508636 151,151_0,COMPLETED,BoTorch,0.283070822777839015138567901886,149,0.100000000000000005551115123126,0.622283128859416168054963236500 152,152_0,COMPLETED,BoTorch,0.280151999118845695946333762549,142,0.100000000000000005551115123126,0.713164479485242419620760756516 153,153_0,COMPLETED,BoTorch,0.296728714616147137839163860917,500,0.100000000000000005551115123126,0.800000000000000044408920985006 154,154_0,COMPLETED,BoTorch,0.281418658442559754817580142117,152,0.100000000000000005551115123126,0.500000000000000000000000000000 155,155_0,COMPLETED,BoTorch,0.278114329772001345197907085094,125,0.100000000000000005551115123126,0.553270322635416866852153816581 156,156_0,COMPLETED,BoTorch,0.282299812754708723261387603998,88,0.280239437988785589084272942273,0.607837710627459615331247277936 157,157_0,COMPLETED,BoTorch,0.284282409957043680215349468199,140,0.440010489543598670714175113972,0.597240754300619181016429593001 158,158_0,COMPLETED,BoTorch,0.277618680471417578203841003415,147,0.100000444397151569408954685514,0.736858308486604385301177444489 159,159_0,COMPLETED,BoTorch,0.281088225575503947162303575169,151,0.100000000000000005551115123126,0.800000000000000044408920985006 160,160_0,COMPLETED,BoTorch,0.281088225575503947162303575169,119,0.100000000000000005551115123126,0.800000000000000044408920985006 161,161_0,COMPLETED,BoTorch,0.278885339795131637075087382982,156,0.759328526471418685517278390762,0.573682427118430426915551834099 162,162_0,COMPLETED,BoTorch,0.280702720563938745712562194967,158,0.811226580330461799483998674987,0.601976145592748035362262726267 163,163_0,COMPLETED,BoTorch,0.281638947020596996928532007587,103,1.000000000000000000000000000000,0.641159654533012712818162981421 164,164_0,COMPLETED,BoTorch,0.285218636413701931431319280819,157,0.715667241700024492345733051479,0.617026921353644897116907941381 165,165_0,COMPLETED,BoTorch,0.282409957043727288805712305475,138,0.882297257699207748693481789815,0.625156568365934584008414276468 166,166_0,COMPLETED,BoTorch,0.282409957043727288805712305475,117,0.855948281786603648590983084432,0.616026613298382930139496238553 167,167_0,COMPLETED,BoTorch,0.282850534199801773027616036416,140,0.768956595616834825968055611156,0.581679216058005543743547605118 168,168_0,COMPLETED,BoTorch,0.282520101332745854350037006952,101,0.753390832249697162126267357962,0.500000000000000000000000000000 169,169_0,COMPLETED,BoTorch,0.280041854829827019379706598556,158,0.573830279752985172869728103251,0.504244906419654026485943631997 170,170_0,COMPLETED,BoTorch,0.280096926974336413174171411811,143,0.738780599315331354404179364792,0.635865821269970710183372375468 171,171_0,COMPLETED,BoTorch,0.282244740610199329466922790743,149,0.850683235577019969397838394798,0.636228354121950778754523980751 172,172_0,COMPLETED,BoTorch,0.280482431985901503601610329497,292,1.000000000000000000000000000000,0.800000000000000044408920985006 173,173_0,COMPLETED,BoTorch,0.295627271725961038306706996082,252,1.000000000000000000000000000000,0.700659248987379634776573311683 174,174_0,COMPLETED,BoTorch,0.282850534199801773027616036416,150,0.791706784628439241835451412044,0.621133305534232627032054097072 175,175_0,COMPLETED,BoTorch,0.281088225575503947162303575169,147,0.847495953438784033195929623616,0.589111630732766622386975541303 176,176_0,COMPLETED,BoTorch,0.280702720563938745712562194967,164,0.739701675300268091106659085199,0.554711366237580216775882036018 177,177_0,COMPLETED,BoTorch,0.268476704482872530199699667719,301,1.000000000000000000000000000000,0.800000000000000044408920985006 178,178_0,COMPLETED,BoTorch,0.281914307743143521811646223796,90,0.294478249442521322620791579538,0.500000000000000000000000000000 179,179_0,COMPLETED,BoTorch,0.277178103315343093981937272474,75,0.395910251503120091953746850777,0.544326927050409636876793229021 180,180_0,COMPLETED,BoTorch,0.279931710540808453835381897079,174,0.100000000000000005551115123126,0.800000000000000044408920985006 181,181_0,COMPLETED,BoTorch,0.283070822777839015138567901886,92,0.360545560127547681794624168106,0.560290197268636047667200728029 182,182_0,COMPLETED,BoTorch,0.276572309725740761443546489318,324,1.000000000000000000000000000000,0.800000000000000044408920985006 183,183_0,COMPLETED,BoTorch,0.274865073245952218350396378810,335,1.000000000000000000000000000000,0.800000000000000044408920985006 184,184_0,RUNNING,BoTorch,,303,0.958694909086270308229416059476,0.800000000000000044408920985006 185,185_0,COMPLETED,BoTorch,0.280482431985901503601610329497,291,0.996548394472000276245182703860,0.798523219987048227253012555593 186,186_0,COMPLETED,BoTorch,0.281749091309615562472856709064,142,0.100000000000000005551115123126,0.558193795146972204790358773607 187,187_0,COMPLETED,BoTorch,0.281859235598634239039483873057,138,0.100000000000000005551115123126,0.744358055170847854320470560197 188,188_0,COMPLETED,BoTorch,0.264511510078202394247171014285,293,0.996391492882949703080441850034,0.796162224358458403550287130201 189,189_0,COMPLETED,BoTorch,0.274865073245952218350396378810,311,0.994690502591530245624085182499,0.800000000000000044408920985006 190,190_0,COMPLETED,BoTorch,0.271781033153430939819372724742,166,0.100000000000000005551115123126,0.800000000000000044408920985006 191,191_0,COMPLETED,BoTorch,0.276241876858684842765967459854,312,0.996242845505960095131570142257,0.799984622502764697316024467000 192,192_0,COMPLETED,BoTorch,0.284227337812534397443187117460,75,0.100000000000000005551115123126,0.713608965085757218638207177719 193,193_0,COMPLETED,BoTorch,0.267375261592686430667242802883,332,0.945649661879584435553169896593,0.800000000000000044408920985006 194,194_0,COMPLETED,BoTorch,0.280482431985901503601610329497,379,1.000000000000000000000000000000,0.745245213501755654483815760614 195,195_0,COMPLETED,BoTorch,0.285549069280757739086595847766,304,0.994362178770515914294492176850,0.785640164104857152693739408278 196,196_0,COMPLETED,BoTorch,0.279380989095715404069153464661,323,0.837817851960625636920099168492,0.800000000000000044408920985006 197,197_0,COMPLETED,BoTorch,0.283456327789404105566006819572,311,0.878770023384396470333967954502,0.800000000000000044408920985006 198,198_0,COMPLETED,BoTorch,0.276737526159268609760033541534,353,0.874053436737264433098459903704,0.800000000000000044408920985006 199,199_0,COMPLETED,BoTorch,0.278554906928075829419810816034,319,0.926535312920402587266721639025,0.800000000000000044408920985006 200,200_0,COMPLETED,BoTorch,0.277398391893380336092889137944,338,0.808711414977091092026739715948,0.800000000000000044408920985006 201,201_0,COMPLETED,BoTorch,0.278389690494547870081021301303,314,0.766525375307473999519913832046,0.800000000000000044408920985006 202,202_0,COMPLETED,BoTorch,0.273983918933803249906588916929,347,1.000000000000000000000000000000,0.800000000000000044408920985006 203,203_0,COMPLETED,BoTorch,0.291221500165216418132274611708,343,0.647847515836837861691321904800,0.800000000000000044408920985006 204,204_0,COMPLETED,BoTorch,0.280262143407864261490658464027,317,1.000000000000000000000000000000,0.800000000000000044408920985006 205,205_0,COMPLETED,BoTorch,0.276517237581231367649081676063,335,0.906182912697792275480423995759,0.800000000000000044408920985006 206,206_0,COMPLETED,BoTorch,0.273323053199691634596035783034,290,1.000000000000000000000000000000,0.797369097296063200630555911630 207,207_0,COMPLETED,BoTorch,0.279821566251789888291057195602,315,0.767212150206641907423943393951,0.800000000000000044408920985006 208,208_0,COMPLETED,BoTorch,0.283456327789404105566006819572,311,0.878747217887670739600025626714,0.800000000000000044408920985006 209,209_0,COMPLETED,BoTorch,0.268531776627381923994164480973,356,0.771509734929296975280976766953,0.800000000000000044408920985006 210,210_0,COMPLETED,BoTorch,0.297059147483202945494440427865,312,0.668046677948009426373232599872,0.800000000000000044408920985006 211,211_0,COMPLETED,BoTorch,0.264181077211146586591894447338,343,0.874318592330021848724186384061,0.800000000000000044408920985006 212,212_0,COMPLETED,BoTorch,0.270569445974226274742591158429,358,1.000000000000000000000000000000,0.800000000000000044408920985006 213,213_0,COMPLETED,BoTorch,0.264511510078202394247171014285,377,1.000000000000000000000000000000,0.800000000000000044408920985006 214,214_0,COMPLETED,BoTorch,0.264511510078202394247171014285,370,0.914378186276954885158829711145,0.800000000000000044408920985006 215,215_0,COMPLETED,BoTorch,0.285769357858794981197547713236,500,1.000000000000000000000000000000,0.800000000000000044408920985006 216,216_0,COMPLETED,BoTorch,0.265833241546425846912882207107,368,1.000000000000000000000000000000,0.800000000000000044408920985006 217,217_0,COMPLETED,BoTorch,0.276352021147703519332594623847,412,1.000000000000000000000000000000,0.800000000000000044408920985006 218,218_0,COMPLETED,BoTorch,0.285273708558211214203481631557,380,0.871642830438248972235726341751,0.800000000000000044408920985006 219,219_0,COMPLETED,BoTorch,0.263905716488600061708780231129,383,0.873060294683975057772329364525,0.800000000000000044408920985006 220,220_0,COMPLETED,BoTorch,0.269302786650512215871344778861,373,0.949563283463774387982425650989,0.800000000000000044408920985006 221,215_0,COMPLETED,BoTorch,0.285769357858794981197547713236,500,1.000000000000000000000000000000,0.800000000000000044408920985006 222,222_0,COMPLETED,BoTorch,0.268917281638947014421603398659,383,0.929627497434246552465708646196,0.800000000000000044408920985006 223,223_0,COMPLETED,BoTorch,0.264181077211146586591894447338,358,0.947436191357290624637244036421,0.800000000000000044408920985006 224,224_0,COMPLETED,BoTorch,0.277178103315343093981937272474,372,0.768712087063325277824787917780,0.799020612880612102202348978608 225,225_0,COMPLETED,BoTorch,0.290780923009141933910370880767,250,1.000000000000000000000000000000,0.800000000000000044408920985006 226,226_0,COMPLETED,BoTorch,0.264511510078202394247171014285,370,0.914452224469342156432105639396,0.799998037603066425305087250308 227,227_0,COMPLETED,BoTorch,0.264015860777618627253104932606,342,1.000000000000000000000000000000,0.800000000000000044408920985006 228,228_0,COMPLETED,BoTorch,0.266934684436611946445339071943,380,0.965319240107303300213459351653,0.798559567634888001208537389175 229,229_0,COMPLETED,BoTorch,0.269688291662077306298783696548,365,0.973863576583314793921886121097,0.800000000000000044408920985006 230,230_0,COMPLETED,BoTorch,0.286099790725850899875126742700,373,0.977648729541179761781677370891,0.800000000000000044408920985006 231,231_0,COMPLETED,BoTorch,0.277618680471417578203841003415,365,0.955519798111623908098977153713,0.800000000000000044408920985006 232,232_0,COMPLETED,BoTorch,0.273653486066747442251312349981,364,1.000000000000000000000000000000,0.800000000000000044408920985006 233,233_0,COMPLETED,BoTorch,0.265833241546425846912882207107,303,0.959553949322008659095217808499,0.800000000000000044408920985006 234,234_0,COMPLETED,BoTorch,0.275801299702610469566366191430,382,1.000000000000000000000000000000,0.800000000000000044408920985006 235,235_0,COMPLETED,BoTorch,0.264511510078202394247171014285,365,0.938316447105499884528967413644,0.800000000000000044408920985006 236,236_0,COMPLETED,BoTorch,0.265943385835444412457206908584,372,1.000000000000000000000000000000,0.800000000000000044408920985006 237,237_0,COMPLETED,BoTorch,0.277673752615926860976003354153,387,0.969519165844187291725120303454,0.800000000000000044408920985006 238,238_0,COMPLETED,BoTorch,0.267154973014649188556290937413,396,0.933779004908241727633821938070,0.800000000000000044408920985006 239,239_0,COMPLETED,BoTorch,0.276021588280647600655015594384,416,0.818087587539432337102596193290,0.800000000000000044408920985006 240,240_0,COMPLETED,BoTorch,0.279436061240224686841315815400,361,1.000000000000000000000000000000,0.800000000000000044408920985006 241,241_0,COMPLETED,BoTorch,0.279711421962771211724430031609,378,0.966108892349967551815836941387,0.800000000000000044408920985006 242,242_0,COMPLETED,BoTorch,0.264511510078202394247171014285,392,1.000000000000000000000000000000,0.800000000000000044408920985006 243,243_0,COMPLETED,BoTorch,0.282244740610199329466922790743,378,0.904400921222387244036156062066,0.800000000000000044408920985006 244,244_0,COMPLETED,BoTorch,0.269027425927965579965928100137,360,0.977674684351510725477396590577,0.800000000000000044408920985006 245,245_0,COMPLETED,BoTorch,0.269027425927965579965928100137,354,1.000000000000000000000000000000,0.800000000000000044408920985006 246,246_0,COMPLETED,BoTorch,0.272331754598524100607903619675,374,1.000000000000000000000000000000,0.800000000000000044408920985006 247,247_0,COMPLETED,BoTorch,0.283511399933913388338169170311,338,0.497856292931495136855346572702,0.800000000000000044408920985006 248,248_0,COMPLETED,BoTorch,0.278224474061019910742231786571,354,0.973304040590569297108913815464,0.800000000000000044408920985006 249,249_0,COMPLETED,BoTorch,0.278609979072585112191973166773,396,0.965560588305434008837835335726,0.800000000000000044408920985006 250,250_0,COMPLETED,BoTorch,0.274424496089877734128492647869,448,0.677222300592064785362822476600,0.800000000000000044408920985006 251,251_0,COMPLETED,BoTorch,0.283896904945478589787910550513,360,0.925360115892686518890286606620,0.800000000000000044408920985006 252,252_0,COMPLETED,BoTorch,0.273212908910672958029408619041,500,0.618052195965712591885221627308,0.800000000000000044408920985006 253,253_0,RUNNING,BoTorch,,398,0.798818729532374871560307383334,0.781131639569701441416782472515 254,254_0,COMPLETED,BoTorch,0.274865073245952218350396378810,352,0.952081214674563636179982495378,0.800000000000000044408920985006 255,255_0,COMPLETED,BoTorch,0.285163564269192648659156930080,371,0.988840638652387027285328713333,0.787800422303434944026889752422 256,256_0,COMPLETED,BoTorch,0.268862209494437731649441047921,354,1.000000000000000000000000000000,0.781122717880043282434598950204 257,257_0,COMPLETED,BoTorch,0.269963652384623831181897912757,316,0.988956860205542631625519334193,0.795428244523919802944078583096 258,258_0,COMPLETED,BoTorch,0.276737526159268609760033541534,403,0.849806287291500428437984737684,0.800000000000000044408920985006 259,259_0,COMPLETED,BoTorch,0.278830267650622354302925032243,351,1.000000000000000000000000000000,0.800000000000000044408920985006 260,260_0,COMPLETED,BoTorch,0.275195506113008026005672945757,361,0.957184276880728490688454712654,0.800000000000000044408920985006 261,261_0,COMPLETED,BoTorch,0.278665051217094394964135517512,352,1.000000000000000000000000000000,0.793664962769950133036900297157 262,262_0,COMPLETED,BoTorch,0.269082498072474973760392913391,361,0.976879885690498994677000155207,0.786449735510538427973870057031 263,263_0,COMPLETED,BoTorch,0.279215772662187444730363949930,417,0.813759295452348263566477726272,0.799184387915465310747720195650 264,264_0,COMPLETED,BoTorch,0.287476594338583524290697823744,365,1.000000000000000000000000000000,0.783930065114958329886007959431 265,265_0,COMPLETED,BoTorch,0.276627381870250044215708840056,440,0.927922219217322763462618695485,0.800000000000000044408920985006 266,266_0,COMPLETED,BoTorch,0.273708558211256725023474700720,500,0.744786375018513191470503898017,0.800000000000000044408920985006 267,267_0,COMPLETED,BoTorch,0.286595440026434666869192824379,476,0.738024596849858993685700170317,0.800000000000000044408920985006 268,268_0,COMPLETED,BoTorch,0.283951977089987872560072901251,484,0.542015798575687601790207281738,0.800000000000000044408920985006 269,269_0,COMPLETED,BoTorch,0.276462165436722084876919325325,469,0.570047933644828752619559963932,0.800000000000000044408920985006 270,270_0,COMPLETED,BoTorch,0.293204097367551486108538938424,500,0.119001310869403534309363124066,0.731041902652132313811250696745 271,271_0,COMPLETED,BoTorch,0.264511510078202394247171014285,349,0.928681174851617163845673985634,0.800000000000000044408920985006 272,272_0,COMPLETED,BoTorch,0.264511510078202394247171014285,490,0.711339466987648827434043141693,0.800000000000000044408920985006 273,273_0,COMPLETED,BoTorch,0.276462165436722084876919325325,469,0.570039185814783078143364036805,0.799999996382466327382587678585 274,274_0,COMPLETED,BoTorch,0.298050446084370479482572591223,500,0.449057296389054894092396352789,0.800000000000000044408920985006 275,275_0,COMPLETED,BoTorch,0.290340345853067560710769612342,500,0.558757918264831343613252556679,0.747939360968299049581275994569 276,276_0,COMPLETED,BoTorch,0.287751955061130049173812039953,468,0.787010987648970483654409235896,0.791783090219285723421194234106 277,277_0,COMPLETED,BoTorch,0.291662077321290902354178342648,475,0.546273556956913375337592242431,0.779025746004914809716979107179 278,278_0,COMPLETED,BoTorch,0.295737416014979603851031697559,500,0.542080462615144309523884658120,0.692623722192892588012114174489 279,279_0,COMPLETED,BoTorch,0.278224474061019910742231786571,447,0.899986609472720244795596045151,0.800000000000000044408920985006 280,280_0,COMPLETED,BoTorch,0.296233065315563370845097779238,452,0.605195164894083759143939005298,0.782081064253801194752213632455 281,281_0,COMPLETED,BoTorch,0.285934574292322940536337227968,500,0.710666496069529474155501702626,0.800000000000000044408920985006 282,282_0,COMPLETED,BoTorch,0.296453353893600612956049644708,463,0.466763662967187298313831433916,0.800000000000000044408920985006 283,283_0,COMPLETED,BoTorch,0.276737526159268609760033541534,344,0.970965635780379643371418296738,0.800000000000000044408920985006 284,284_0,COMPLETED,BoTorch,0.293093953078532920564214236947,500,1.000000000000000000000000000000,0.500000000000000000000000000000 285,285_0,COMPLETED,BoTorch,0.288743253662297583161944203312,362,0.904783247603351381549430243467,0.800000000000000044408920985006 286,286_0,COMPLETED,BoTorch,0.284778059257627447209415549878,353,0.935047208092617987418293523660,0.781619565752822653692533094727 287,287_0,COMPLETED,BoTorch,0.289954840841502359261028232140,500,0.869717856049883164537561697216,0.500000000000000000000000000000 288,288_0,COMPLETED,BoTorch,0.289954840841502359261028232140,500,0.869727479016347282403387453087,0.500000000000000000000000000000 289,289_0,COMPLETED,BoTorch,0.291386716598744377471064126439,500,0.808222844830971176577349979198,0.602645215464353900536309538438 290,290_0,COMPLETED,BoTorch,0.288522965084260341050992337841,402,0.828145117292187205038089814479,0.500000000000000000000000000000 291,291_0,COMPLETED,BoTorch,0.293754818812644535874767370842,500,0.942351149349494932039306149818,0.503186307146251676769566074654 292,292_0,COMPLETED,BoTorch,0.289073686529353501839523232775,457,0.974183312275675339364511273743,0.500000000000000000000000000000 293,293_0,COMPLETED,BoTorch,0.289404119396409309494799799722,500,0.921327802328761102934606697090,0.557325846917591016804749415314 294,294_0,COMPLETED,BoTorch,0.289789624407974399922238717409,418,1.000000000000000000000000000000,0.734837078255498710177562315948 295,295_0,COMPLETED,BoTorch,0.286815728604471908980144689849,254,0.100000000000000005551115123126,0.800000000000000044408920985006 296,296_0,COMPLETED,BoTorch,0.288798325806806865934106554050,456,0.969985726895343347564448777121,0.500567422951536222797130903928 297,297_0,COMPLETED,BoTorch,0.293314241656570051652863639902,334,0.514786495187640080750668403198,0.800000000000000044408920985006 298,298_0,COMPLETED,BoTorch,0.295021478136358594746013750409,371,0.802047668324960216779118127306,0.500326266852395340478665275441 299,299_0,COMPLETED,BoTorch,0.294801189558321352635061884939,402,0.828175035233876144502573879436,0.500002483116878781999048442231 300,300_0,COMPLETED,BoTorch,0.287641810772111483629487338476,50,1.000000000000000000000000000000,0.800000000000000044408920985006 301,301_0,COMPLETED,BoTorch,0.280427359841392220829447978758,395,0.878371272819775117390861396416,0.800000000000000044408920985006 302,302_0,COMPLETED,BoTorch,0.291221500165216418132274611708,250,0.999880893261605230293298518518,0.799966775751612901856901771680 303,303_0,COMPLETED,BoTorch,0.285053419980174083114832228603,444,0.804053724192205443443981494056,0.800000000000000044408920985006 304,304_0,COMPLETED,BoTorch,0.278004185482982668631279921101,393,0.908712312931448051855909398000,0.800000000000000044408920985006 305,305_0,COMPLETED,BoTorch,0.275030289679480066666883431026,399,1.000000000000000000000000000000,0.800000000000000044408920985006 306,306_0,COMPLETED,BoTorch,0.290505562286595409027256664558,119,0.706887094618293021497379413631,0.799383409817482903925167647685 307,307_0,COMPLETED,BoTorch,0.263960788633109344480942581868,437,1.000000000000000000000000000000,0.800000000000000044408920985006 308,308_0,COMPLETED,BoTorch,0.284943275691155406548205064610,70,0.781203632255474289536323340144,0.759073324553110717616277725028 309,309_0,COMPLETED,BoTorch,0.278004185482982668631279921101,400,0.730550347859349136214746067708,0.794760142491494181449240841175 310,300_0,COMPLETED,BoTorch,0.288082387928185967851391069416,50,1.000000000000000000000000000000,0.800000000000000044408920985006 311,307_0,COMPLETED,BoTorch,0.277563608326908295431678652676,437,1.000000000000000000000000000000,0.800000000000000044408920985006 312,312_0,COMPLETED,BoTorch,0.277233175459852376754099623213,448,0.898010723511952102526834096352,0.800000000000000044408920985006 313,313_0,COMPLETED,BoTorch,0.286925872893490474524469391326,50,1.000000000000000000000000000000,0.740704797156905292432327314600 314,314_0,COMPLETED,BoTorch,0.278224474061019910742231786571,385,0.946059566970685161813037211687,0.800000000000000044408920985006 315,315_0,COMPLETED,BoTorch,0.278334618350038587308858950564,434,0.822046806520217887559454084112,0.798450131505901383732748399780 316,316_0,COMPLETED,BoTorch,0.282520101332745854350037006952,50,0.999992873823973282831900633028,0.740709432493519392970426906686 317,24_0,COMPLETED,BoTorch,0.278940411939640919847249733721,50,0.100000000000000005551115123126,0.800000000000000044408920985006 318,318_0,COMPLETED,BoTorch,0.264015860777618627253104932606,387,1.000000000000000000000000000000,0.800000000000000044408920985006 319,319_0,COMPLETED,BoTorch,0.282409957043727288805712305475,62,0.115082222306682732670957136634,0.751205196599644242638760260888 320,320_0,COMPLETED,BoTorch,0.275636083269082510227576676698,424,1.000000000000000000000000000000,0.800000000000000044408920985006 321,321_0,COMPLETED,BoTorch,0.278279546205529193514394137310,424,0.842470598010515270281928223994,0.800000000000000044408920985006 322,322_0,COMPLETED,BoTorch,0.285218636413701931431319280819,424,0.842376037895278040323887580598,0.800000000000000044408920985006 323,323_0,COMPLETED,BoTorch,0.264511510078202394247171014285,349,0.984035261259347815432363404398,0.800000000000000044408920985006 324,324_0,COMPLETED,BoTorch,0.266769468003083987106549557211,336,0.933590232477291004364872151200,0.800000000000000044408920985006 325,325_0,COMPLETED,BoTorch,0.287201233616036999407583607535,405,0.798355786996317928760902304930,0.784354885594374606760936785577 326,326_0,COMPLETED,BoTorch,0.274865073245952218350396378810,421,1.000000000000000000000000000000,0.800000000000000044408920985006 327,327_0,COMPLETED,BoTorch,0.274810001101442935578234028071,406,0.890354400847754412495760334423,0.800000000000000044408920985006 328,328_0,COMPLETED,BoTorch,0.299317105408084538353818970791,462,0.479025935772380284660698634980,0.783723759567748645693541220680 329,329_0,COMPLETED,BoTorch,0.280978081286485270595676411176,410,0.802610349451436055900899191329,0.785748504749085086018567380961 330,330_0,COMPLETED,BoTorch,0.278114329772001345197907085094,396,0.803045710812418600532680557080,0.779427191600907676161114068236 331,331_0,COMPLETED,BoTorch,0.273708558211256725023474700720,367,0.986626421116001850464272138197,0.800000000000000044408920985006 332,332_0,COMPLETED,BoTorch,0.278830267650622354302925032243,408,0.982111240425600273695749820035,0.800000000000000044408920985006 333,333_0,COMPLETED,BoTorch,0.277673752615926860976003354153,409,0.962433585620513842862067122041,0.800000000000000044408920985006 334,334_0,COMPLETED,BoTorch,0.278554906928075829419810816034,128,0.100000000000000005551115123126,0.799553483664968700495023767871 335,335_0,COMPLETED,BoTorch,0.265007159378786161241237095965,339,0.965146170227359490034757527610,0.800000000000000044408920985006 336,336_0,COMPLETED,BoTorch,0.264511510078202394247171014285,339,1.000000000000000000000000000000,0.800000000000000044408920985006 337,337_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.949585111209304444379597498482,0.800000000000000044408920985006 338,338_0,COMPLETED,BoTorch,0.264401365789183828702846312808,316,1.000000000000000000000000000000,0.800000000000000044408920985006 339,339_0,COMPLETED,BoTorch,0.292488159488930477003520991275,321,0.761778960652554792964963326085,0.669717101590609376060569957190 340,340_0,COMPLETED,BoTorch,0.271836105297940333613837537996,356,1.000000000000000000000000000000,0.800000000000000044408920985006 341,341_0,COMPLETED,BoTorch,0.270018724529133113954060263495,387,0.904023238134499806939459176647,0.800000000000000044408920985006 342,342_0,COMPLETED,BoTorch,0.264511510078202394247171014285,329,0.989850196413605720735517934372,0.796152193813440933745084748807 343,343_0,COMPLETED,BoTorch,0.275801299702610469566366191430,355,0.985445767284867435442663463618,0.800000000000000044408920985006 344,344_0,COMPLETED,BoTorch,0.264511510078202394247171014285,356,0.961258130087330964208547356975,0.800000000000000044408920985006 345,345_0,COMPLETED,BoTorch,0.278609979072585112191973166773,366,1.000000000000000000000000000000,0.800000000000000044408920985006 346,346_0,COMPLETED,BoTorch,0.278169401916510627970069435833,345,1.000000000000000000000000000000,0.800000000000000044408920985006 347,347_0,COMPLETED,BoTorch,0.266218746557990937340321124793,340,1.000000000000000000000000000000,0.800000000000000044408920985006 348,348_0,COMPLETED,BoTorch,0.264070932922128021047569745861,343,1.000000000000000000000000000000,0.800000000000000044408920985006 349,349_0,COMPLETED,BoTorch,0.277783896904945426520328055631,371,0.932349307009185390704431029008,0.800000000000000044408920985006 350,350_0,COMPLETED,BoTorch,0.275856371847119752338528542168,343,1.000000000000000000000000000000,0.784515612435524145595877598680 351,351_0,COMPLETED,BoTorch,0.275415794691045268116624811228,338,0.990292207468124408009657599905,0.800000000000000044408920985006 352,352_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.913256487777031500385760409699,0.800000000000000044408920985006 353,353_0,COMPLETED,BoTorch,0.276186804714175559993805109116,330,1.000000000000000000000000000000,0.800000000000000044408920985006 354,354_0,COMPLETED,BoTorch,0.279821566251789888291057195602,333,1.000000000000000000000000000000,0.800000000000000044408920985006 355,355_0,COMPLETED,BoTorch,0.268366560193853964655374966242,332,0.981558225651133042788387683686,0.800000000000000044408920985006 356,356_0,COMPLETED,BoTorch,0.275030289679480066666883431026,349,0.948855759533783649217753008998,0.800000000000000044408920985006 357,357_0,COMPLETED,BoTorch,0.277012886881815134643147757743,348,0.961062590450701081934425928921,0.800000000000000044408920985006 358,358_0,COMPLETED,BoTorch,0.274369423945368451356330297131,349,1.000000000000000000000000000000,0.800000000000000044408920985006 359,359_0,COMPLETED,BoTorch,0.264511510078202394247171014285,337,1.000000000000000000000000000000,0.800000000000000044408920985006 360,360_0,COMPLETED,BoTorch,0.265833241546425846912882207107,303,0.958683511489446082975973695284,0.800000000000000044408920985006 361,361_0,COMPLETED,BoTorch,0.277123031170833811209774921736,363,0.866583358738276965382851813047,0.800000000000000044408920985006 362,362_0,COMPLETED,BoTorch,0.274865073245952218350396378810,335,1.000000000000000000000000000000,0.787408843480241760204307865934 363,363_0,COMPLETED,BoTorch,0.278665051217094394964135517512,352,1.000000000000000000000000000000,0.793670438510651221619696116250 364,364_0,COMPLETED,BoTorch,0.264511510078202394247171014285,343,0.983673936120684100359312651563,0.800000000000000044408920985006 365,365_0,COMPLETED,BoTorch,0.278609979072585112191973166773,339,0.981389278196067804671542944561,0.800000000000000044408920985006 366,366_0,COMPLETED,BoTorch,0.274534640378896299672817349347,310,1.000000000000000000000000000000,0.798703991220894859992540659732 367,367_0,COMPLETED,BoTorch,0.274754928956933541783769214817,363,0.874871131545547742192070472811,0.800000000000000044408920985006 368,368_0,COMPLETED,BoTorch,0.277838969049454820314792868885,345,0.947390430355685042584923394315,0.800000000000000044408920985006 369,369_0,COMPLETED,BoTorch,0.284337482101553074009814281453,50,0.100000000000000005551115123126,0.619975147175153340484143882350 370,370_0,COMPLETED,BoTorch,0.287421522194074241518535473006,337,1.000000000000000000000000000000,0.777112613027567400436623756832 371,371_0,COMPLETED,BoTorch,0.292212798766383952120406775066,321,0.907515187698901826252040336840,0.754975537278088637549444683827 372,372_0,COMPLETED,BoTorch,0.264511510078202394247171014285,329,0.984936700452129887395358309732,0.800000000000000044408920985006 373,373_0,COMPLETED,BoTorch,0.278995484084150202619412084459,321,1.000000000000000000000000000000,0.800000000000000044408920985006 374,374_0,COMPLETED,BoTorch,0.276847670448287286326660705527,349,0.902959480871610931096427066223,0.800000000000000044408920985006 375,375_0,COMPLETED,BoTorch,0.276076660425156994449480407638,334,0.961390718435399471708535656944,0.800000000000000044408920985006 376,376_0,COMPLETED,BoTorch,0.278665051217094394964135517512,352,1.000000000000000000000000000000,0.800000000000000044408920985006 377,377_0,COMPLETED,BoTorch,0.282299812754708723261387603998,328,0.972119922431419980490829857445,0.800000000000000044408920985006 378,378_0,COMPLETED,BoTorch,0.264511510078202394247171014285,349,0.969779718485501041058682858420,0.800000000000000044408920985006 379,379_0,COMPLETED,BoTorch,0.277178103315343093981937272474,358,0.972177304922462792724502378405,0.800000000000000044408920985006 380,380_0,COMPLETED,BoTorch,0.282244740610199329466922790743,166,0.171053403606766074585010528608,0.623142828047839048011269369454 381,381_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.991085614532080994010243557568,0.800000000000000044408920985006 382,382_0,COMPLETED,BoTorch,0.278609979072585112191973166773,407,1.000000000000000000000000000000,0.800000000000000044408920985006 383,383_0,COMPLETED,BoTorch,0.277178103315343093981937272474,166,0.171149750162323199820235686275,0.623159477146704721128855908319 384,384_0,COMPLETED,BoTorch,0.265172375812314120580026610696,322,0.962743576366238640318329089496,0.800000000000000044408920985006 385,385_0,COMPLETED,BoTorch,0.275250578257517308777835296496,358,0.931070939664085051035158357990,0.800000000000000044408920985006 386,386_0,COMPLETED,BoTorch,0.273323053199691634596035783034,360,0.986921747965769924348933272995,0.800000000000000044408920985006 387,387_0,COMPLETED,BoTorch,0.264511510078202394247171014285,354,0.922174370540257859474309043435,0.800000000000000044408920985006 388,388_0,COMPLETED,BoTorch,0.275581011124573227455414325959,396,0.960426822040453154194494800322,0.800000000000000044408920985006 389,389_0,COMPLETED,BoTorch,0.277894041193964103086955219624,343,0.959536667758540517958465443371,0.800000000000000044408920985006 390,390_0,COMPLETED,BoTorch,0.289018614384844108045058419521,398,0.940836702304848948585913603893,0.798559891419931000555720856937 391,391_0,COMPLETED,BoTorch,0.281033153430994553367838761915,353,0.992945998275720942416455727653,0.800000000000000044408920985006 392,392_0,COMPLETED,BoTorch,0.264511510078202394247171014285,338,0.998609985436715308360078324768,0.800000000000000044408920985006 393,393_0,COMPLETED,BoTorch,0.291056283731688458793485096976,257,0.100000000000000005551115123126,0.607358953805776424772489008319 394,394_0,COMPLETED,BoTorch,0.267099900870139905784128586674,336,0.971126349658125942454489631928,0.800000000000000044408920985006 395,395_0,COMPLETED,BoTorch,0.278334618350038587308858950564,359,0.883746314346583794474554451881,0.800000000000000044408920985006 396,396_0,COMPLETED,BoTorch,0.286705584315453232413517525856,353,1.000000000000000000000000000000,0.757523871265718184986326377839 397,397_0,COMPLETED,BoTorch,0.264621654367221070813798178278,328,1.000000000000000000000000000000,0.800000000000000044408920985006 398,398_0,COMPLETED,BoTorch,0.270018724529133113954060263495,353,1.000000000000000000000000000000,0.800000000000000044408920985006 399,399_0,COMPLETED,BoTorch,0.276131732569666277221642758377,333,0.928557871260234257704269111855,0.800000000000000044408920985006 400,400_0,COMPLETED,BoTorch,0.278059257627492062425744734355,371,0.878165462428932119820501611684,0.800000000000000044408920985006 401,401_0,COMPLETED,BoTorch,0.264181077211146586591894447338,353,0.962648112879937345454095520836,0.800000000000000044408920985006 402,402_0,COMPLETED,BoTorch,0.276407093292212802104756974586,403,1.000000000000000000000000000000,0.800000000000000044408920985006 403,403_0,COMPLETED,BoTorch,0.285989646436832223308499578707,413,0.867455724098970981650325029477,0.788965255439551649274676492496 404,404_0,COMPLETED,BoTorch,0.277949113338473385859117570362,331,0.959104206769312073177502497856,0.800000000000000044408920985006 405,405_0,COMPLETED,BoTorch,0.264511510078202394247171014285,346,0.979611769728258141576304751652,0.800000000000000044408920985006 406,406_0,COMPLETED,BoTorch,0.278169401916510627970069435833,359,0.959884444806337833178133678302,0.799999931958860655001330997038 407,407_0,COMPLETED,BoTorch,0.278334618350038587308858950564,318,1.000000000000000000000000000000,0.800000000000000044408920985006 408,408_0,COMPLETED,BoTorch,0.278665051217094394964135517512,361,0.955926763434453374834731675946,0.799999949035196555868765244668 409,409_0,COMPLETED,BoTorch,0.273818702500275401590101864713,327,0.942653704910634937519375853299,0.800000000000000044408920985006 410,410_0,COMPLETED,BoTorch,0.278334618350038587308858950564,351,0.983008458711519983452831183968,0.800000000000000044408920985006 411,411_0,COMPLETED,BoTorch,0.278169401916510627970069435833,332,1.000000000000000000000000000000,0.800000000000000044408920985006 412,412_0,COMPLETED,BoTorch,0.264511510078202394247171014285,344,1.000000000000000000000000000000,0.800000000000000044408920985006 413,413_0,COMPLETED,BoTorch,0.265888313690935129685044557846,341,1.000000000000000000000000000000,0.800000000000000044408920985006 414,414_0,COMPLETED,BoTorch,0.288743253662297583161944203312,50,1.000000000000000000000000000000,0.500000000000000000000000000000 415,415_0,COMPLETED,BoTorch,0.264511510078202394247171014285,334,0.986497041824346387883792885987,0.800000000000000044408920985006 416,416_0,COMPLETED,BoTorch,0.284447626390571639554138982930,370,0.716832978099981255049044648331,0.799940566570973787818843447894 417,417_0,COMPLETED,BoTorch,0.274865073245952218350396378810,335,0.992281399600686064843557687709,0.800000000000000044408920985006 418,418_0,COMPLETED,BoTorch,0.278334618350038587308858950564,340,0.935073468217983538863791181939,0.800000000000000044408920985006 419,414_0,COMPLETED,BoTorch,0.288357748650732492734505285625,50,1.000000000000000000000000000000,0.500000000000000000000000000000 420,420_0,COMPLETED,BoTorch,0.278224474061019910742231786571,346,1.000000000000000000000000000000,0.800000000000000044408920985006 421,421_0,COMPLETED,BoTorch,0.277894041193964103086955219624,413,0.830726048070937417300285687816,0.794418931845575659878022634075 422,422_0,COMPLETED,BoTorch,0.289238902962881350156010284991,289,0.807764866203070308614542227588,0.799631746672094023686838681897 423,423_0,COMPLETED,BoTorch,0.264511510078202394247171014285,378,0.893650746997445843966545453441,0.797782324720174074172973632812 424,424_0,COMPLETED,BoTorch,0.291827293754818861692967857380,485,0.377073714230209633413437586569,0.758561083581298589706420898438 425,425_0,COMPLETED,BoTorch,0.302621434078643058995794490329,373,0.836787578929215625223037022806,0.683350554481148786400979133759 426,426_0,COMPLETED,BoTorch,0.289844696552483793716703530663,324,0.776717238221317551882805219066,0.788401307910680859691865407513 427,427_0,COMPLETED,BoTorch,0.282575173477255248144501820207,68,0.227466815151274204254150390625,0.646022873278707221444960850931 428,428_0,COMPLETED,BoTorch,0.274975217534970783894721080287,185,0.526101604010909773556647905934,0.640938762295991226736191492819 429,429_0,COMPLETED,BoTorch,0.272552043176561342718855485145,180,0.919039545115083456039428710938,0.642256155516952365047700368450 430,430_0,COMPLETED,BoTorch,0.280978081286485270595676411176,135,0.988373387511819578854499468434,0.617067003436386540826674718119 431,431_0,COMPLETED,BoTorch,0.287696882916620766401649689215,50,0.554376172912027120887046294229,0.500000000000000000000000000000 432,432_0,COMPLETED,BoTorch,0.296067848882035411506308264507,379,0.589864200726151532983010383759,0.637542753200978085104111414694 433,433_0,COMPLETED,BoTorch,0.268531776627381923994164480973,313,0.756362622044980503765998491872,0.782387177832424729473359548138 434,434_0,COMPLETED,BoTorch,0.287201233616036999407583607535,348,0.832218886818592240572911578056,0.793625532742846306888395702117 435,435_0,COMPLETED,BoTorch,0.284557770679590316120766146923,381,0.909182007052004359515251508128,0.761303404252976267940766774700 436,436_0,COMPLETED,BoTorch,0.284502698535080922326301333669,393,0.960436632484197638781608930003,0.784294681064784615642793141888 437,437_0,COMPLETED,BoTorch,0.275746227558101075771901378175,350,1.000000000000000000000000000000,0.800000000000000044408920985006 438,438_0,COMPLETED,BoTorch,0.279270844806696727502526300668,92,0.833984427433460906442519444681,0.774533524829894348684433680319 439,439_0,COMPLETED,BoTorch,0.279656349818261928952267680870,328,0.821081615705043121877793055319,0.789951575547456763537468305003 440,440_0,COMPLETED,BoTorch,0.278830267650622354302925032243,65,0.187412137351930130346744363123,0.515699910372495629040656694997 441,441_0,COMPLETED,BoTorch,0.281033153430994553367838761915,211,0.564800202194601363991921516572,0.753880960494279883654655805003 442,442_0,COMPLETED,BoTorch,0.288963542240334825272896068782,403,0.540310607943683907095078211569,0.680518964864313624651970258128 443,443_0,COMPLETED,BoTorch,0.269523075228549346959994181816,368,0.756516205570551436565551739477,0.795682962196360699103081515204 444,444_0,COMPLETED,BoTorch,0.278720123361603677736297868250,309,0.837765400856733344348015180003,0.788302881177514791488647460938 445,445_0,COMPLETED,BoTorch,0.278830267650622354302925032243,179,0.770974516402930021286010742188,0.685794231854379265911347829388 446,446_0,COMPLETED,BoTorch,0.289128758673862784611685583513,497,0.434020082745701141213601204072,0.516020196303725198205825108744 447,447_0,COMPLETED,BoTorch,0.300198259720233506797626432672,316,0.673510281927883647234978070628,0.796794812195003032684326171875 448,448_0,COMPLETED,BoTorch,0.267375261592686430667242802883,375,0.834379109460860468594489702809,0.796353137586265802383422851562 449,449_0,COMPLETED,BoTorch,0.281859235598634239039483873057,118,0.996667972207069374768195757497,0.641122202202677682336684483744 450,450_0,COMPLETED,BoTorch,0.280262143407864261490658464027,86,0.334626543708145596234260210622,0.751033002696931406561020594381 451,451_0,COMPLETED,BoTorch,0.287751955061130049173812039953,300,0.931941129732877016067504882812,0.772496823500841922616189094697 452,452_0,COMPLETED,BoTorch,0.293589602379116687558280318626,212,0.197693394869565969296232310626,0.734770802967250391546372156881 453,453_0,COMPLETED,BoTorch,0.294030179535191060757881587051,330,0.183395398594439040795833761877,0.719966177362948656082153320312 454,454_0,COMPLETED,BoTorch,0.277949113338473385859117570362,348,0.990078176118527553128956242290,0.800000000000000044408920985006 455,455_0,COMPLETED,BoTorch,0.275085361823989460461348244280,395,0.935011040614540434035006910563,0.773092241958079351427102210437 456,456_0,COMPLETED,BoTorch,0.277398391893380336092889137944,364,0.954862922019846016574717850744,0.800000000000000044408920985006 457,457_0,COMPLETED,BoTorch,0.275250578257517308777835296496,372,0.983954708022317992011096521310,0.800000000000000044408920985006 458,458_0,COMPLETED,BoTorch,0.264511510078202394247171014285,338,1.000000000000000000000000000000,0.800000000000000044408920985006 459,459_0,COMPLETED,BoTorch,0.274589712523405693467282162601,367,0.911286600830005233397912434157,0.800000000000000044408920985006 460,460_0,COMPLETED,BoTorch,0.264181077211146586591894447338,346,0.985968012036414820364882416470,0.800000000000000044408920985006 461,461_0,COMPLETED,BoTorch,0.278114329772001345197907085094,341,0.978491497777593366436121868901,0.800000000000000044408920985006 462,462_0,COMPLETED,BoTorch,0.265833241546425846912882207107,303,0.959985816255465307378358374990,0.800000000000000044408920985006 463,463_0,COMPLETED,BoTorch,0.264511510078202394247171014285,398,1.000000000000000000000000000000,0.800000000000000044408920985006 464,464_0,COMPLETED,BoTorch,0.274975217534970783894721080287,354,0.937588310446198391900907154195,0.800000000000000044408920985006 465,465_0,COMPLETED,BoTorch,0.281143297720013229934465925908,380,1.000000000000000000000000000000,0.782510078290110655530043004546 466,466_0,COMPLETED,BoTorch,0.277453464037889618865051488683,337,0.945919677778365386444647811004,0.800000000000000044408920985006 467,467_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.966764598122828111570470355218,0.800000000000000044408920985006 468,468_0,COMPLETED,BoTorch,0.276352021147703519332594623847,348,1.000000000000000000000000000000,0.800000000000000044408920985006 469,469_0,COMPLETED,BoTorch,0.264511510078202394247171014285,315,1.000000000000000000000000000000,0.800000000000000044408920985006 470,470_0,COMPLETED,BoTorch,0.274644784667914976239444513340,356,0.930059076473119139549794454069,0.792409512493759571327700541588 471,471_0,COMPLETED,BoTorch,0.264511510078202394247171014285,345,0.940634984371485849408145440975,0.800000000000000044408920985006 472,472_0,COMPLETED,BoTorch,0.289073686529353501839523232775,163,0.561638532282869973322192436171,0.665457099752961189764732807816 473,473_0,COMPLETED,BoTorch,0.266824540147593380901014370465,327,1.000000000000000000000000000000,0.800000000000000044408920985006 474,474_0,COMPLETED,BoTorch,0.274920145390461501122558729548,309,1.000000000000000000000000000000,0.800000000000000044408920985006 475,475_0,COMPLETED,BoTorch,0.264015860777618627253104932606,340,0.948333643678593407599919373752,0.800000000000000044408920985006 476,476_0,COMPLETED,BoTorch,0.281969379887652804583808574534,154,0.527191479534418139252238688641,0.671666047511963903993148505833 477,477_0,COMPLETED,BoTorch,0.266163674413481654568158774055,370,1.000000000000000000000000000000,0.800000000000000044408920985006 478,478_0,COMPLETED,BoTorch,0.279491133384733969613478166139,308,0.997367845445341294485785965662,0.800000000000000044408920985006 479,479_0,COMPLETED,BoTorch,0.274699856812424259011606864078,327,0.944162466459332327950448870979,0.800000000000000044408920985006 480,480_0,COMPLETED,BoTorch,0.277343319748871053320726787206,432,1.000000000000000000000000000000,0.800000000000000044408920985006 481,481_0,COMPLETED,BoTorch,0.278389690494547870081021301303,331,1.000000000000000000000000000000,0.800000000000000044408920985006 482,482_0,COMPLETED,BoTorch,0.279215772662187444730363949930,186,0.462209141972206860593530564074,0.693832919467097797294741212681 483,483_0,COMPLETED,BoTorch,0.279491133384733969613478166139,308,0.997372926182211405610189558502,0.800000000000000044408920985006 484,484_0,COMPLETED,BoTorch,0.269963652384623831181897912757,380,0.928896930401370002350347476749,0.800000000000000044408920985006 485,485_0,COMPLETED,BoTorch,0.273763630355766007795637051458,375,1.000000000000000000000000000000,0.800000000000000044408920985006 486,486_0,COMPLETED,BoTorch,0.288192532217204533395715770894,139,0.533862741867790924210623870749,0.683835514797203170012096506980 487,487_0,COMPLETED,BoTorch,0.280096926974336413174171411811,149,0.928959041528482298843982789549,0.594357399350710746688264407567 488,488_0,COMPLETED,BoTorch,0.277949113338473385859117570362,346,0.952379497985298306694801340200,0.800000000000000044408920985006 489,489_0,COMPLETED,BoTorch,0.278169401916510627970069435833,336,0.984720799814420288953442650381,0.800000000000000044408920985006 490,490_0,COMPLETED,BoTorch,0.288247604361713816167878121632,370,0.971128635496970149532103278034,0.800000000000000044408920985006 491,491_0,COMPLETED,BoTorch,0.274810001101442935578234028071,336,1.000000000000000000000000000000,0.800000000000000044408920985006 492,242_0,COMPLETED,BoTorch,0.264511510078202394247171014285,392,1.000000000000000000000000000000,0.800000000000000044408920985006 493,493_0,COMPLETED,BoTorch,0.264511510078202394247171014285,348,0.935429293320749422058213440323,0.800000000000000044408920985006 494,494_0,COMPLETED,BoTorch,0.280151999118845695946333762549,373,0.960964076871820616077002341626,0.800000000000000044408920985006 495,358_0,COMPLETED,BoTorch,0.274369423945368451356330297131,349,1.000000000000000000000000000000,0.800000000000000044408920985006 496,496_0,COMPLETED,BoTorch,0.277563608326908295431678652676,334,1.000000000000000000000000000000,0.800000000000000044408920985006 497,497_0,COMPLETED,BoTorch,0.264511510078202394247171014285,328,0.988131727106894564016670301498,0.800000000000000044408920985006 498,498_0,COMPLETED,BoTorch,0.277012886881815134643147757743,362,1.000000000000000000000000000000,0.800000000000000044408920985006 499,499_0,COMPLETED,BoTorch,0.285934574292322940536337227968,240,0.615423200093209721295295366872,0.793603436648845694811882367503 500,500_0,COMPLETED,BoTorch,0.264731798656239636358122879756,331,0.989621768114827493612040143489,0.800000000000000044408920985006 501,501_0,RUNNING,BoTorch,,346,0.967961020360862511857646950375,0.800000000000000044408920985006 502,502_0,RUNNING,BoTorch,,363,0.983294949103297177828153508017,0.800000000000000044408920985006 503,503_0,RUNNING,BoTorch,,360,1.000000000000000000000000000000,0.800000000000000044408920985006 504,504_0,RUNNING,BoTorch,,352,0.932941605873227164380523390719,0.800000000000000044408920985006 505,505_0,RUNNING,BoTorch,,367,0.943557581308920090279457326687,0.800000000000000044408920985006
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 1727302167,1727302176,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 279 recent_samples_proportion 0.8911640584468842 threshold 0.5169260140508414,279,0.8911640584468842,0.5169260140508414,0.28962440797444655,0,None,i7185,6,0.0007248204825741067 1727302167,1727302177,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 459 recent_samples_proportion 0.12882631719112397 threshold 0.6831329035572709,459,0.12882631719112397,0.6831329035572709,0.2883577486507325,0,None,i7185,6,0.0012159349800870704 1727302167,1727302177,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 277 recent_samples_proportion 0.796329740807414 threshold 0.5538515927270055,277,0.796329740807414,0.5538515927270055,0.2872563057605463,0,None,i7185,6,0.0007474076754834546 1727302167,1727302177,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 176 recent_samples_proportion 0.1992199204862118 threshold 0.7115410847589374,176,0.1992199204862118,0.7115410847589374,0.2819693798876528,0,None,i7185,7,0.0005237360942835112 1727302167,1727302178,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 56 recent_samples_proportion 0.5640080939047039 threshold 0.7778040557168424,56,0.5640080939047039,0.7778040557168424,0.2876418107721115,0,None,i7185,8,0.0001796199174765094 1727302187,1727302196,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 346 recent_samples_proportion 0.14665257837623358 threshold 0.7362952074036002,346,0.14665257837623358,0.7362952074036002,0.2897896244079744,0,None,i7185,6,0.0009137480303278932 1727302187,1727302196,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 237 recent_samples_proportion 0.779963809158653 threshold 0.7207854843698442,237,0.779963809158653,0.7207854843698442,0.29177222161030947,0,None,i7185,6,0.0009509123618607044 1727302187,1727302196,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 423 recent_samples_proportion 0.9680545964278281 threshold 0.6966641470789909,423,0.9680545964278281,0.6966641470789909,0.30008811543121494,0,None,i7185,6,0.005745860410471038 1727302187,1727302196,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 346 recent_samples_proportion 0.8039055211469531 threshold 0.6813653163611889,346,0.8039055211469531,0.6813653163611889,0.29188236589932814,0,None,i7185,6,0.002667556999669564 1727302187,1727302197,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 63 recent_samples_proportion 0.5138353911228478 threshold 0.6575860104523599,63,0.5138353911228478,0.6575860104523599,0.2864302235929067,0,None,i7185,8,0.00018148774895112581 1727302199,1727302208,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 354 recent_samples_proportion 0.956087445653975 threshold 0.7142651116475464,354,0.956087445653975,0.7142651116475464,0.2841722656680251,0,None,i7185,6,0.01007820244520321 1727302169,1727302226,57,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 330 recent_samples_proportion 0.13132796743884684 threshold 0.6897510453127325,330,0.13132796743884684,0.6897510453127325,0.2950214781363586,0,None,i7182,7,0.0007460716935411806 1727302167,1727302226,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 403 recent_samples_proportion 0.5925482713617386 threshold 0.5895193100906908,403,0.5925482713617386,0.5895193100906908,0.2905606344311047,0,None,i7182,7,0.0010476819872127733 1727302167,1727302226,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 386 recent_samples_proportion 0.18621035432443023 threshold 0.6061487564817071,386,0.18621035432443023,0.6061487564817071,0.28373168851195063,0,None,i7182,7,0.0011002456696533325 1727302187,1727302226,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 390 recent_samples_proportion 0.6507653087377548 threshold 0.5097444768063724,390,0.6507653087377548,0.5097444768063724,0.2867606564599625,0,None,i7182,7,0.001062280476312859 1727302168,1727302226,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 451 recent_samples_proportion 0.15969422543421388 threshold 0.6249805356375874,451,0.15969422543421388,0.6249805356375874,0.29072585086463265,0,None,i7182,7,0.0011198002716892451 1727302199,1727302226,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 214 recent_samples_proportion 0.2269304571673274 threshold 0.7261720342561603,214,0.2269304571673274,0.7261720342561603,0.28995484084150236,0,None,i7182,7,0.0005507214450930717 1727302187,1727302226,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 113 recent_samples_proportion 0.7620072470977902 threshold 0.6195997603237628,113,0.7620072470977902,0.6195997603237628,0.28180416345412496,0,None,i7182,8,0.0003367873452684549 1727302168,1727302229,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 97 recent_samples_proportion 0.23777572875842454 threshold 0.7720709443092346,97,0.23777572875842454,0.7720709443092346,0.2838418328009693,0,None,i7182,10,0.0002988234113315658 1727302219,1727302235,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.37252391660586004 threshold 0.7155048968270421,50,0.37252391660586004,0.7155048968270421,0.2875867386276022,0,None,i7181,9,0.00014985977784744142 1727302307,1727302317,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 141 recent_samples_proportion 0.4705126144179741 threshold 0.5644992196950593,141,0.4705126144179741,0.5644992196950593,0.28026214340786426,0,None,i7185,7,0.0004258334323475564 1727302307,1727302317,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 97 recent_samples_proportion 0.1 threshold 0.6247298718400034,97,0.1,0.6247298718400034,0.27734331974887105,0,None,i7185,7,0.00031318499762160917 1727302307,1727302318,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8017434438526766 threshold 0.5404526333721693,50,0.8017434438526766,0.5404526333721693,0.28813746007269525,0,None,i7185,8,0.00013315714479339677 1727302307,1727302318,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 56 recent_samples_proportion 1 threshold 0.649486102295222,56,1,0.649486102295222,0.28411719352351583,0,None,i7185,8,0.00016854293890096675 1727302319,1727302329,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 1 threshold 0.5645645117791961,140,1,0.5645645117791961,0.2777838969049454,0,None,i7182,7,0.0004453471843390433 1727302319,1727302330,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.8997985977156759 threshold 0.7370717128943474,50,0.8997985977156759,0.7370717128943474,0.2878620993501487,0,None,i7182,8,0.00015239636054378754 1727302319,1727302331,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.8,50,0.1,0.8,0.2831809670668576,0,None,i7185,9,0.00016241911825125822 1727302320,1727302333,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 0.1272970516785026 threshold 0.5,349,0.1272970516785026,0.5,0.292763520211477,0,None,i7182,9,0.0008196030918149836 1727302327,1727302338,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5572362926591334,50,1,0.5572362926591334,0.2879722436391673,0,None,i7185,8,0.0001324878447909618 1727302347,1727302356,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.1 threshold 0.5,500,0.1,0.5,0.28890847009582554,0,None,i7185,6,0.0012681891055059897 1727302347,1727302356,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 393 recent_samples_proportion 0.1 threshold 0.5087584172895199,393,0.1,0.5087584172895199,0.28918383081837207,0,None,i7185,6,0.001008432157237091 1727302347,1727302357,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 149 recent_samples_proportion 0.1 threshold 0.6769261649214665,149,0.1,0.6769261649214665,0.27987663839629917,0,None,i7185,7,0.0004577004278966786 1727302347,1727302357,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 105 recent_samples_proportion 0.1 threshold 0.6973494599573961,105,0.1,0.6973494599573961,0.2790505562286596,0,None,i7185,7,0.0003330869463093032 1727302347,1727302358,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.3427021197795591 threshold 0.5,50,0.3427021197795591,0.5,0.28263024562176453,0,None,i7185,8,0.0001477506962692554 1727302350,1727302359,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 153 recent_samples_proportion 0.6600614396329413 threshold 0.5,153,0.6600614396329413,0.5,0.27580129970261047,0,None,i7182,7,0.0005003563214819953 1727302348,1727302361,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 0.3526894343615825 threshold 0.6395322771469655,147,0.3526894343615825,0.6395322771469655,0.28103315343099455,0,None,i7182,9,0.0004442486323750781 1727302367,1727302376,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.5172694100436185 threshold 0.5,500,0.5172694100436185,0.5,0.29226787091089323,0,None,i7185,6,0.0012021462401460197 1727302367,1727302377,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 102 recent_samples_proportion 0.8303700831226976 threshold 0.6888152222217417,102,0.8303700831226976,0.6888152222217417,0.2858244300033044,0,None,i7185,7,0.00030903540963502895 1727302367,1727302378,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.7528641040311715 threshold 0.6489795779447989,50,0.7528641040311715,0.6489795779447989,0.2838969049454786,0,None,i7185,8,0.00015194603726061847 1727302367,1727302379,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6986379804302362,50,0.1,0.6986379804302362,0.28141865844255975,0,None,i7185,9,0.0001579830772341617 1727302467,1727302477,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 157 recent_samples_proportion 0.919310758947714 threshold 0.5,157,0.919310758947714,0.5,0.27949113338473397,0,None,i7185,6,0.0004811566309760522 1727302467,1727302477,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 107 recent_samples_proportion 0.1 threshold 0.5144775061747415,107,0.1,0.5144775061747415,0.27954620552924336,0,None,i7185,7,0.0003281244538129375 1727302471,1727302480,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 141 recent_samples_proportion 0.824542545677123 threshold 0.5,141,0.824542545677123,0.5,0.28290560634431106,0,None,i7185,7,0.00040501876355663675 1727302487,1727302497,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 169 recent_samples_proportion 0.48521348380511253 threshold 0.5,169,0.48521348380511253,0.5,0.281638947020597,0,None,i7185,6,0.0004972079461830842 1727302487,1727302497,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 163 recent_samples_proportion 0.1 threshold 0.5,163,0.1,0.5,0.27883026765062235,0,None,i7185,7,0.0005046611060489233 1727302487,1727302497,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 162 recent_samples_proportion 0.8572640417449945 threshold 0.5671509244444168,162,0.8572640417449945,0.5671509244444168,0.28070272056393875,0,None,i7185,7,0.0004966691551117146 1727302487,1727302497,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 174 recent_samples_proportion 0.8659476023995604 threshold 0.5,174,0.8659476023995604,0.5,0.2826853177662738,0,None,i7185,7,0.0005064477602914716 1727302500,1727302510,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 88 recent_samples_proportion 0.1 threshold 0.5,88,0.1,0.5,0.28213459632118076,0,None,i7185,7,0.00025718420194494165 1727302500,1727302510,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 131 recent_samples_proportion 0.9647754611494695 threshold 0.5,131,0.9647754611494695,0.5,0.2846679149686089,0,None,i7182,7,0.00036525789961319884 1727302500,1727302514,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 159 recent_samples_proportion 1 threshold 0.6701569310372014,159,1,0.6701569310372014,0.2776186804714176,0,None,i7182,9,0.000556120674946925 1727302507,1727302516,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 166 recent_samples_proportion 1 threshold 0.5235387481693294,166,1,0.5235387481693294,0.28406212137900655,0,None,i7185,6,0.0004719734253741553 1727302507,1727302516,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 150 recent_samples_proportion 0.8957727071804488 threshold 0.606021793766658,150,0.8957727071804488,0.606021793766658,0.2824099570437273,0,None,i7185,7,0.0004364120359014843 1727302529,1727302539,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 155 recent_samples_proportion 0.7786458147500023 threshold 0.5,155,0.7786458147500023,0.5,0.28587950214781366,0,None,i7185,7,0.0004233371804019783 1727302529,1727302539,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 169 recent_samples_proportion 0.7293098406987234 threshold 0.5,169,0.7293098406987234,0.5,0.2811983698645225,0,None,i7185,7,0.0005013643344479379 1727302529,1727302539,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 115 recent_samples_proportion 0.1 threshold 0.5792294923282517,115,0.1,0.5792294923282517,0.2778940411939641,0,None,i7185,7,0.00036418676207767626 1727302529,1727302539,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 104 recent_samples_proportion 0.34479023498913164 threshold 0.5,104,0.34479023498913164,0.5,0.2815288027315783,0,None,i7185,7,0.00030705922432805585 1727302667,1727302677,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 137 recent_samples_proportion 0.1 threshold 0.539134108423303,137,0.1,0.539134108423303,0.2779491133384734,0,None,i7185,7,0.0004337990459810041 1727302667,1727302677,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 125 recent_samples_proportion 0.1 threshold 0.628846958941706,125,0.1,0.628846958941706,0.2820795241766715,0,None,i7185,7,0.0003680525714037496 1727302667,1727302677,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 153 recent_samples_proportion 1 threshold 0.7994255054449628,153,1,0.7994255054449628,0.2853287807027206,0,None,i7185,7,0.0005977342513815039 1727302667,1727302677,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 137 recent_samples_proportion 0.3324526730830339 threshold 0.5,137,0.3324526730830339,0.5,0.2813085141535412,0,None,i7185,7,0.00040795750124971336 1727302680,1727302689,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 153 recent_samples_proportion 0.5981395525465576 threshold 0.6001085107384855,153,0.5981395525465576,0.6001085107384855,0.27503028967948007,0,None,i7185,7,0.000520286838916876 1727302680,1727302691,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 133 recent_samples_proportion 0.1 threshold 0.8,133,0.1,0.8,0.28103315343099455,0,None,i7182,7,0.00042647868708007496 1727302681,1727302694,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 138 recent_samples_proportion 0.1 threshold 0.5,138,0.1,0.5,0.28097808128648527,0,None,i7182,9,0.0004104992925347589 1727302687,1727302697,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 145 recent_samples_proportion 0.44665707952202405 threshold 0.8,145,0.44665707952202405,0.8,0.2890186143848441,0,None,i7185,7,0.00042758844274679074 1727302707,1727302717,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 156 recent_samples_proportion 0.6834392461115323 threshold 0.7559223361733028,156,0.6834392461115323,0.7559223361733028,0.2916070051767816,0,None,i7185,7,0.0004211399286005838 1727302707,1727302717,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 0.1 threshold 0.5829154516977773,147,0.1,0.5829154516977773,0.2819693798876528,0,None,i7185,7,0.00043283974734174477 1727302707,1727302717,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 120 recent_samples_proportion 0.19756783037374148 threshold 0.5376752873537948,120,0.19756783037374148,0.5376752873537948,0.28075779270844803,0,None,i7185,7,0.00035963219199701944 1727302707,1727302717,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 124 recent_samples_proportion 0.28353564149203775 threshold 0.5940052940010256,124,0.28353564149203775,0.5940052940010256,0.2798215662517899,0,None,i7185,7,0.00037862099350148635 1727302710,1727302720,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 141 recent_samples_proportion 0.4166961884822521 threshold 0.5,141,0.4166961884822521,0.5,0.2815288027315783,0,None,i7185,7,0.00041585973688524095 1727302727,1727302737,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 126 recent_samples_proportion 0.1 threshold 0.5893147448336813,126,0.1,0.5893147448336813,0.27624187685868484,0,None,i7185,7,0.0004091627637839372 1727302727,1727302737,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 125 recent_samples_proportion 0.1 threshold 0.5229437115854407,125,0.1,0.5229437115854407,0.27849983478356644,0,None,i7185,7,0.00039051157015690554 1727302727,1727302737,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 129 recent_samples_proportion 0.1 threshold 0.6991438540743933,129,0.1,0.6991438540743933,0.28108822557550395,0,None,i7185,7,0.00039742361000373117 1727302875,1727302885,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 168 recent_samples_proportion 0.39541366504933806 threshold 0.569241035680599,168,0.39541366504933806,0.569241035680599,0.28147373058706904,0,None,i7185,7,0.0004987665917824046 1727302875,1727302885,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 0.6456547328490992 threshold 0.5723508803738483,140,0.6456547328490992,0.5723508803738483,0.2831809670668576,0,None,i7185,7,0.0004028505688909161 1727302875,1727302886,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 73 recent_samples_proportion 0.1 threshold 0.6010015361359895,73,0.1,0.6010015361359895,0.2816940191651063,0,None,i7185,7,0.00021937070896207352 1727302887,1727302897,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 176 recent_samples_proportion 1 threshold 0.6095069057693824,176,1,0.6095069057693824,0.27883026765062235,0,None,i7185,6,0.0005782575173477247 1727302887,1727302897,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 159 recent_samples_proportion 0.6464487556310375 threshold 0.5694660490179485,159,0.6464487556310375,0.5694660490179485,0.28224474061019933,0,None,i7185,7,0.00046935359194418555 1727302888,1727302897,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 196 recent_samples_proportion 0.1 threshold 0.5,196,0.1,0.5,0.2859896464368322,0,None,i7182,7,0.0005337761698594389 1727302890,1727302901,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5,50,0.1,0.5,0.2834563277894041,0,None,i7185,8,0.0001470712760884388 1727302888,1727302901,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 136 recent_samples_proportion 1 threshold 0.6231417115291609,136,1,0.6231417115291609,0.27772882476043614,0,None,i7182,9,0.0004422981605903733 1727302907,1727302917,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 167 recent_samples_proportion 0.25845876359626774 threshold 0.5647751811527503,167,0.25845876359626774,0.5647751811527503,0.2831258949223483,0,None,i7185,6,0.0004786644335855666 1727302907,1727302918,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.5667569002445003,50,0.1,0.5667569002445003,0.2834012556448948,0,None,i7185,8,0.00014723044413615348 1727302920,1727302930,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 187 recent_samples_proportion 0.1 threshold 0.5449394985678014,187,0.1,0.5449394985678014,0.2790505562286596,0,None,i7185,7,0.0005759628446598367 1727302920,1727302930,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 194 recent_samples_proportion 0.2868509070748282 threshold 0.5045276802559352,194,0.2868509070748282,0.5045276802559352,0.2827403899107831,0,None,i7185,7,0.0005632922606875877 1727302920,1727302930,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 218 recent_samples_proportion 0.1 threshold 0.5332538416348688,218,0.1,0.5332538416348688,0.2875316664830928,0,None,i7185,6,0.0005708697906452575 1727302927,1727302937,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 175 recent_samples_proportion 0.8223664762232946 threshold 0.6132992545362577,175,0.8223664762232946,0.6132992545362577,0.27816940191651063,0,None,i7185,6,0.0005561741326682505 1727302947,1727302957,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 155 recent_samples_proportion 0.5076390058286016 threshold 0.5632067366105021,155,0.5076390058286016,0.5632067366105021,0.2805925762749202,0,None,i7185,7,0.0004714948512375767 1727302947,1727302957,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 175 recent_samples_proportion 0.566697434739261 threshold 0.5737566279975408,175,0.566697434739261,0.5737566279975408,0.28587950214781366,0,None,i7185,7,0.00048463487168190267 1727302947,1727302958,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 86 recent_samples_proportion 0.1 threshold 0.6670656068527109,86,0.1,0.6670656068527109,0.28411719352351583,0,None,i7185,7,0.00024864255342815886 1727303100,1727303110,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 153 recent_samples_proportion 1 threshold 0.6276457311244895,153,1,0.6276457311244895,0.2853838528472299,0,None,i7185,6,0.0004491663896217801 1727303108,1727303117,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 111 recent_samples_proportion 0.1 threshold 0.6479112386182806,111,0.1,0.6479112386182806,0.27855490692807583,0,None,i7185,7,0.0003486755149245506 1727303128,1727303137,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 169 recent_samples_proportion 1 threshold 0.6482693001396873,169,1,0.6482693001396873,0.2789954840841502,0,None,i7185,6,0.0005705928374417909 1727303128,1727303137,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 156 recent_samples_proportion 1 threshold 0.5962348212787242,156,1,0.5962348212787242,0.2805925762749202,0,None,i7185,7,0.0004756673720449889 1727303128,1727303138,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 106 recent_samples_proportion 0.20488116344456808 threshold 0.6030621109598383,106,0.20488116344456808,0.6030621109598383,0.2792708448066967,0,None,i7185,7,0.00032977332041501315 1727303128,1727303138,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 102 recent_samples_proportion 0.1 threshold 0.5757336397584346,102,0.1,0.5757336397584346,0.2798215662517899,0,None,i7185,7,0.00031515273447522567 1727303148,1727303157,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 0.1 threshold 0.629480158745431,140,0.1,0.629480158745431,0.2792708448066967,0,None,i7185,7,0.00043363893314415117 1727303148,1727303157,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 146 recent_samples_proportion 1 threshold 0.6729329053040212,146,1,0.6729329053040212,0.28263024562176453,0,None,i7185,7,0.00046172092584142316 1727303148,1727303158,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 103 recent_samples_proportion 0.1 threshold 0.8,103,0.1,0.8,0.2782795462055292,0,None,i7185,7,0.0003593810455799662 1727303161,1727303171,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 112 recent_samples_proportion 0.35042947579757866 threshold 0.5838883434696689,112,0.35042947579757866,0.5838883434696689,0.2812534420090318,0,None,i7185,7,0.0003338965239432209 1727303162,1727303172,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 126 recent_samples_proportion 1 threshold 0.6190254032519911,126,1,0.6190254032519911,0.28549399713624846,0,None,i7185,7,0.0003565619867135436 1727303168,1727303177,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 1 threshold 0.6459353809958557,147,1,0.6459353809958557,0.27690274259279657,0,None,i7185,7,0.0004951745407173048 1727303188,1727303197,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 183 recent_samples_proportion 1 threshold 0.6470200221974943,183,1,0.6470200221974943,0.2875867386276022,0,None,i7185,7,0.0005138049526197992 1727303188,1727303197,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 179 recent_samples_proportion 0.755580169805031 threshold 0.5792964203407792,179,0.755580169805031,0.5792964203407792,0.2820244520321621,0,None,i7185,6,0.00052847007357416 1727303188,1727303197,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 137 recent_samples_proportion 0.2724266334840751 threshold 0.5671320299901358,137,0.2724266334840751,0.5671320299901358,0.27734331974887105,0,None,i7185,7,0.00043845899667025286 1727303208,1727303217,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 204 recent_samples_proportion 1 threshold 0.5984903665968457,204,1,0.5984903665968457,0.2850534199801741,0,None,i7185,6,0.0005938502329618053 1727303208,1727303217,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 152 recent_samples_proportion 1 threshold 0.6145934403769864,152,1,0.6145934403769864,0.283070822777839,0,None,i7185,7,0.0004458449264188253 1727303401,1727303410,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 218 recent_samples_proportion 1 threshold 0.5277256134584865,218,1,0.5277256134584865,0.2874765943385835,0,None,i7185,6,0.000578597468857042 1727303401,1727303411,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 117 recent_samples_proportion 0.559933035129034 threshold 0.518328787245505,117,0.559933035129034,0.518328787245505,0.28174909130961556,0,None,i7185,7,0.00034375096736201545 1727303408,1727303417,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 129 recent_samples_proportion 0.18243277765768157 threshold 0.5739616798781871,129,0.18243277765768157,0.5739616798781871,0.2816940191651063,0,None,i7185,7,0.00038151427645578004 1727303428,1727303437,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 0.21578326674654924 threshold 0.5267361180095317,140,0.21578326674654924,0.5267361180095317,0.2822998127547087,0,None,i7185,7,0.00040658731688511876 1727303428,1727303438,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 132 recent_samples_proportion 0.21459393576115934 threshold 0.604675191583381,132,0.21459393576115934,0.604675191583381,0.28246502918823657,0,None,i7185,7,0.0003871489561773683 1727303428,1727303438,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 84 recent_samples_proportion 0.1 threshold 0.8,84,0.1,0.8,0.2792708448066967,0,None,i7185,8,0.0002795540330421685 1727303448,1727303457,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 152 recent_samples_proportion 0.24245143474410824 threshold 0.5,152,0.24245143474410824,0.5,0.28026214340786426,0,None,i7185,6,0.0004583122534588107 1727303448,1727303457,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 133 recent_samples_proportion 0.1 threshold 0.5685339263719333,133,0.1,0.5685339263719333,0.2815288027315783,0,None,i7185,7,0.00039413572077929556 1727303448,1727303458,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 121 recent_samples_proportion 0.46109932104078444 threshold 0.5202383756751139,121,0.46109932104078444,0.5202383756751139,0.27993171054080845,0,None,i7185,7,0.00036764377550807753 1727303461,1727303471,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 114 recent_samples_proportion 0.1 threshold 0.5,114,0.1,0.5,0.27954620552924336,0,None,i7185,7,0.00034902410055261503 1727303461,1727303471,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 116 recent_samples_proportion 0.1 threshold 0.5529024560567254,116,0.1,0.5529024560567254,0.2789954840841502,0,None,i7185,7,0.0003593993846224268 1727303488,1727303498,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 155 recent_samples_proportion 0.1 threshold 0.5531118181412809,155,0.1,0.5531118181412809,0.2797664941072805,0,None,i7185,7,0.0004745782192062907 1727303488,1727303498,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 158 recent_samples_proportion 0.3349274179875885 threshold 0.5108324489873969,158,0.3349274179875885,0.5108324489873969,0.2819143077431435,0,None,i7185,7,0.0004639706333881452 1727303488,1727303498,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 111 recent_samples_proportion 0.17387002464523488 threshold 0.5,111,0.17387002464523488,0.5,0.285769357858795,0,None,i7185,7,0.0003016995742683785 1727303491,1727303501,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 118 recent_samples_proportion 0.26840870533640004 threshold 0.6443320278364686,118,0.26840870533640004,0.6443320278364686,0.28362154422293206,0,None,i7185,7,0.00034041238317497895 1727303508,1727303518,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 122 recent_samples_proportion 0.7085570932788313 threshold 0.55608299527451,122,0.7085570932788313,0.55608299527451,0.28246502918823657,0,None,i7185,7,0.0003553284940258038 1727303508,1727303518,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 106 recent_samples_proportion 0.1 threshold 0.7320726359402523,106,0.1,0.7320726359402523,0.2808679369974667,0,None,i7185,7,0.0003280678056351977 1727303521,1727303531,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 121 recent_samples_proportion 0.5669095731711752 threshold 0.5418652728915366,121,0.5669095731711752,0.5418652728915366,0.2835113999339134,0,None,i7185,7,0.00034579312504823495 1727303756,1727303766,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 149 recent_samples_proportion 0.7546958247479605 threshold 0.5884254204913832,149,0.7546958247479605,0.5884254204913832,0.2756360832690825,0,None,i7185,7,0.000497516152940012 1727303756,1727303766,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 129 recent_samples_proportion 0.589246593185611 threshold 0.5,129,0.589246593185611,0.5,0.2831258949223483,0,None,i7185,7,0.00036846830499032824 1727303761,1727303771,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 110 recent_samples_proportion 0.1 threshold 0.6091150665595034,110,0.1,0.6091150665595034,0.28141865844255975,0,None,i7185,7,0.00032872255200897 1727303797,1727303813,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 142 recent_samples_proportion 0.6146504476962783 threshold 0.5403679095310723,142,0.6146504476962783,0.5403679095310723,0.2797664941072805,0,None,i7186,7,0.0004366119616697874 1727303794,1727303813,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 203 recent_samples_proportion 0.5310400334592466 threshold 0.5,203,0.5310400334592466,0.5,0.2828505341998018,0,None,i7186,7,0.0005876448147072659 1727303794,1727303813,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 165 recent_samples_proportion 0.1 threshold 0.624624212429148,165,0.1,0.624624212429148,0.28114329772001323,0,None,i7186,7,0.0004925897369999139 1727303794,1727303813,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 121 recent_samples_proportion 0.1 threshold 0.6851146315776618,121,0.1,0.6851146315776618,0.2793259169512061,0,None,i7186,7,0.00037682926277258767 1727303794,1727303814,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 87 recent_samples_proportion 0.1 threshold 0.7511110287448326,87,0.1,0.7511110287448326,0.27960127767375265,0,None,i7186,8,0.00029273642589439184 1727303816,1727303827,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 146 recent_samples_proportion 0.5497764974560994 threshold 0.5,146,0.5497764974560994,0.5,0.2805925762749202,0,None,i7186,7,0.0004405771560744569 1727303816,1727303827,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 127 recent_samples_proportion 0.1 threshold 0.7521566892692402,127,0.1,0.7521566892692402,0.28114329772001323,0,None,i7186,8,0.00040610451599992894 1727303816,1727303827,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 111 recent_samples_proportion 0.1 threshold 0.7427164009904379,111,0.1,0.7427164009904379,0.27954620552924336,0,None,i7186,8,0.0003581489136389579 1727303822,1727303832,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 158 recent_samples_proportion 0.1 threshold 0.6005840017075629,158,0.1,0.6005840017075629,0.2776186804714176,0,None,i7186,7,0.0005019850340228881 1727303836,1727303846,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 142 recent_samples_proportion 0.826278419562595 threshold 0.5526840771327152,142,0.826278419562595,0.5526840771327152,0.2812534420090318,0,None,i7186,7,0.00042134561354739784 1727303852,1727303862,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 105 recent_samples_proportion 1 threshold 0.5303645695943455,105,1,0.5303645695943455,0.27888533979513164,0,None,i7186,7,0.000326221467769837 1727303852,1727303862,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 117 recent_samples_proportion 0.1 threshold 0.7284697424016293,117,0.1,0.7284697424016293,0.27591144399162904,0,None,i7186,7,0.0003948077386782087 1727303877,1727303888,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 201 recent_samples_proportion 0.6085905751696508 threshold 0.5387170430235118,201,0.6085905751696508,0.5387170430235118,0.28213459632118076,0,None,i7186,8,0.0005932771931229905 1727303877,1727303888,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 136 recent_samples_proportion 0.611143793025807 threshold 0.5405281778305309,136,0.611143793025807,0.5405281778305309,0.2811983698645225,0,None,i7186,8,0.00040568411795023976 1727304137,1727304147,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 130 recent_samples_proportion 0.1 threshold 0.6599274030013762,130,0.1,0.6599274030013762,0.28037228769688294,0,None,i7185,7,0.00039684339425824256 1727304154,1727304164,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 135 recent_samples_proportion 0.1 threshold 0.5978704347120208,135,0.1,0.5978704347120208,0.27767375261592686,0,None,i7185,7,0.00042931239924300806 1727304154,1727304164,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 126 recent_samples_proportion 1 threshold 0.5744205025342664,126,1,0.5744205025342664,0.2859896464368322,0,None,i7185,7,0.00034786577610914894 1727304158,1727304167,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 157 recent_samples_proportion 0.1 threshold 0.7751001457918172,157,0.1,0.7751001457918172,0.27508536182398946,0,None,i7185,7,0.0005436479586423345 1727304177,1727304187,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 150 recent_samples_proportion 0.4019544125109976 threshold 0.5514102321453699,150,0.4019544125109976,0.5514102321453699,0.28477805925762745,0,None,i7185,6,0.0004165120172972813 1727304177,1727304187,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 0.24124570140502868 threshold 0.626640860581472,147,0.24124570140502868,0.626640860581472,0.28709108932701843,0,None,i7185,6,0.0003905115701569049 1727304198,1727304209,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 0.1 threshold 0.7368450880128716,147,0.1,0.7368450880128716,0.283070822777839,0,None,i7186,8,0.00043450988591665175 1727304198,1727304209,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 102 recent_samples_proportion 0.1 threshold 0.6651052916053988,102,0.1,0.6651052916053988,0.2804824319859015,0,None,i7186,8,0.00031497401947428317 1727304212,1727304224,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 144 recent_samples_proportion 0.7988552815590896 threshold 0.6074193639174305,144,0.7988552815590896,0.6074193639174305,0.2749752175349708,0,None,i7186,8,0.0004906427419920094 1727304212,1727304224,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 95 recent_samples_proportion 0.1 threshold 0.7035872123375245,95,0.1,0.7035872123375245,0.27772882476043614,0,None,i7186,9,0.0003110668382174054 1727304218,1727304229,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 149 recent_samples_proportion 0.1 threshold 0.6222831288594162,149,0.1,0.6222831288594162,0.283070822777839,0,None,i7186,8,0.00043085854233752027 1727304238,1727304248,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.1 threshold 0.8,500,0.1,0.8,0.29672871461614714,0,None,i7186,7,0.0011465019175119402 1727304238,1727304249,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 142 recent_samples_proportion 0.1 threshold 0.7131644794852424,142,0.1,0.7131644794852424,0.2801519991188457,0,None,i7186,8,0.0004405771560744571 1727304258,1727304269,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 152 recent_samples_proportion 0.1 threshold 0.5,152,0.1,0.5,0.28141865844255975,0,None,i7186,8,0.0004485112785885099 1727304258,1727304269,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 125 recent_samples_proportion 0.1 threshold 0.5532703226354169,125,0.1,0.5532703226354169,0.27811432977200135,0,None,i7186,8,0.00039320740939861946 1727304272,1727304282,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 0.44001048954359867 threshold 0.5972407543006192,140,0.44001048954359867,0.5972407543006192,0.2842824099570437,0,None,i7185,7,0.00039730618538857336 1727304272,1727304282,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 88 recent_samples_proportion 0.2802394379887856 threshold 0.6078377106274596,88,0.2802394379887856,0.6078377106274596,0.2822998127547087,0,None,i7185,7,0.000260215882806476 1727304278,1727304289,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 0.10000044439715157 threshold 0.7368583084866044,147,0.10000044439715157,0.7368583084866044,0.2776186804714176,0,None,i7186,8,0.0004807144817337826 1727304649,1727304660,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 151 recent_samples_proportion 0.1 threshold 0.8,151,0.1,0.8,0.28108822557550395,0,None,i7186,8,0.0004841342158227271 1727304649,1727304661,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 119 recent_samples_proportion 0.1 threshold 0.8,119,0.1,0.8,0.28108822557550395,0,None,i7186,8,0.0003887209032153283 1727304663,1727304673,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 158 recent_samples_proportion 0.8112265803304618 threshold 0.601976145592748,158,0.8112265803304618,0.601976145592748,0.28070272056393875,0,None,i7186,7,0.00048763880683695617 1727304663,1727304674,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 156 recent_samples_proportion 0.7593285264714187 threshold 0.5736824271184304,156,0.7593285264714187,0.5736824271184304,0.27888533979513164,0,None,i7186,8,0.00048647060983221307 1727304689,1727304700,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 157 recent_samples_proportion 0.7156672417000245 threshold 0.6170269213536449,157,0.7156672417000245,0.6170269213536449,0.28521863641370193,0,None,i7186,8,0.0004425617378585765 1727304689,1727304700,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 103 recent_samples_proportion 1 threshold 0.6411596545330127,103,1,0.6411596545330127,0.281638947020597,0,None,i7186,8,0.0003155930676371672 1727304709,1727304720,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 138 recent_samples_proportion 0.8822972576992077 threshold 0.6251565683659346,138,0.8822972576992077,0.6251565683659346,0.2824099570437273,0,None,i7186,8,0.0004057268146271612 1727304709,1727304720,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 117 recent_samples_proportion 0.8559482817866036 threshold 0.6160266132983829,117,0.8559482817866036,0.6160266132983829,0.2824099570437273,0,None,i7186,8,0.0003462202151485109 1727304723,1727304734,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 140 recent_samples_proportion 0.7689565956168348 threshold 0.5816792160580055,140,0.7689565956168348,0.5816792160580055,0.2828505341998018,0,None,i7186,8,0.0004054524024897807 1727304723,1727304735,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 101 recent_samples_proportion 0.7533908322496972 threshold 0.5,101,0.7533908322496972,0.5,0.28252010133274585,0,None,i7186,8,0.00029278467787151454 1727304749,1727304760,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 158 recent_samples_proportion 0.5738302797529852 threshold 0.504244906419654,158,0.5738302797529852,0.504244906419654,0.280041854829827,0,None,i7186,8,0.000480541013152008 1727304749,1727304760,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 143 recent_samples_proportion 0.7387805993153314 threshold 0.6358658212699707,143,0.7387805993153314,0.6358658212699707,0.2800969269743364,0,None,i7186,8,0.0004483145648071695 1727304753,1727304765,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 149 recent_samples_proportion 0.85068323557702 threshold 0.6362283541219508,149,0.85068323557702,0.6362283541219508,0.28224474061019933,0,None,i7186,8,0.0004530282496156921 1727304769,1727304779,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 252 recent_samples_proportion 1 threshold 0.7006592489873796,252,1,0.7006592489873796,0.29562727172596104,0,None,i7186,7,0.000998359132514875 1727304769,1727304779,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 292 recent_samples_proportion 1 threshold 0.8,292,1,0.8,0.2804824319859015,0,None,i7186,7,0.05408084590813966 1727304783,1727304795,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 150 recent_samples_proportion 0.7917067846284392 threshold 0.6211333055342326,150,0.7917067846284392,0.6211333055342326,0.2828505341998018,0,None,i7186,8,0.0004401064539846338 1727304789,1727304800,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 147 recent_samples_proportion 0.847495953438784 threshold 0.5891116307327666,147,0.847495953438784,0.5891116307327666,0.28108822557550395,0,None,i7186,8,0.0004437896978374998 1727304809,1727304820,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 164 recent_samples_proportion 0.7397016753002681 threshold 0.5547113662375802,164,0.7397016753002681,0.5547113662375802,0.28070272056393875,0,None,i7186,8,0.0004921125573583962 1727304813,1727304824,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 301 recent_samples_proportion 1 threshold 0.8,301,1,0.8,0.26847670448287253,0,None,i7186,7,0.03304328670558432 1727305264,1727305274,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 90 recent_samples_proportion 0.2944782494425213 threshold 0.5,90,0.2944782494425213,0.5,0.2819143077431435,0,None,i7185,7,0.00026401420031095014 1727305264,1727305275,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 75 recent_samples_proportion 0.3959102515031201 threshold 0.5443269270504096,75,0.3959102515031201,0.5443269270504096,0.2771781033153431,0,None,i7185,7,0.00024120205063569972 1727305289,1727305298,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 174 recent_samples_proportion 0.1 threshold 0.8,174,0.1,0.8,0.27993171054080845,0,None,i7185,7,0.0005441127877519547 1727305289,1727305299,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 92 recent_samples_proportion 0.3605455601275477 threshold 0.560290197268636,92,0.3605455601275477,0.560290197268636,0.283070822777839,0,None,i7185,7,0.00026565889398012907 1727305294,1727305303,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 324 recent_samples_proportion 1 threshold 0.8,324,1,0.8,0.27657230972574076,0,None,i7185,6,0.057990968168300405 1727305309,1727305318,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 335 recent_samples_proportion 1 threshold 0.8,335,1,0.8,0.2748650732459522,0,None,i7185,6,0.029849102324044474 1727305324,1727305333,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 291 recent_samples_proportion 0.9965483944720003 threshold 0.7985232199870482,291,0.9965483944720003,0.7985232199870482,0.2804824319859015,0,None,i7185,6,0.013520211477034916 1727305324,1727305333,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 303 recent_samples_proportion 0.9586949090862703 threshold 0.8,303,0.9586949090862703,0.8,0.26583324154642585,0,None,i7185,6,0.03436501817380766 1727305349,1727305358,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 142 recent_samples_proportion 0.1 threshold 0.5581937951469722,142,0.1,0.5581937951469722,0.28174909130961556,0,None,i7185,7,0.0004174118889395902 1727305349,1727305359,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 138 recent_samples_proportion 0.1 threshold 0.7443580551708479,138,0.1,0.7443580551708479,0.28185923559863424,0,None,i7185,7,0.0004132579032863755 1727305354,1727305364,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 293 recent_samples_proportion 0.9963914928829497 threshold 0.7961622243584584,293,0.9963914928829497,0.7961622243584584,0.2645115100782024,0,None,i7185,6,0 1727305369,1727305378,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 311 recent_samples_proportion 0.9946905025915302 threshold 0.8,311,0.9946905025915302,0.8,0.2748650732459522,0,None,i7185,6,0.029849102324044474 1727305384,1727305394,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 166 recent_samples_proportion 0.1 threshold 0.8,166,0.1,0.8,0.27178103315343094,0,None,i7185,7,0.0006256195616257299 1727305389,1727305398,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 312 recent_samples_proportion 0.9962428455059601 threshold 0.7999846225027647,312,0.9962428455059601,0.7999846225027647,0.27624187685868484,0,None,i7185,6,0.029160700517678162 1727305409,1727305418,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 332 recent_samples_proportion 0.9456496618795844 threshold 0.8,332,0.9456496618795844,0.8,0.26737526159268643,0,None,i7185,6,0.06718801630135474 1727305409,1727305419,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 75 recent_samples_proportion 0.1 threshold 0.7136089650857572,75,0.1,0.7136089650857572,0.2842273378125344,0,None,i7185,7,0.0002169508723093919 1727305429,1727305438,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 379 recent_samples_proportion 1 threshold 0.7452452135017557,379,1,0.7452452135017557,0.2804824319859015,0,None,i7185,6,0.013520211477034916 1727305429,1727305439,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 304 recent_samples_proportion 0.9943621787705159 threshold 0.7856401641048572,304,0.9943621787705159,0.7856401641048572,0.28554906928075774,0,None,i7185,6,0.024507104306641714 1727305715,1727305724,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 323 recent_samples_proportion 0.8378178519606256 threshold 0.8,323,0.8378178519606256,0.8,0.2793809890957154,0,None,i7185,6,0.02759114439916288 1727305729,1727305738,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 311 recent_samples_proportion 0.8787700233843965 threshold 0.8,311,0.8787700233843965,0.8,0.2834563277894041,0,None,i7185,6,0.012776737526159265 1727305745,1727305754,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 353 recent_samples_proportion 0.8740534367372644 threshold 0.8,353,0.8740534367372644,0.8,0.2767375261592686,0,None,i7185,6,0.02891287586738628 1727305750,1727305759,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 319 recent_samples_proportion 0.9265353129204026 threshold 0.8,319,0.9265353129204026,0.8,0.27855490692807583,0,None,i7185,6,0.02800418548298267 1727305769,1727305778,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 338 recent_samples_proportion 0.8087114149770911 threshold 0.8,338,0.8087114149770911,0.8,0.27739839189338034,0,None,i7185,6,0.019054962000220277 1727305775,1727305784,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 314 recent_samples_proportion 0.766525375307474 threshold 0.8,314,0.766525375307474,0.8,0.27838969049454787,0,None,i7185,6,0.0561735873994933 1727305789,1727305798,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 347 recent_samples_proportion 1 threshold 0.8,347,1,0.8,0.27398391893380325,0,None,i7185,6,0.03028967948011896 1727305805,1727305814,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 343 recent_samples_proportion 0.6478475158368379 threshold 0.8,343,0.6478475158368379,0.8,0.2912215001652164,0,None,i7185,6,0.010835444432206187 1727305805,1727305814,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 317 recent_samples_proportion 1 threshold 0.8,317,1,0.8,0.28026214340786426,0,None,i7185,6,0.027150567243088453 1727305829,1727305839,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 335 recent_samples_proportion 0.9061829126977923 threshold 0.8,335,0.9061829126977923,0.8,0.27651723758123137,0,None,i7185,6,0.0290230201564049 1727305829,1727305839,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 290 recent_samples_proportion 1 threshold 0.7973690972960632,290,1,0.7973690972960632,0.27332305319969163,0,None,i7185,6,0.020413408231449843 1727305849,1727305858,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 315 recent_samples_proportion 0.7672121502066419 threshold 0.8,315,0.7672121502066419,0.8,0.2798215662517899,0,None,i7185,6,0.05474171164225128 1727305849,1727305859,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 311 recent_samples_proportion 0.8787472178876707 threshold 0.8,311,0.8787472178876707,0.8,0.2834563277894041,0,None,i7185,6,0.012776737526159265 1727305865,1727305876,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 356 recent_samples_proportion 0.771509734929297 threshold 0.8,356,0.771509734929297,0.8,0.2685317766273819,0,None,i7186,8,0.06603150126665924 1727305890,1727305901,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 343 recent_samples_proportion 0.8743185923300218 threshold 0.8,343,0.8743185923300218,0.8,0.2641810772111466,0,None,i7186,7,0.07038220068289458 1727305890,1727305901,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 312 recent_samples_proportion 0.6680466779480094 threshold 0.8,312,0.6680466779480094,0.8,0.29705914748320295,0,None,i7186,8,0.0053577329158340315 1727305895,1727305906,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 358 recent_samples_proportion 1 threshold 0.8,358,1,0.8,0.2705694459742263,0,None,i7186,7,0.06399383191981489 1727306169,1727306179,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 377 recent_samples_proportion 1 threshold 0.8,377,1,0.8,0.2645115100782024,0,None,i7185,6,0 1727306190,1727306199,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 370 recent_samples_proportion 0.9143781862769549 threshold 0.8,370,0.9143781862769549,0.8,0.2645115100782024,0,None,i7185,6,0 1727306210,1727306219,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 1 threshold 0.8,500,1,0.8,0.285769357858795,0,None,i7185,6,0.016264640011748727 1727306210,1727306219,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 368 recent_samples_proportion 1 threshold 0.8,368,1,0.8,0.26583324154642585,0,None,i7185,6,0.06873003634761532 1727306225,1727306234,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 412 recent_samples_proportion 1 threshold 0.8,412,1,0.8,0.2763520211477035,0,None,i7185,6,0.05821125674633765 1727306230,1727306239,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 380 recent_samples_proportion 0.871642830438249 threshold 0.8,380,0.871642830438249,0.8,0.2852737085582112,0,None,i7185,6,0.016429856445276652 1727306250,1727306259,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 383 recent_samples_proportion 0.8730602946839751 threshold 0.8,383,0.8730602946839751,0.8,0.26390571648860006,0,None,i7185,6,0.0706575614054411 1727306270,1727306279,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 373 recent_samples_proportion 0.9495632834637744 threshold 0.8,373,0.9495632834637744,0.8,0.2693027866505122,0,None,i7185,6,0.032630245621764475 1727306271,1727306280,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 1 threshold 0.8,500,1,0.8,0.285769357858795,0,None,i7185,6,0.016264640011748727 1727306285,1727306295,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 383 recent_samples_proportion 0.9296274974342466 threshold 0.8,383,0.9296274974342466,0.8,0.268917281638947,0,None,i7185,6,0.06564599625509415 1727306290,1727306299,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 358 recent_samples_proportion 0.9474361913572906 threshold 0.8,358,0.9474361913572906,0.8,0.2641810772111466,0,None,i7185,6,0.07038220068289458 1727306310,1727306319,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 372 recent_samples_proportion 0.7687120870633253 threshold 0.7990206128806121,372,0.7687120870633253,0.7990206128806121,0.2771781033153431,0,None,i7185,6,0.05738517457869807 1727306330,1727306339,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 370 recent_samples_proportion 0.9144522244693422 threshold 0.7999980376030664,370,0.9144522244693422,0.7999980376030664,0.2645115100782024,0,None,i7185,6,0 1727306330,1727306339,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 250 recent_samples_proportion 1 threshold 0.8,250,1,0.8,0.29078092300914193,0,None,i7185,6,0.005472794360612404 1727306345,1727306355,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 342 recent_samples_proportion 1 threshold 0.8,342,1,0.8,0.2640158607776186,0,None,i7185,6,0.07054741711642254 1727306350,1727306359,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 380 recent_samples_proportion 0.9653192401073033 threshold 0.798559567634888,380,0.9653192401073033,0.798559567634888,0.26693468443661195,0,None,i7185,6,0.06762859345742922 1727306664,1727306715,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 373 recent_samples_proportion 0.9776487295411798 threshold 0.8,373,0.9776487295411798,0.8,0.2860997907258509,0,None,i7184,7,0.01615449572273009 1727306664,1727306715,51,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 365 recent_samples_proportion 0.9738635765833148 threshold 0.8,365,0.9738635765833148,0.8,0.2696882916620773,0,None,i7184,7,0.02162499541065462 1727306670,1727306715,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 365 recent_samples_proportion 0.9555197981116239 threshold 0.8,365,0.9555197981116239,0.8,0.2776186804714176,0,None,i7184,7,0.028472298711311794 1727306690,1727306715,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 382 recent_samples_proportion 1 threshold 0.8,382,1,0.8,0.27580129970261047,0,None,i7184,7,0.0587619781914307 1727306690,1727306715,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 303 recent_samples_proportion 0.9595539493220087 threshold 0.8,303,0.9595539493220087,0.8,0.26583324154642585,0,None,i7184,7,0.03436501817380766 1727306670,1727306715,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 364 recent_samples_proportion 1 threshold 0.8,364,1,0.8,0.27365348606674744,0,None,i7184,7,0.020303263942431243 1727306706,1727306716,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 365 recent_samples_proportion 0.9383164471054999 threshold 0.8,365,0.9383164471054999,0.8,0.2645115100782024,0,None,i7184,7,0 1727306710,1727306719,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 372 recent_samples_proportion 1 threshold 0.8,372,1,0.8,0.2659433858354444,0,None,i7185,6,0.03430994602929838 1727306730,1727306739,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 387 recent_samples_proportion 0.9695191658441873 threshold 0.8,387,0.9695191658441873,0.8,0.27767375261592686,0,None,i7185,6,0.028444762639057153 1727306750,1727306760,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 396 recent_samples_proportion 0.9337790049082417 threshold 0.8,396,0.9337790049082417,0.8,0.2671549730146492,0,None,i7185,7,0.03370415243969599 1727306780,1727306790,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 416 recent_samples_proportion 0.8180875875394323 threshold 0.8,416,0.8180875875394323,0.8,0.2760215882806476,0,None,i7185,7,0.029270844806696783 1727306780,1727306790,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 361 recent_samples_proportion 1 threshold 0.8,361,1,0.8,0.2794360612402247,0,None,i7185,7,0.02756360832690824 1727306790,1727306800,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 378 recent_samples_proportion 0.9661088923499676 threshold 0.8,378,0.9661088923499676,0.8,0.2797114219627712,0,None,i7185,7,0.027425927965634977 1727306796,1727306806,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 392 recent_samples_proportion 1 threshold 0.8,392,1,0.8,0.2645115100782024,0,None,i7185,7,0 1727306810,1727306820,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 378 recent_samples_proportion 0.9044009212223872 threshold 0.8,378,0.9044009212223872,0.8,0.28224474061019933,0,None,i7185,7,0.02615926864192092 1727306826,1727306837,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 360 recent_samples_proportion 0.9776746843515107 threshold 0.8,360,0.9776746843515107,0.8,0.2690274259279656,0,None,i7185,7,0.06553585196607559 1727306850,1727306860,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 374 recent_samples_proportion 1 threshold 0.8,374,1,0.8,0.2723317545985241,0,None,i7185,7,0.031115761647758533 1727306850,1727306860,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 354 recent_samples_proportion 1 threshold 0.8,354,1,0.8,0.2690274259279656,0,None,i7185,7,0.06553585196607559 1727306870,1727306880,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 338 recent_samples_proportion 0.49785629293149514 threshold 0.8,338,0.49785629293149514,0.8,0.2835113999339134,0,None,i7185,7,0.003910122260160811 1727307219,1727307236,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 396 recent_samples_proportion 0.965560588305434 threshold 0.8,396,0.965560588305434,0.8,0.2786099790725851,0,None,i7182,7,0.055953298821456054 1727307219,1727307238,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 354 recent_samples_proportion 0.9733040405905693 threshold 0.8,354,0.9733040405905693,0.8,0.2782244740610199,0,None,i7182,9,0.056338803833021256 1727307230,1727307240,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 360 recent_samples_proportion 0.9253601158926865 threshold 0.8,360,0.9253601158926865,0.8,0.2838969049454786,0,None,i7182,7,0.01688879098285419 1727307230,1727307240,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 448 recent_samples_proportion 0.6772223005920648 threshold 0.8,448,0.6772223005920648,0.8,0.27442449608987773,0,None,i7182,7,0.06013878180416343 1727307248,1727307261,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.6180521959657126 threshold 0.8,500,0.6180521959657126,0.8,0.27321290891067296,0,None,i7182,9,0.030675184491684104 1727307270,1727307281,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 352 recent_samples_proportion 0.9520812146745636 threshold 0.8,352,0.9520812146745636,0.8,0.2748650732459522,0,None,i7182,7,0.05969820464808895 1727307271,1727307284,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 398 recent_samples_proportion 0.7988187295323749 threshold 0.7811316395697014,398,0.7988187295323749,0.7811316395697014,0.29105628373168846,0,None,i7182,9,0.014502331387450903 1727307291,1727307304,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 371 recent_samples_proportion 0.988840638652387 threshold 0.7878004223034349,371,0.988840638652387,0.7878004223034349,0.28516356426919265,0,None,i7182,9,0.016466571208282838 1727307307,1727307317,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 354 recent_samples_proportion 1 threshold 0.7811227178800433,354,1,0.7811227178800433,0.26886220949443773,0,None,i7182,7,0.03285053419980172 1727307311,1727307324,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 316 recent_samples_proportion 0.9889568602055426 threshold 0.7954282445239198,316,0.9889568602055426,0.7954282445239198,0.26996365238462383,0,None,i7182,9,0.06459962550941734 1727307331,1727307344,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 403 recent_samples_proportion 0.8498062872915004 threshold 0.8,403,0.8498062872915004,0.8,0.2767375261592686,0,None,i7182,9,0.02891287586738628 1727307351,1727307364,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 351 recent_samples_proportion 1 threshold 0.8,351,1,0.8,0.27883026765062235,0,None,i7182,9,0.05573301024341881 1727307367,1727307377,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 361 recent_samples_proportion 0.9571842768807285 threshold 0.8,361,0.9571842768807285,0.8,0.275195506113008,0,None,i7182,6,0.05936777178103314 1727307367,1727307377,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 352 recent_samples_proportion 1 threshold 0.7936649627699501,352,1,0.7936649627699501,0.2786650512170944,0,None,i7182,7,0.027949113338473386 1727307390,1727307400,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 361 recent_samples_proportion 0.976879885690499 threshold 0.7864497355105384,361,0.976879885690499,0.7864497355105384,0.269082498072475,0,None,i7185,6,0.032740389910783096 1727307397,1727307406,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 417 recent_samples_proportion 0.8137592954523483 threshold 0.7991843879154653,417,0.8137592954523483,0.7991843879154653,0.27921577266218744,0,None,i7185,6,0.02767375261592686 1727307410,1727307419,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 365 recent_samples_proportion 1 threshold 0.7839300651149583,365,1,0.7839300651149583,0.2874765943385835,0,None,i7185,6,0.01177167088886441 1727307427,1727307437,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 440 recent_samples_proportion 0.9279222192173228 threshold 0.8,440,0.9279222192173228,0.8,0.27662738187025004,0,None,i7185,6,0.05793589602379112 1727307848,1727307857,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.7447863750185132 threshold 0.8,500,0.7447863750185132,0.8,0.2737085582112567,0,None,i7185,6,0.03042735984139222 1727307871,1727307881,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 476 recent_samples_proportion 0.738024596849859 threshold 0.8,476,0.738024596849859,0.8,0.28659544002643467,0,None,i7185,7,0.02398391893380325 1727307871,1727307881,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 484 recent_samples_proportion 0.5420157985756876 threshold 0.8,484,0.5420157985756876,0.8,0.2839519770899879,0,None,i7185,7,0.025305650402026647 1727307878,1727307887,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 469 recent_samples_proportion 0.5700479336448288 threshold 0.8,469,0.5700479336448288,0.8,0.2764621654367221,0,None,i7185,7,0.05810111245731908 1727307891,1727307900,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.11900131086940353 threshold 0.7310419026521323,500,0.11900131086940353,0.7310419026521323,0.2932040973675515,0,None,i7185,6,0.0012164464860732259 1727307908,1727307917,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 0.9286811748516172 threshold 0.8,349,0.9286811748516172,0.8,0.2645115100782024,0,None,i7185,6,0 1727307911,1727307920,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 490 recent_samples_proportion 0.7113394669876488 threshold 0.8,490,0.7113394669876488,0.8,0.2645115100782024,0,None,i7185,6,0 1727307931,1727307940,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 469 recent_samples_proportion 0.5700391858147831 threshold 0.7999999963824663,469,0.5700391858147831,0.7999999963824663,0.2764621654367221,0,None,i7185,6,0.05810111245731908 1727307938,1727307947,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.4490572963890549 threshold 0.8,500,0.4490572963890549,0.8,0.2980504460843705,0,None,i7185,6,0.006085471968278448 1727307951,1727307960,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.5587579182648313 threshold 0.747939360968299,500,0.5587579182648313,0.747939360968299,0.29034034585306756,0,None,i7185,6,0.007370488673495601 1727307968,1727307977,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 468 recent_samples_proportion 0.7870109876489705 threshold 0.7917830902192857,468,0.7870109876489705,0.7917830902192857,0.28775195506113005,0,None,i7185,6,0.02340566141645556 1727307968,1727307977,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 475 recent_samples_proportion 0.5462735569569134 threshold 0.7790257460049148,475,0.5462735569569134,0.7790257460049148,0.2916620773212909,0,None,i7185,6,0.008580240114550053 1727307991,1727308000,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 447 recent_samples_proportion 0.8999866094727202 threshold 0.8,447,0.8999866094727202,0.8,0.2782244740610199,0,None,i7185,6,0.028169401916510628 1727307991,1727308000,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.5420804626151443 threshold 0.6926237221928926,500,0.5420804626151443,0.6926237221928926,0.2957374160149796,0,None,i7185,6,0.0020434664146874505 1727307998,1727308007,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 452 recent_samples_proportion 0.6051951648940838 threshold 0.7820810642538012,452,0.6051951648940838,0.7820810642538012,0.29623306531556337,0,None,i7185,6,0.007666042515695559 1727308011,1727308021,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.7106664960695295 threshold 0.8,500,0.7106664960695295,0.8,0.28593457429232294,0,None,i7185,7,0.024314351800859113 1727308028,1727308038,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 463 recent_samples_proportion 0.4667636629671873 threshold 0.8,463,0.4667636629671873,0.8,0.2964533538936006,0,None,i7185,7,0.003810992400044055 1727308371,1727308382,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 344 recent_samples_proportion 0.9709656357803796 threshold 0.8,344,0.9709656357803796,0.8,0.2767375261592686,0,None,i7182,7,0.02891287586738628 1727308389,1727308398,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 1 threshold 0.5,500,1,0.5,0.2930939530785329,0,None,i7182,7,0.0011848378518716641 1727308389,1727308398,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 362 recent_samples_proportion 0.9047832476033514 threshold 0.8,362,0.9047832476033514,0.8,0.2887432536622976,0,None,i7182,7,0.022910012115871792 1727308411,1727308421,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 353 recent_samples_proportion 0.935047208092618 threshold 0.7816195657528227,353,0.935047208092618,0.7816195657528227,0.28477805925762745,0,None,i7182,7,0.02489260931820686 1727308419,1727308429,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.8697178560498832 threshold 0.5,500,0.8697178560498832,0.5,0.28995484084150236,0,None,i7182,7,0.0012745267729296801 1727308431,1727308441,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.8697274790163473 threshold 0.5,500,0.8697274790163473,0.5,0.28995484084150236,0,None,i7182,6,0.0012745267729296801 1727308449,1727308458,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.8082228448309712 threshold 0.6026452154643539,500,0.8082228448309712,0.6026452154643539,0.2913867165987444,0,None,i7182,6,0.0016606369728960303 1727308449,1727308458,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 402 recent_samples_proportion 0.8281451172921872 threshold 0.5,402,0.8281451172921872,0.5,0.28852296508426034,0,None,i7182,7,0.0010961979240424007 1727308471,1727308481,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 457 recent_samples_proportion 0.9741833122756753 threshold 0.5,457,0.9741833122756753,0.5,0.2890736865293535,0,None,i7182,6,0.0011970945095970437 1727308471,1727308481,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.9423511493494949 threshold 0.5031863071462517,500,0.9423511493494949,0.5031863071462517,0.29375481881264454,0,None,i7182,6,0.0011659559737541895 1727308491,1727308501,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 418 recent_samples_proportion 1 threshold 0.7348370782554987,418,1,0.7348370782554987,0.2897896244079744,0,None,i7182,7,0.014924551162022256 1727308491,1727308501,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 500 recent_samples_proportion 0.9213278023287611 threshold 0.557325846917591,500,0.9213278023287611,0.557325846917591,0.2894041193964093,0,None,i7182,7,0.0013684593484130866 1727308509,1727308520,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 254 recent_samples_proportion 0.1 threshold 0.8,254,0.1,0.8,0.2868157286044719,0,None,i7185,7,0.0007234477165086251 1727308531,1727308542,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 456 recent_samples_proportion 0.9699857268953433 threshold 0.5005674229515362,456,0.9699857268953433,0.5005674229515362,0.28879832580680687,0,None,i7185,7,0.0012368905969522784 1727308532,1727308543,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 334 recent_samples_proportion 0.5147864951876401 threshold 0.8,334,0.5147864951876401,0.8,0.29331424165657005,0,None,i7185,7,0.005156129529683889 1727308539,1727308549,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 371 recent_samples_proportion 0.8020476683249602 threshold 0.5003262668523953,371,0.8020476683249602,0.5003262668523953,0.2950214781363586,0,None,i7185,7,0.0008237874949517202 1727308552,1727308562,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 402 recent_samples_proportion 0.8281750352338761 threshold 0.5000024831168788,402,0.8281750352338761,0.5000024831168788,0.29480118955832135,0,None,i7185,7,0.000903683825811814 1727308839,1727308850,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.8,50,1,0.8,0.2876418107721115,0,None,i7182,8,0.0001810123199375676 1727308852,1727308861,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 395 recent_samples_proportion 0.8783712728197751 threshold 0.8,395,0.8783712728197751,0.8,0.2804273598413922,0,None,i7182,7,0.01804530601754965 1727308869,1727308879,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 444 recent_samples_proportion 0.8040537241922054 threshold 0.8,444,0.8040537241922054,0.8,0.2850534199801741,0,None,i7182,7,0.016503285971289028 1727308869,1727308879,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 250 recent_samples_proportion 0.9998808932616052 threshold 0.7999667757516129,250,0.9998808932616052,0.7999667757516129,0.2912215001652164,0,None,i7182,7,0.005417722216103094 1727308892,1727308902,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 393 recent_samples_proportion 0.908712312931448 threshold 0.8,393,0.908712312931448,0.8,0.27800418548298267,0,None,i7182,7,0.02827954620552925 1727308900,1727308910,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 399 recent_samples_proportion 1 threshold 0.8,399,1,0.8,0.27503028967948007,0,None,i7182,6,0.0595329882145611 1727308912,1727308922,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 119 recent_samples_proportion 0.706887094618293 threshold 0.7993834098174829,119,0.706887094618293,0.7993834098174829,0.2905055622865954,0,None,i7182,7,0.00034791608753498824 1727308929,1727308939,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 437 recent_samples_proportion 1 threshold 0.8,437,1,0.8,0.26396078863310934,0,None,i7182,6,0.07060248926093182 1727308929,1727308940,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 70 recent_samples_proportion 0.7812036322554743 threshold 0.7590733245531107,70,0.7812036322554743,0.7590733245531107,0.2849432756911554,0,None,i7182,8,0.00023301751709834206 1727308952,1727308961,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 400 recent_samples_proportion 0.7305503478593491 threshold 0.7947601424914942,400,0.7305503478593491,0.7947601424914942,0.27800418548298267,0,None,i7182,6,0.02827954620552925 1727308959,1727308970,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.8,50,1,0.8,0.28808238792818597,0,None,i7182,8,0.0001772436834782297 1727308989,1727308999,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 448 recent_samples_proportion 0.8980107235119521 threshold 0.8,448,0.8980107235119521,0.8,0.2772331754598524,0,None,i7182,7,0.028665051217094395 1727308989,1727309001,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.7407047971569053,50,1,0.7407047971569053,0.2869258728934905,0,None,i7182,8,0.0001646427653559495 1727308989,1727309016,27,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 437 recent_samples_proportion 1 threshold 0.8,437,1,0.8,0.2775636083269083,0,None,i7179,6,0.028499834783566436 1727309012,1727309022,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 385 recent_samples_proportion 0.9460595669706852 threshold 0.8,385,0.9460595669706852,0.8,0.2782244740610199,0,None,i7185,7,0.028169401916510628 1727309013,1727309022,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 434 recent_samples_proportion 0.8220468065202179 threshold 0.7984501315059014,434,0.8220468065202179,0.7984501315059014,0.2783346183500386,0,None,i7185,7,0.05622865954400258 1727309032,1727309043,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.9999928738239733 threshold 0.7407094324935194,50,0.9999928738239733,0.7407094324935194,0.28252010133274585,0,None,i7185,8,0.00018247495768752842 1727309552,1727309564,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.8,50,0.1,0.8,0.2789404119396409,0,None,i7182,9,0.00017757236338577885 1727309560,1727309570,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 387 recent_samples_proportion 1 threshold 0.8,387,1,0.8,0.2640158607776186,0,None,i7182,6,0.07054741711642254 1727309572,1727309586,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 62 recent_samples_proportion 0.11508222230668273 threshold 0.7512051965996442,62,0.11508222230668273,0.7512051965996442,0.2824099570437273,0,None,i7182,10,0.00019523696342961142 1727309591,1727309602,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 424 recent_samples_proportion 1 threshold 0.8,424,1,0.8,0.2756360832690825,0,None,i7182,8,0.029463597312479328 1727309612,1727309624,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 424 recent_samples_proportion 0.8424705980105153 threshold 0.8,424,0.8424705980105153,0.8,0.2782795462055292,0,None,i7182,8,0.05628373168851197 1727309621,1727309632,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 424 recent_samples_proportion 0.842376037895278 threshold 0.8,424,0.842376037895278,0.8,0.28521863641370193,0,None,i7182,8,0.012336160370084809 1727309633,1727309644,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 0.9840352612593478 threshold 0.8,349,0.9840352612593478,0.8,0.2645115100782024,0,None,i7182,8,0 1727309651,1727309662,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 336 recent_samples_proportion 0.933590232477291 threshold 0.8,336,0.933590232477291,0.8,0.266769468003084,0,None,i7182,8,0.06779380989095718 1727309673,1727309684,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 405 recent_samples_proportion 0.7983557869963179 threshold 0.7843548855943746,405,0.7983557869963179,0.7843548855943746,0.287201233616037,0,None,i7182,8,0.015787348092668057 1727309693,1727309704,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 421 recent_samples_proportion 1 threshold 0.8,421,1,0.8,0.2748650732459522,0,None,i7182,8,0.05969820464808895 1727309710,1727309720,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 406 recent_samples_proportion 0.8903544008477544 threshold 0.8,406,0.8903544008477544,0.8,0.27481000110144294,0,None,i7185,7,0.029876638396299116 1727309712,1727309722,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 462 recent_samples_proportion 0.4790259357723803 threshold 0.7837237595677486,462,0.4790259357723803,0.7837237595677486,0.29931710540808454,0,None,i7185,7,0.0027112440373812793 1727309732,1727309742,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 410 recent_samples_proportion 0.8026103494514361 threshold 0.7857485047490851,410,0.8026103494514361,0.7857485047490851,0.28097808128648527,0,None,i7185,6,0.017861732202518632 1727309752,1727309762,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 396 recent_samples_proportion 0.8030457108124186 threshold 0.7794271916009077,396,0.8030457108124186,0.7794271916009077,0.27811432977200135,0,None,i7185,6,0.02822447406101991 1727309770,1727309780,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 367 recent_samples_proportion 0.9866264211160019 threshold 0.8,367,0.9866264211160019,0.8,0.2737085582112567,0,None,i7185,7,0.020284906560928146 1727309792,1727309802,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 409 recent_samples_proportion 0.9624335856205138 threshold 0.8,409,0.9624335856205138,0.8,0.27767375261592686,0,None,i7185,6,0.028444762639057153 1727309792,1727309802,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 408 recent_samples_proportion 0.9821112404256003 threshold 0.8,408,0.9821112404256003,0.8,0.27883026765062235,0,None,i7185,6,0.05573301024341881 1727309812,1727309823,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 128 recent_samples_proportion 0.1 threshold 0.7995534836649687,128,0.1,0.7995534836649687,0.27855490692807583,0,None,i7185,7,0.0004226369877873341 1727310274,1727310285,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 339 recent_samples_proportion 0.9651461702273595 threshold 0.8,339,0.9651461702273595,0.8,0.26500715937878616,0,None,i7182,8,0.069556118515255 1727310293,1727310305,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 339 recent_samples_proportion 1 threshold 0.8,339,1,0.8,0.2645115100782024,0,None,i7182,8,0 1727310312,1727310323,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 341 recent_samples_proportion 0.9495851112093044 threshold 0.8,341,0.9495851112093044,0.8,0.2645115100782024,0,None,i7182,8,0 1727310333,1727310345,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 316 recent_samples_proportion 1 threshold 0.8,316,1,0.8,0.26440136578918383,0,None,i7182,8,0.07016191210485734 1727310342,1727310353,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 321 recent_samples_proportion 0.7617789606525548 threshold 0.6697171015906094,321,0.7617789606525548,0.6697171015906094,0.2924881594889305,0,None,i7182,8,0.0013572618840358287 1727310354,1727310365,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 356 recent_samples_proportion 1 threshold 0.8,356,1,0.8,0.27183610529794033,0,None,i7182,8,0.02090905753203361 1727310372,1727310383,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 387 recent_samples_proportion 0.9040232381344998 threshold 0.8,387,0.9040232381344998,0.8,0.2700187245291331,0,None,i7182,8,0.06454455336490805 1727310393,1727310405,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 329 recent_samples_proportion 0.9898501964136057 threshold 0.7961521938134409,329,0.9898501964136057,0.7961521938134409,0.2645115100782024,0,None,i7182,8,0 1727310413,1727310425,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 355 recent_samples_proportion 0.9854457672848674 threshold 0.8,355,0.9854457672848674,0.8,0.27580129970261047,0,None,i7182,8,0.0587619781914307 1727310432,1727310443,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 356 recent_samples_proportion 0.961258130087331 threshold 0.8,356,0.961258130087331,0.8,0.2645115100782024,0,None,i7182,8,0 1727310453,1727310465,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 345 recent_samples_proportion 1 threshold 0.8,345,1,0.8,0.27816940191651063,0,None,i7182,8,0.05639387597753054 1727310453,1727310465,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 366 recent_samples_proportion 1 threshold 0.8,366,1,0.8,0.2786099790725851,0,None,i7182,8,0.027976649410728027 1727310474,1727310485,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 340 recent_samples_proportion 1 threshold 0.8,340,1,0.8,0.26621874655799094,0,None,i7182,8,0.034172265668025115 1727310492,1727310503,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 343 recent_samples_proportion 1 threshold 0.8,343,1,0.8,0.264070932922128,0,None,i7182,8,0.07049234497191315 1727310513,1727310525,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 371 recent_samples_proportion 0.9323493070091854 threshold 0.8,371,0.9323493070091854,0.8,0.2777838969049454,0,None,i7182,8,0.01892646032969858 1727310536,1727310546,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 343 recent_samples_proportion 1 threshold 0.7845156124355241,343,1,0.7845156124355241,0.27585637184711975,0,None,i7184,6,0.029353453023460707 1727310551,1727310561,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 338 recent_samples_proportion 0.9902922074681244 threshold 0.8,338,0.9902922074681244,0.8,0.27541579469104527,0,None,i7184,6,0.02957374160149795 1727311113,1727311123,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 341 recent_samples_proportion 0.9132564877770315 threshold 0.8,341,0.9132564877770315,0.8,0.2645115100782024,0,None,i7184,6,0 1727311122,1727311132,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 330 recent_samples_proportion 1 threshold 0.8,330,1,0.8,0.27618680471417556,0,None,i7185,6,0.029188236589932803 1727311152,1727311162,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 333 recent_samples_proportion 1 threshold 0.8,333,1,0.8,0.2798215662517899,0,None,i7185,6,0.02737085582112564 1727311173,1727311182,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 332 recent_samples_proportion 0.981558225651133 threshold 0.8,332,0.981558225651133,0.8,0.26836656019385396,0,None,i7185,6,0.0661967177001872 1727311173,1727311183,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 0.9488557595337836 threshold 0.8,349,0.9488557595337836,0.8,0.27503028967948007,0,None,i7185,6,0.0595329882145611 1727311193,1727311203,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 348 recent_samples_proportion 0.9610625904507011 threshold 0.8,348,0.9610625904507011,0.8,0.27701288688181513,0,None,i7185,6,0.05755039101222603 1727311212,1727311222,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 1 threshold 0.8,349,1,0.8,0.27436942394536845,0,None,i7185,6,0.030096926974336358 1727311233,1727311243,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 337 recent_samples_proportion 1 threshold 0.8,337,1,0.8,0.2645115100782024,0,None,i7185,6,0 1727311242,1727311252,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 303 recent_samples_proportion 0.9586835114894461 threshold 0.8,303,0.9586835114894461,0.8,0.26583324154642585,0,None,i7185,7,0.03436501817380766 1727311272,1727311282,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 363 recent_samples_proportion 0.866583358738277 threshold 0.8,363,0.866583358738277,0.8,0.2771230311708338,0,None,i7185,7,0.028720123361603678 1727311273,1727311283,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 335 recent_samples_proportion 1 threshold 0.7874088434802418,335,1,0.7874088434802418,0.2748650732459522,0,None,i7185,6,0.029849102324044474 1727311293,1727311303,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 352 recent_samples_proportion 1 threshold 0.7936704385106512,352,1,0.7936704385106512,0.2786650512170944,0,None,i7185,6,0.027949113338473386 1727311313,1727311323,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 343 recent_samples_proportion 0.9836739361206841 threshold 0.8,343,0.9836739361206841,0.8,0.2645115100782024,0,None,i7185,6,0 1727311333,1727311342,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 339 recent_samples_proportion 0.9813892781960678 threshold 0.8,339,0.9813892781960678,0.8,0.2786099790725851,0,None,i7185,6,0.027976649410728027 1727311353,1727311363,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 310 recent_samples_proportion 1 threshold 0.7987039912208949,310,1,0.7987039912208949,0.2745346403788963,0,None,i7185,6,0.030014318757572434 1727311363,1727311372,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 363 recent_samples_proportion 0.8748711315455477 threshold 0.8,363,0.8748711315455477,0.8,0.27475492895693354,0,None,i7185,6,0.059808348937107625 1727311934,1727311946,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 345 recent_samples_proportion 0.947390430355685 threshold 0.8,345,0.947390430355685,0.8,0.2778389690494548,0,None,i7182,8,0.028362154422293173 1727311954,1727311968,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.1 threshold 0.6199751471751533,50,0.1,0.6199751471751533,0.2843374821015531,0,None,i7182,11,0.00014452458732500245 1727311964,1727311976,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 337 recent_samples_proportion 1 threshold 0.7771126130275674,337,1,0.7771126130275674,0.28742152219407424,0,None,i7182,8,0.023570877849983463 1727311974,1727311986,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 321 recent_samples_proportion 0.9075151876989018 threshold 0.7549755372780886,321,0.9075151876989018,0.7549755372780886,0.29221279876638395,0,None,i7182,8,0.007058413187942869 1727311994,1727312005,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 329 recent_samples_proportion 0.9849367004521299 threshold 0.8,329,0.9849367004521299,0.8,0.2645115100782024,0,None,i7182,8,0 1727312024,1727312036,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 321 recent_samples_proportion 1 threshold 0.8,321,1,0.8,0.2789954840841502,0,None,i7182,8,0.027783896904945482 1727312034,1727312046,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 0.9029594808716109 threshold 0.8,349,0.9029594808716109,0.8,0.2768476704482873,0,None,i7182,8,0.05771560744575388 1727312054,1727312067,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 334 recent_samples_proportion 0.9613907184353995 threshold 0.8,334,0.9613907184353995,0.8,0.276076660425157,0,None,i7182,8,0.029243308734442086 1727312074,1727312086,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 352 recent_samples_proportion 1 threshold 0.8,352,1,0.8,0.2786650512170944,0,None,i7182,8,0.027949113338473386 1727312094,1727312106,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 328 recent_samples_proportion 0.97211992243142 threshold 0.8,328,0.97211992243142,0.8,0.2822998127547087,0,None,i7182,8,0.02613173256966622 1727312114,1727312126,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 0.969779718485501 threshold 0.8,349,0.969779718485501,0.8,0.2645115100782024,0,None,i7182,8,0 1727312134,1727312146,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 358 recent_samples_proportion 0.9721773049224628 threshold 0.8,358,0.9721773049224628,0.8,0.2771781033153431,0,None,i7182,8,0.05738517457869807 1727312144,1727312157,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 166 recent_samples_proportion 0.17105340360676607 threshold 0.623142828047839,166,0.17105340360676607,0.623142828047839,0.28224474061019933,0,None,i7182,9,0.0004914929123189112 1727312174,1727312186,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 341 recent_samples_proportion 0.991085614532081 threshold 0.8,341,0.991085614532081,0.8,0.2645115100782024,0,None,i7182,8,0 1727312194,1727312206,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 407 recent_samples_proportion 1 threshold 0.8,407,1,0.8,0.2786099790725851,0,None,i7182,8,0.027976649410728027 1727312194,1727312207,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 166 recent_samples_proportion 0.1711497501623232 threshold 0.6231594771467047,166,0.1711497501623232,0.6231594771467047,0.2771781033153431,0,None,i7182,9,0.0005342512710342133 1727312214,1727312223,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 322 recent_samples_proportion 0.9627435763662386 threshold 0.8,322,0.9627435763662386,0.8,0.2651723758123141,0,None,i7184,7,0.06939090208172705 1727312913,1727312925,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 358 recent_samples_proportion 0.931070939664085 threshold 0.8,358,0.931070939664085,0.8,0.2752505782575173,0,None,i7182,8,0.02965634981826193 1727312925,1727312937,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 360 recent_samples_proportion 0.9869217479657699 threshold 0.8,360,0.9869217479657699,0.8,0.27332305319969163,0,None,i7182,8,0.020413408231449843 1727312945,1727312957,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 354 recent_samples_proportion 0.9221743705402579 threshold 0.8,354,0.9221743705402579,0.8,0.2645115100782024,0,None,i7182,8,0 1727312956,1727312967,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 396 recent_samples_proportion 0.9604268220404532 threshold 0.8,396,0.9604268220404532,0.8,0.2755810111245732,0,None,i7182,8,0.05898226676946794 1727312985,1727312997,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 343 recent_samples_proportion 0.9595366677585405 threshold 0.8,343,0.9595366677585405,0.8,0.2778940411939641,0,None,i7182,8,0.056669236700077064 1727313005,1727313017,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 398 recent_samples_proportion 0.940836702304849 threshold 0.798559891419931,398,0.940836702304849,0.798559891419931,0.2890186143848441,0,None,i7182,8,0.02277233175459853 1727313005,1727313017,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 353 recent_samples_proportion 0.9929459982757209 threshold 0.8,353,0.9929459982757209,0.8,0.28103315343099455,0,None,i7182,8,0.026765062231523307 1727313025,1727313036,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 338 recent_samples_proportion 0.9986099854367153 threshold 0.8,338,0.9986099854367153,0.8,0.2645115100782024,0,None,i7182,8,0 1727313045,1727313057,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 257 recent_samples_proportion 0.1 threshold 0.6073589538057764,257,0.1,0.6073589538057764,0.29105628373168846,0,None,i7182,8,0.0006305361472804741 1727313065,1727313077,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 336 recent_samples_proportion 0.9711263496581259 threshold 0.8,336,0.9711263496581259,0.8,0.2670999008701399,0,None,i7182,8,0.06746337702390126 1727313085,1727313097,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 359 recent_samples_proportion 0.8837463143465838 threshold 0.8,359,0.8837463143465838,0.8,0.2783346183500386,0,None,i7182,8,0.05622865954400258 1727313105,1727313117,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 353 recent_samples_proportion 1 threshold 0.7575238712657182,353,1,0.7575238712657182,0.28670558431545323,0,None,i7182,8,0.011964423394646984 1727313125,1727313137,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 328 recent_samples_proportion 1 threshold 0.8,328,1,0.8,0.26462165436722107,0,None,i7182,8,0.0699416235268201 1727313145,1727313157,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 353 recent_samples_proportion 1 threshold 0.8,353,1,0.8,0.2700187245291331,0,None,i7182,8,0.06454455336490805 1727313165,1727313177,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 333 recent_samples_proportion 0.9285578712602343 threshold 0.8,333,0.9285578712602343,0.8,0.2761317325696663,0,None,i7182,8,0.029215772662187445 1727313185,1727313197,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 371 recent_samples_proportion 0.8781654624289321 threshold 0.8,371,0.8781654624289321,0.8,0.27805925762749206,0,None,i7182,8,0.028252010133274552 1727313205,1727313217,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 353 recent_samples_proportion 0.9626481128799373 threshold 0.8,353,0.9626481128799373,0.8,0.2641810772111466,0,None,i7182,8,0.07038220068289458 1727313225,1727313237,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 403 recent_samples_proportion 1 threshold 0.8,403,1,0.8,0.2764070932922128,0,None,i7182,8,0.029078092300914182 1727313946,1727313958,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 413 recent_samples_proportion 0.867455724098971 threshold 0.7889652554395516,413,0.867455724098971,0.7889652554395516,0.2859896464368322,0,None,i7182,8,0.012143407864302236 1727313965,1727313978,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 331 recent_samples_proportion 0.9591042067693121 threshold 0.8,331,0.9591042067693121,0.8,0.2779491133384734,0,None,i7182,8,0.05661416455556778 1727313978,1727313990,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 346 recent_samples_proportion 0.9796117697282581 threshold 0.8,346,0.9796117697282581,0.8,0.2645115100782024,0,None,i7182,8,0 1727314005,1727314017,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 359 recent_samples_proportion 0.9598844448063378 threshold 0.7999999319588607,359,0.9598844448063378,0.7999999319588607,0.27816940191651063,0,None,i7182,8,0.05639387597753054 1727314085,1727314097,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 332 recent_samples_proportion 1 threshold 0.8,332,1,0.8,0.27816940191651063,0,None,i7186,7,0.05639387597753054 1727314065,1727314097,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 351 recent_samples_proportion 0.98300845871152 threshold 0.8,351,0.98300845871152,0.8,0.2783346183500386,0,None,i7186,7,0.05622865954400258 1727314065,1727314097,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 327 recent_samples_proportion 0.9426537049106349 threshold 0.8,327,0.9426537049106349,0.8,0.2738187025002754,0,None,i7186,8,0.030372287696882883 1727314043,1727314097,54,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 361 recent_samples_proportion 0.9559267634344534 threshold 0.7999999490351966,361,0.9559267634344534,0.7999999490351966,0.2786650512170944,0,None,i7186,8,0.027949113338473386 1727314043,1727314097,54,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 318 recent_samples_proportion 1 threshold 0.8,318,1,0.8,0.2783346183500386,0,None,i7186,9,0.05622865954400258 1727314097,1727314108,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 344 recent_samples_proportion 1 threshold 0.8,344,1,0.8,0.2645115100782024,0,None,i7186,7,0 1727314126,1727314138,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 341 recent_samples_proportion 1 threshold 0.8,341,1,0.8,0.26588831369093513,0,None,i7186,8,0.03433748210155302 1727314146,1727314160,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2887432536622976,0,None,i7186,11,0.00012881281258109135 1727314166,1727314178,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 334 recent_samples_proportion 0.9864970418243464 threshold 0.8,334,0.9864970418243464,0.8,0.2645115100782024,0,None,i7186,8,0 1727314186,1727314198,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 370 recent_samples_proportion 0.7168329780999813 threshold 0.7999405665709738,370,0.7168329780999813,0.7999405665709738,0.28444762639057164,0,None,i7186,8,0.012528912875867382 1727314206,1727314218,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 335 recent_samples_proportion 0.9922813996006861 threshold 0.8,335,0.9922813996006861,0.8,0.2748650732459522,0,None,i7186,8,0.05969820464808895 1727314229,1727314241,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 340 recent_samples_proportion 0.9350734682179835 threshold 0.8,340,0.9350734682179835,0.8,0.2783346183500386,0,None,i7186,8,0.05622865954400258 1727314246,1727314261,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 1 threshold 0.5,50,1,0.5,0.2883577486507325,0,None,i7186,11,0.00013063988825361201 1727314879,1727314891,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 346 recent_samples_proportion 1 threshold 0.8,346,1,0.8,0.2782244740610199,0,None,i7182,8,0.056338803833021256 1727314906,1727314918,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 413 recent_samples_proportion 0.8307260480709374 threshold 0.7944189318455757,413,0.8307260480709374,0.7944189318455757,0.2778940411939641,0,None,i7182,8,0.014167309175019266 1727314926,1727314938,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 289 recent_samples_proportion 0.8077648662030703 threshold 0.799631746672094,289,0.8077648662030703,0.799631746672094,0.28923890296288135,0,None,i7182,9,0.015108124977053272 1727314939,1727314951,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 378 recent_samples_proportion 0.8936507469974458 threshold 0.7977823247201741,378,0.8936507469974458,0.7977823247201741,0.2645115100782024,0,None,i7182,8,0 1727314966,1727314978,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 485 recent_samples_proportion 0.37707371423020963 threshold 0.7585610835812986,485,0.37707371423020963,0.7585610835812986,0.29182729375481886,0,None,i7182,8,0.003052570295658736 1727314986,1727314998,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 373 recent_samples_proportion 0.8367875789292156 threshold 0.6833505544811488,373,0.8367875789292156,0.6833505544811488,0.30262143407864306,0,None,i7182,8,0.0026618203179498423 1727314998,1727315008,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 324 recent_samples_proportion 0.7767172382213176 threshold 0.7884013079106809,324,0.7767172382213176,0.7884013079106809,0.2898446965524838,0,None,i7186,6,0.011179645335389343 1727315026,1727315037,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 68 recent_samples_proportion 0.2274668151512742 threshold 0.6460228732787072,68,0.2274668151512742,0.6460228732787072,0.28257517347725525,0,None,i7186,8,0.00020542784062995507 1727315046,1727315056,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 185 recent_samples_proportion 0.5261016040109098 threshold 0.6409387622959912,185,0.5261016040109098,0.6409387622959912,0.2749752175349708,0,None,i7186,7,0.0006315720402237568 1727315058,1727315069,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 180 recent_samples_proportion 0.9190395451150835 threshold 0.6422561555169524,180,0.9190395451150835,0.6422561555169524,0.27255204317656134,0,None,i7186,7,0.0006241509711054806 1727315086,1727315096,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 135 recent_samples_proportion 0.9883733875118196 threshold 0.6170670034363865,135,0.9883733875118196,0.6170670034363865,0.28097808128648527,0,None,i7186,7,0.00041368145759316784 1727315106,1727315117,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 50 recent_samples_proportion 0.5543761729120271 threshold 0.5,50,0.5543761729120271,0.5,0.28769688291662077,0,None,i7186,8,0.00013327458971252332 1727315119,1727315128,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 379 recent_samples_proportion 0.5898642007261515 threshold 0.6375427532009781,379,0.5898642007261515,0.6375427532009781,0.2960678488820354,0,None,i7186,7,0.0008952425351629246 1727315146,1727315155,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 313 recent_samples_proportion 0.7563626220449805 threshold 0.7823871778324247,313,0.7563626220449805,0.7823871778324247,0.2685317766273819,0,None,i7186,6,0.03301575063332962 1727315166,1727315175,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 348 recent_samples_proportion 0.8322188868185922 threshold 0.7936255327428463,348,0.8322188868185922,0.7936255327428463,0.287201233616037,0,None,i7186,6,0.023681022139002084 1727315179,1727315188,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 381 recent_samples_proportion 0.9091820070520044 threshold 0.7613034042529763,381,0.9091820070520044,0.7613034042529763,0.2845577706795903,0,None,i7186,6,0.01000110144289017 1727315206,1727315215,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 393 recent_samples_proportion 0.9604366324841976 threshold 0.7842946810647846,393,0.9604366324841976,0.7842946810647846,0.2845026985350809,0,None,i7186,6,0.01668685978632008 1727315886,1727315896,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 350 recent_samples_proportion 1 threshold 0.8,350,1,0.8,0.2757462275581011,0,None,i7182,6,0.05881705033594009 1727315900,1727315910,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 92 recent_samples_proportion 0.8339844274334609 threshold 0.7745335248298943,92,0.8339844274334609,0.7745335248298943,0.2792708448066967,0,None,i7182,7,0.00031469796862461255 1727315926,1727315936,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 328 recent_samples_proportion 0.8210816157050431 threshold 0.7899515755474568,328,0.8210816157050431,0.7899515755474568,0.27965634981826193,0,None,i7182,7,0.018302309358593078 1727315946,1727315957,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 65 recent_samples_proportion 0.18741213735193013 threshold 0.5156999103724956,65,0.18741213735193013,0.5156999103724956,0.27883026765062235,0,None,i7182,8,0.00020484399138517184 1727315966,1727315976,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 211 recent_samples_proportion 0.5648002021946014 threshold 0.7538809604942799,211,0.5648002021946014,0.7538809604942799,0.28103315343099455,0,None,i7182,6,0.0007539454149724875 1727315987,1727315998,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 403 recent_samples_proportion 0.5403106079436839 threshold 0.6805189648643136,403,0.5403106079436839,0.6805189648643136,0.2889635422403348,0,None,i7186,8,0.001266659323714065 1727316021,1727316030,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 368 recent_samples_proportion 0.7565162055705514 threshold 0.7956829621963607,368,0.7565162055705514,0.7956829621963607,0.26952307522854935,0,None,i7186,6,0.06504020266549182 1727316046,1727316056,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 309 recent_samples_proportion 0.8377654008567333 threshold 0.7883028811775148,309,0.8377654008567333,0.7883028811775148,0.2787201233616037,0,None,i7186,7,0.027921577266218744 1727316046,1727316056,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 179 recent_samples_proportion 0.77097451640293 threshold 0.6857942318543793,179,0.77097451640293,0.6857942318543793,0.27883026765062235,0,None,i7186,7,0.0005664563435243018 1727316080,1727316090,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 497 recent_samples_proportion 0.43402008274570114 threshold 0.5160201963037252,497,0.43402008274570114,0.5160201963037252,0.2891287586738628,0,None,i7182,7,0.0012620699783382884 1727316107,1727316116,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 316 recent_samples_proportion 0.6735102819278836 threshold 0.796794812195003,316,0.6735102819278836,0.796794812195003,0.3001982597202335,0,None,i7182,6,0.004909288310543951 1727316127,1727316136,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 375 recent_samples_proportion 0.8343791094608605 threshold 0.7963531375862658,375,0.8343791094608605,0.7963531375862658,0.26737526159268643,0,None,i7182,6,0.06718801630135474 1727316140,1727316150,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 118 recent_samples_proportion 0.9966679722070694 threshold 0.6411222022026777,118,0.9966679722070694,0.6411222022026777,0.28185923559863424,0,None,i7182,7,0.00036701925676482295 1727316167,1727316177,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 86 recent_samples_proportion 0.3346265437081456 threshold 0.7510330026969314,86,0.3346265437081456,0.7510330026969314,0.28026214340786426,0,None,i7182,7,0.00027313558539464474 1727316187,1727316196,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 300 recent_samples_proportion 0.931941129732877 threshold 0.7724968235008419,300,0.931941129732877,0.7724968235008419,0.28775195506113005,0,None,i7182,6,0.00780188713881852 1727316207,1727316217,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 212 recent_samples_proportion 0.19769339486956597 threshold 0.7347708029672504,212,0.19769339486956597,0.7347708029672504,0.2935896023791167,0,None,i7182,7,0.0004969925236205761 1727316227,1727316236,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 330 recent_samples_proportion 0.18339539859443904 threshold 0.7199661773629487,330,0.18339539859443904,0.7199661773629487,0.29403017953519106,0,None,i7182,7,0.0007794826607471175 1727317276,1727317285,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 364 recent_samples_proportion 0.954862922019846 threshold 0.8,364,0.954862922019846,0.8,0.27739839189338034,0,None,i7186,6,0.019054962000220277 1727317295,1727317305,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 372 recent_samples_proportion 0.983954708022318 threshold 0.8,372,0.983954708022318,0.8,0.2752505782575173,0,None,i7186,7,0.02965634981826193 1727317269,1727317316,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 395 recent_samples_proportion 0.9350110406145404 threshold 0.7730922419580794,395,0.9350110406145404,0.7730922419580794,0.27508536182398946,0,None,i7185,7,0.014869479017512927 1727317269,1727317316,47,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 348 recent_samples_proportion 0.9900781761185276 threshold 0.8,348,0.9900781761185276,0.8,0.2779491133384734,0,None,i7185,7,0.05661416455556778 1727317315,1727317325,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 338 recent_samples_proportion 1 threshold 0.8,338,1,0.8,0.2645115100782024,0,None,i7185,6,0 1727317341,1727317351,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 367 recent_samples_proportion 0.9112866008300052 threshold 0.8,367,0.9112866008300052,0.8,0.2745897125234057,0,None,i7182,6,0.029986782685317737 1727317371,1727317381,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 346 recent_samples_proportion 0.9859680120364148 threshold 0.8,346,0.9859680120364148,0.8,0.2641810772111466,0,None,i7182,6,0.07038220068289458 1727317395,1727317405,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 341 recent_samples_proportion 0.9784914977775934 threshold 0.8,341,0.9784914977775934,0.8,0.27811432977200135,0,None,i7182,6,0.01881631604067994 1727317416,1727317425,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 303 recent_samples_proportion 0.9599858162554653 threshold 0.8,303,0.9599858162554653,0.8,0.26583324154642585,0,None,i7182,6,0.03436501817380766 1727317436,1727317445,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 398 recent_samples_proportion 1 threshold 0.8,398,1,0.8,0.2645115100782024,0,None,i7182,6,0 1727317462,1727317471,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 354 recent_samples_proportion 0.9375883104461984 threshold 0.8,354,0.9375883104461984,0.8,0.2749752175349708,0,None,i7182,6,0.05958806035907038 1727317492,1727317501,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 380 recent_samples_proportion 1 threshold 0.7825100782901107,380,1,0.7825100782901107,0.28114329772001323,0,None,i7182,6,0.013354995043506984 1727317516,1727317525,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 337 recent_samples_proportion 0.9459196777783654 threshold 0.8,337,0.9459196777783654,0.8,0.2774534640378896,0,None,i7182,6,0.05710981385615155 1727317536,1727317545,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 341 recent_samples_proportion 0.9667645981228281 threshold 0.8,341,0.9667645981228281,0.8,0.2645115100782024,0,None,i7182,6,0 1727317552,1727317561,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 348 recent_samples_proportion 1 threshold 0.8,348,1,0.8,0.2763520211477035,0,None,i7182,6,0.019403752248779216 1727317576,1727317585,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 315 recent_samples_proportion 1 threshold 0.8,315,1,0.8,0.2645115100782024,0,None,i7182,6,0 1727317613,1727317622,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 356 recent_samples_proportion 0.9300590764731191 threshold 0.7924095124937596,356,0.9300590764731191,0.7924095124937596,0.274644784667915,0,None,i7182,6,0.05991849322612619 1727317636,1727317645,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 345 recent_samples_proportion 0.9406349843714858 threshold 0.8,345,0.9406349843714858,0.8,0.2645115100782024,0,None,i7186,6,0 1727318693,1727318703,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 163 recent_samples_proportion 0.56163853228287 threshold 0.6654570997529612,163,0.56163853228287,0.6654570997529612,0.2890736865293535,0,None,i7182,7,0.00042706889421368325 1727318717,1727318727,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 327 recent_samples_proportion 1 threshold 0.8,327,1,0.8,0.2668245401475934,0,None,i7182,6,0.06773873774644779 1727318753,1727318763,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 309 recent_samples_proportion 1 threshold 0.8,309,1,0.8,0.2749201453904615,0,None,i7182,6,0.059643132503579666 1727318777,1727318787,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 340 recent_samples_proportion 0.9483336436785934 threshold 0.8,340,0.9483336436785934,0.8,0.2640158607776186,0,None,i7182,6,0.07054741711642254 1727318796,1727318807,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 154 recent_samples_proportion 0.5271914795344181 threshold 0.6716660475119639,154,0.5271914795344181,0.6716660475119639,0.2819693798876528,0,None,i7186,7,0.0004718343191743344 1727318815,1727318824,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 370 recent_samples_proportion 1 threshold 0.8,370,1,0.8,0.26616367441348165,0,None,i7186,7,0.034199801740279756 1727318837,1727318846,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 308 recent_samples_proportion 0.9973678454453413 threshold 0.8,308,0.9973678454453413,0.8,0.27949113338473397,0,None,i7186,6,0.0275360722546536 1727318857,1727318866,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 327 recent_samples_proportion 0.9441624664593323 threshold 0.8,327,0.9441624664593323,0.8,0.27469985681242426,0,None,i7186,6,0.05986342108161691 1727318896,1727318906,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 432 recent_samples_proportion 1 threshold 0.8,432,1,0.8,0.27734331974887105,0,None,i7186,6,0.05721995814517011 1727318917,1727318926,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 331 recent_samples_proportion 1 threshold 0.8,331,1,0.8,0.27838969049454787,0,None,i7186,6,0.0561735873994933 1727318934,1727318944,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 186 recent_samples_proportion 0.46220914197220686 threshold 0.6938329194670978,186,0.46220914197220686,0.6938329194670978,0.27921577266218744,0,None,i7186,7,0.0005864597516363455 1727318957,1727318966,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 308 recent_samples_proportion 0.9973729261822114 threshold 0.8,308,0.9973729261822114,0.8,0.27949113338473397,0,None,i7186,6,0.0275360722546536 1727318977,1727318987,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 380 recent_samples_proportion 0.92889693040137 threshold 0.8,380,0.92889693040137,0.8,0.26996365238462383,0,None,i7186,7,0.06459962550941734 1727319017,1727319027,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 375 recent_samples_proportion 1 threshold 0.8,375,1,0.8,0.273763630355766,0,None,i7182,7,0.03039982376913758 1727319024,1727319034,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 139 recent_samples_proportion 0.5338627418677909 threshold 0.6838355147972032,139,0.5338627418677909,0.6838355147972032,0.28819253221720453,0,None,i7182,7,0.00038140873635371397 1727319054,1727319063,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 149 recent_samples_proportion 0.9289590415284823 threshold 0.5943573993507107,149,0.9289590415284823,0.5943573993507107,0.2800969269743364,0,None,i7182,6,0.00047584265211989045 1727319077,1727319086,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 346 recent_samples_proportion 0.9523794979852983 threshold 0.8,346,0.9523794979852983,0.8,0.2779491133384734,0,None,i7182,7,0.05661416455556778 1727320338,1727320349,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 336 recent_samples_proportion 0.9847207998144203 threshold 0.8,336,0.9847207998144203,0.8,0.27816940191651063,0,None,i7182,6,0.05639387597753054 1727320358,1727320368,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 370 recent_samples_proportion 0.9711286354969701 threshold 0.8,370,0.9711286354969701,0.8,0.2882476043617138,0,None,i7182,6,0.015438557844109116 1727320376,1727320385,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 336 recent_samples_proportion 1 threshold 0.8,336,1,0.8,0.27481000110144294,0,None,i7182,6,0.029876638396299116 1727320398,1727320408,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 392 recent_samples_proportion 1 threshold 0.8,392,1,0.8,0.2645115100782024,0,None,i7182,6,0 1727320436,1727320446,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 348 recent_samples_proportion 0.9354292933207494 threshold 0.8,348,0.9354292933207494,0.8,0.2645115100782024,0,None,i7185,7,0 1727320458,1727320468,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 373 recent_samples_proportion 0.9609640768718206 threshold 0.8,373,0.9609640768718206,0.8,0.2801519991188457,0,None,i7185,7,0.027205639387597735 1727320478,1727320488,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 349 recent_samples_proportion 1 threshold 0.8,349,1,0.8,0.27436942394536845,0,None,i7185,6,0.030096926974336358 1727320496,1727320506,10,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 334 recent_samples_proportion 1 threshold 0.8,334,1,0.8,0.2775636083269083,0,None,i7185,7,0.018999889855710956 1727320526,1727320535,9,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 328 recent_samples_proportion 0.9881317271068946 threshold 0.8,328,0.9881317271068946,0.8,0.2645115100782024,0,None,i7182,6,0 1727320574,1727320637,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 362 recent_samples_proportion 1 threshold 0.8,362,1,0.8,0.27701288688181513,0,None,i7183,7,0.019183463670742012 1727320599,1727320637,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 331 recent_samples_proportion 0.9896217681148275 threshold 0.8,331,0.9896217681148275,0.8,0.26473179865623964,0,None,i7183,7,0.06983147923780153 1727320578,1727320637,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 240 recent_samples_proportion 0.6154232000932097 threshold 0.7936034366488457,240,0.6154232000932097,0.7936034366488457,0.28593457429232294,0,None,i7183,7,0.0012157175900429557
timestamp,ram_usage_mb,cpu_usage_percent 1727302144,475.390625,34.6 1727302144,475.421875,33.3 1727302144,475.421875,34.6 1727302144,475.421875,25.7 1727302144,475.421875,39.5 1727302144,475.421875,34.4 1727302144,475.421875,35.3 1727302192,481.39453125,35.0 1727302192,481.39453125,40.9 1727302192,481.39453125,34.0 1727302192,481.39453125,35.0 1727302194,481.39453125,34.7 1727302194,481.39453125,38.6 1727302194,481.39453125,33.0 1727302194,481.39453125,37.5 1727302196,481.3984375,34.8 1727302196,481.3984375,37.0 1727302197,481.3984375,37.1 1727302197,481.3984375,26.2 1727302200,483.55859375,35.4 1727302200,483.55859375,40.0 1727302200,483.55859375,33.3 1727302200,483.55859375,45.5 1727302203,483.55859375,35.1 1727302203,483.55859375,25.7 1727302203,483.55859375,36.9 1727302203,483.55859375,30.6 1727302205,483.57421875,35.0 1727302205,483.57421875,29.3 1727302205,483.57421875,38.0 1727302205,483.57421875,31.4 1727302207,483.57421875,34.8 1727302207,483.57421875,39.1 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1727303512,538.046875,30.2 1727303512,538.046875,37.5 1727303512,538.046875,35.7 1727303512,538.046875,26.5 1727303868,552.98828125,33.9 1727303868,552.98828125,40.0 1727303868,552.98828125,35.0 1727303868,552.98828125,37.5 1727304280,559.4375,35.0 1727304280,559.4375,38.6 1727304280,559.4375,35.7 1727304280,559.4375,26.5 1727304811,569.22265625,31.7 1727304811,569.22265625,37.8 1727304811,569.22265625,33.3 1727304811,569.22265625,37.5 1727305428,580.78125,35.0 1727305428,580.78125,42.2 1727305428,580.78125,34.4 1727305428,580.78125,40.5 1727305894,581.64453125,31.1 1727305894,581.64453125,38.0 1727305894,581.64453125,34.5 1727305894,581.64453125,27.0 1727306354,525.79296875,35.0 1727306354,525.79296875,33.3 1727306354,525.79296875,35.5 1727306354,525.79296875,26.5 1727306859,493.34375,32.1 1727306859,493.34375,37.8 1727306859,493.34375,33.2 1727306859,493.34375,41.9 1727307425,492.25,32.1 1727307425,492.25,31.1 1727307425,492.25,27.2 1727307425,492.25,20.0 1727308021,497.4765625,26.1 1727308021,497.4765625,34.1 1727308021,497.4765625,28.0 1727308021,497.4765625,22.6 1727308553,490.39453125,27.0 1727308553,490.39453125,32.5 1727308553,490.39453125,27.8 1727308553,490.39453125,25.8 1727309039,489.03125,22.1 1727309039,489.03125,28.9 1727309039,489.03125,34.6 1727309039,489.03125,41.9 1727309815,501.171875,31.5 1727309815,501.171875,21.6 1727309815,501.171875,28.5 1727309815,501.171875,33.3 1727310539,491.6875,33.5 1727310539,491.6875,32.6 1727310539,491.6875,36.0 1727310539,491.6875,27.8 1727311361,500.1640625,33.4 1727311361,500.1640625,25.0 1727311361,500.1640625,25.2 1727311361,500.1640625,30.2 1727312217,509.046875,32.9 1727312217,509.046875,27.8 1727312217,509.046875,35.5 1727312217,509.046875,35.3 1727313226,468.30078125,33.5 1727313226,468.30078125,37.8 1727313226,468.30078125,35.4 1727313226,468.30078125,41.9 1727314251,474.8828125,32.7 1727314251,474.8828125,34.5 1727314251,474.8828125,34.8 1727314251,474.8828125,37.0 1727315199,489.8671875,33.8 1727315199,489.8671875,37.8 1727315199,489.8671875,34.7 1727315199,489.8671875,25.7 1727316232,472.98046875,32.5 1727316232,472.98046875,41.3 1727316232,472.98046875,35.5 1727316232,472.98046875,26.5 1727317621,480.01171875,31.8 1727317621,480.01171875,28.9 1727317621,480.01171875,25.5 1727317621,480.01171875,30.2 1727319075,513.09765625,33.1 1727319075,513.09765625,28.2 1727319075,513.09765625,26.6 1727319075,513.09765625,19.4 1727320596,505.25390625,31.9 1727320596,505.25390625,34.7 1727320652,505.2578125,34.5 1727320652,505.2578125,41.3
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 (2024-09-26 00:09:25,680) - Starting with JobEnvironment(job_id=2213257, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-26 00:09:25,682) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/DiscriminativeDriftDetector2019_NOAAWeather/0/single_runs/2213257/2213257_submitted.pkl
Parameters: {"n_reference_samples": 279, "recent_samples_proportion": 0.8911640584468842, "threshold": 0.5169260140508414}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 279 recent_samples_proportion 0.8911640584468842 threshold 0.5169260140508414
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate
UID: 2063769
GID: 200270
Status-Change-Time: 1724762786.0
Size: 2165 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727302080.0
Last modification: 1724759185.0
Hostname: i7185
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: 1727302080.0
Last modification: 1727277242.0
Hostname: i7185
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 DiscriminativeDriftDetector2019 n_reference_samples 279 recent_samples_proportion 0.8911640584468842 threshold 0.5169260140508414
stdout:
RESULT: 0.28962440797444655
OO-Info: runtime: 6
OO-Info: lpd: 0.0007248204825741067
Result: 0.28962440797444655
EXIT_CODE: 0
submitit INFO (2024-09-26 00:09:36,915) - Job completed successfully
submitit INFO (2024-09-26 00:09:36,918) - Exiting after successful completion
Job-Infos
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background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 -0.5 15 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e6eefc' d='M0 0h1'/%3E%3Cpath stroke='%23d1e0fd' d='M1 0h1M0 1h1m3 0h2M2 3h1M2 4h1'/%3E%3Cpath stroke='%23cad8f9' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23c4d3f7' d='M3 0h1M0 3h1M0 4h1'/%3E%3Cpath stroke='%23bfd0f8' d='M4 0h2M0 5h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 0h1M0 6h1'/%3E%3Cpath stroke='%23baccf4' d='M7 0h1m6 2h1m-1 5h1m-1 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M8 0h1M0 7h1M0 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M9 0h2M0 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 0h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 0h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 0h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 0h1'/%3E%3Cpath stroke='%23e1eafe' d='M1 1h1'/%3E%3Cpath stroke='%23dae6fe' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23d4e1fc' d='M3 1h1M1 3h1M1 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M6 1h1M1 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M7 1h1M4 2h2'/%3E%3Cpath stroke='%23cad9fd' d='M8 1h1M6 2h1M3 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M9 1h2'/%3E%3Cpath stroke='%23c5d6fc' d='M11 1h1M2 11h4'/%3E%3Cpath stroke='%23c2d3fc' d='M12 1h1m-2 1h1M1 11h1m0 1h2m-2 1h2'/%3E%3Cpath stroke='%23bccefa' d='M13 1h1m-1 1h1m-1 1h1m-1 1h1M3 15h4'/%3E%3Cpath stroke='%23b9c9f3' d='M14 1h1M3 16h4'/%3E%3Cpath stroke='%23d8e3fc' d='M2 2h1'/%3E%3Cpath stroke='%23d1defd' d='M3 2h1'/%3E%3Cpath stroke='%23c9d8fc' d='M7 2h1M4 3h3M4 4h3M3 6h1m1 0h2M1 7h1M1 8h1'/%3E%3Cpath stroke='%23c5d5fc' d='M8 2h1m-8 8h5'/%3E%3Cpath stroke='%23c5d3fc' d='M9 2h2'/%3E%3Cpath stroke='%23bed0fc' d='M12 2h1M8 3h1M8 4h1m-8 8h1m-1 1h1m0 1h1m1 0h3'/%3E%3Cpath stroke='%23cddbfc' d='M3 3h1M3 4h1M1 6h2'/%3E%3Cpath stroke='%23c8d5fb' d='M7 3h1M7 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M9 3h4M9 4h4M8 5h1M7 6h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 3h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23ceddfd' d='M2 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M4 5h4M1 9h3'/%3E%3Cpath stroke='%23bacdfc' d='M9 5h2m1 0h2M1 14h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1M8 6h2m2 0h2m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%234d6185' d='M4 6h1m5 0h1M3 7h3m3 0h3M4 8h3m1 0h3M5 9h5m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1m-1 1h1'/%3E%3Cpath stroke='%23cad8fd' d='M2 7h1M2 8h2'/%3E%3Cpath stroke='%23c1d3fb' d='M6 7h2M7 8h1M4 9h1'/%3E%3Cpath stroke='%23b6cefb' d='M8 7h1m2 1h1m-2 1h3m-2 1h2'/%3E%3Cpath stroke='%23b6cdfb' d='M13 9h1m-6 6h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 9h1'/%3E%3Cpath stroke='%23b4c8f6' d='M0 10h1'/%3E%3Cpath stroke='%23bdd3fb' d='M9 10h2m-4 4h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 10h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 10h1'/%3E%3Cpath stroke='%23b1c7f6' d='M0 11h1'/%3E%3Cpath stroke='%23c3d5fd' d='M6 11h1'/%3E%3Cpath stroke='%23bad4fc' d='M8 11h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M9 11h4m-2 3h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 11h1m-3 4h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 11h1m-7 5h3'/%3E%3Cpath stroke='%23adc3f6' d='M0 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c2d5fc' d='M4 12h4m-4 1h4'/%3E%3Cpath stroke='%23b7d3fc' d='M9 12h2m-2 1h2m-3 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 12h1m-1 1h1'/%3E%3Cpath stroke='%23afcdfb' d='M12 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afcbfa' d='M13 12h1m-1 1h1'/%3E%3Cpath stroke='%23b2c8f4' d='M14 12h1m-1 1h1m-4 3h1'/%3E%3Cpath stroke='%23c1d2fb' d='M3 14h1'/%3E%3Cpath stroke='%23b6d1fb' d='M9 14h2'/%3E%3Cpath stroke='%23adc9f9' d='M13 14h1m-2 1h1'/%3E%3Cpath stroke='%23b1c6f3' d='M14 14h1m-3 2h1'/%3E%3Cpath stroke='%23abc1f4' d='M0 15h1'/%3E%3Cpath stroke='%23b7cbf9' d='M1 15h1'/%3E%3Cpath stroke='%23b9cefb' d='M2 15h1'/%3E%3Cpath stroke='%23b9cffb' d='M7 15h1'/%3E%3Cpath stroke='%23b2cdfb' d='M9 15h2'/%3E%3Cpath stroke='%23aec8f7' d='M13 15h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 15h1m-2 1h1'/%3E%3Cpath stroke='%23dbe3f8' d='M0 16h1'/%3E%3Cpath stroke='%23b7c6f1' d='M1 16h1'/%3E%3Cpath stroke='%23b8c9f2' d='M2 16h1m4 0h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 16h1'/%3E%3C/svg%3E");
background-size: 15px;
font-size: 11px;
border: none;
background-color: #fff;
box-sizing: border-box;
height: 21px;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
position: relative;
padding: 5px 32px 32px 5px;
background-position: top 50% right 2px;
background-repeat: no-repeat;
border-radius: 0;
border: 1px solid black;
}
body {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
background-color: #fafafa;
font-variant: oldstyle-nums;
text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
scroll-behavior: smooth;
text-wrap: balance;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-feature-settings: "ss02", "liga", "onum";
}
.marked_text {
background-color: yellow;
}
.time_picker_container {
font-variant: small-caps;
width: 100%;
}
.time_picker_container > input {
width: 50px;
}
#loader {
display: grid;
justify-content: center;
align-items: center;
height: 100%;
}
.no_linebreak {
line-break: auto;
}
.dark_code_bg {
background-color: #363636;
color: white;
}
.code_bg {
background-color: #C0C0C0;
}
#commands {
line-break: anywhere;
}
.color_red {
color: red;
}
.color_orange {
color: orange;
}
table > tbody > tr:nth-child(odd) {
background-color: #fafafa;
}
table > tbody > tr:nth-child(even) {
background-color: #ddd;
}
table {
border-collapse: collapse;
margin: 25px 0;
min-width: 200px;
}
th {
background-color: #4eae46;
color: #ffffff;
text-align: left;
border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
min-height: 80px;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 60px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 7h1'/%3E%3Cpath stroke='%23c5d5fc' d='M9 3h1M3 9h1m3 0h1'/%3E%3Cpath stroke='%23c5d3fc' d='M10 3h1'/%3E%3Cpath stroke='%23bed0fc' d='M12 3h1M9 4h1m-7 7h1m0 1h1'/%3E%3Cpath stroke='%23bccdfa' d='M13 3h1'/%3E%3Cpath stroke='%23baccf4' d='M14 3h1'/%3E%3Cpath stroke='%23bdcbda' d='M16 3h1'/%3E%3Cpath stroke='%23c4d4f7' d='M2 4h1'/%3E%3Cpath stroke='%23cddbfc' d='M5 4h1M3 6h1'/%3E%3Cpath stroke='%23c8d5fb' d='M8 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M10 4h3M9 5h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 4h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c2d5' d='M16 4h1'/%3E%3Cpath stroke='%23bed0f8' d='M2 5h1'/%3E%3Cpath stroke='%23ceddfd' d='M4 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M6 5h2M3 8h2'/%3E%3Cpath stroke='%234d6185' d='M8 5h1M7 6h3M6 7h5M5 8h3m1 0h3M4 9h3m3 0h3m-8 1h1m5 0h1'/%3E%3Cpath stroke='%23bacdfc' d='M10 5h1m1 0h2M3 12h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1m-2 1h1m1 0h2m-1 1h1'/%3E%3Cpath stroke='%23a8bbd4' d='M16 5h1'/%3E%3Cpath stroke='%23cddafc' d='M4 6h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1'/%3E%3Cpath stroke='%23a4b8d3' d='M16 6h1'/%3E%3Cpath stroke='%23cad8fd' d='M4 7h2'/%3E%3Cpath stroke='%23b6cefb' d='M11 7h1m0 1h1'/%3E%3Cpath stroke='%23bacbf4' d='M14 7h1'/%3E%3Cpath stroke='%23a0b5d3' d='M16 7h1m-1 1h1m-1 5h1'/%3E%3Cpath stroke='%23c1d3fb' d='M8 8h1'/%3E%3Cpath stroke='%23b6cdfb' d='M13 8h1m-5 5h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 8h1'/%3E%3Cpath stroke='%23b4c8f6' d='M2 9h1'/%3E%3Cpath stroke='%23c2d5fc' d='M8 9h1m-1 1h1m-3 1h2'/%3E%3Cpath stroke='%23bdd3fb' d='M9 9h1m-2 3h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 9h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 9h1'/%3E%3Cpath stroke='%239fb5d2' d='M16 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c7f6' d='M2 10h1'/%3E%3Cpath stroke='%23c3d5fd' d='M7 10h1'/%3E%3Cpath stroke='%23bad4fc' d='M9 10h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M10 10h1m1 0h1m-2 2h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 10h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 10h1m-6 4h2'/%3E%3Cpath stroke='%23adc3f6' d='M2 11h1'/%3E%3Cpath stroke='%23c3d3fd' d='M5 11h1'/%3E%3Cpath stroke='%23c1d5fb' d='M8 11h1'/%3E%3Cpath stroke='%23b7d3fc' d='M10 11h1m-2 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 11h1'/%3E%3Cpath stroke='%23afcefb' d='M12 11h1'/%3E%3Cpath stroke='%23aecafa' d='M13 11h1'/%3E%3Cpath stroke='%23b1c8f3' d='M14 11h1'/%3E%3Cpath stroke='%23acc2f5' d='M2 12h1'/%3E%3Cpath stroke='%23c1d2fb' d='M5 12h1'/%3E%3Cpath stroke='%23bed1fc' d='M6 12h2'/%3E%3Cpath stroke='%23b6d1fb' d='M10 12h1'/%3E%3Cpath stroke='%23afccfb' d='M12 12h1'/%3E%3Cpath stroke='%23adc9f9' d='M13 12h1m-2 1h1'/%3E%3Cpath stroke='%23b1c5f3' d='M14 12h1'/%3E%3Cpath stroke='%23aac0f3' d='M2 13h1'/%3E%3Cpath stroke='%23b7cbf9' d='M3 13h1'/%3E%3Cpath stroke='%23b9cefb' d='M4 13h1'/%3E%3Cpath stroke='%23bbcef9' d='M7 13h1'/%3E%3Cpath stroke='%23b9cffb' d='M8 13h1'/%3E%3Cpath stroke='%23b2cdfb' d='M10 13h1'/%3E%3Cpath stroke='%23b0cbf9' d='M11 13h1'/%3E%3Cpath stroke='%23aec8f7' d='M13 13h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 13h1'/%3E%3Cpath stroke='%23dbe3f8' d='M2 14h1'/%3E%3Cpath stroke='%23b7c6f1' d='M3 14h1'/%3E%3Cpath stroke='%23b8c9f2' d='M4 14h1m3 0h1'/%3E%3Cpath stroke='%23b2c8f4' d='M11 14h1'/%3E%3Cpath stroke='%23b1c6f3' d='M12 14h1'/%3E%3Cpath stroke='%23b0c4f2' d='M13 14h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 14h1'/%3E%3Cpath stroke='%23aec0d6' d='M16 14h1'/%3E%3Cpath stroke='%23c3d4e7' d='M1 15h1'/%3E%3Cpath stroke='%23aec4e5' d='M15 15h1'/%3E%3Cpath stroke='%23edf1f3' d='M1 16h1'/%3E%3Cpath stroke='%23aac0e1' d='M2 16h1'/%3E%3Cpath stroke='%2394b1d9' d='M3 16h1'/%3E%3Cpath stroke='%2388a7d8' d='M4 16h1'/%3E%3Cpath stroke='%2383a4d3' d='M5 16h1'/%3E%3Cpath stroke='%237da0d4' d='M6 16h1m3 0h3'/%3E%3Cpath stroke='%237e9fd2' d='M7 16h1'/%3E%3Cpath stroke='%237c9fd3' d='M8 16h2'/%3E%3Cpath stroke='%2382a4d6' d='M13 16h1'/%3E%3Cpath stroke='%2394b0dd' d='M14 16h1'/%3E%3Cpath stroke='%23ecf2f7' d='M15 16h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h3'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M6 6h2M3 7h1'/%3E%3Cpath 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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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}
.title-bar-controls button[aria-label=Maximize]: not(: disabled): active{
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}
.title-bar-controls button[aria-label=Restore]{
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}
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}
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stroke='%23b5381a' d='M9 4h1M4 9h1'/%3E%3Cpath stroke='%23b8391a' d='M10 4h1m-7 6h1'/%3E%3Cpath stroke='%23ba3a1b' d='M11 4h1m-8 7h2'/%3E%3Cpath stroke='%23bc3b1c' d='M12 4h1m-9 8h1'/%3E%3Cpath stroke='%23bd3c1c' d='M13 4h1m-1 1h1m-2 1h1m-7 6h1m-3 1h2'/%3E%3Cpath stroke='%23be3d1c' d='M14 4h3m-1 1h1m-1 1h1M4 14h1m-1 1h1m-1 1h2'/%3E%3Cpath stroke='%23bf3d1c' d='M17 4h3m-3 1h3m-2 1h2m-1 1h1M4 17h2m-2 1h4m-4 1h4'/%3E%3Cpath stroke='%235b1d0d' d='M1 5h1'/%3E%3Cpath stroke='%239c3016' d='M3 5h1'/%3E%3Cpath stroke='%23a43217' d='M4 5h1'/%3E%3Cpath stroke='%23b8553e' d='M5 5h1'/%3E%3Cpath stroke='%23d59485' d='M6 5h1M5 6h1'/%3E%3Cpath stroke='%23b33619' d='M7 5h1'/%3E%3Cpath stroke='%23b53719' d='M8 5h1'/%3E%3Cpath stroke='%23b8381a' d='M9 5h1M6 8h1'/%3E%3Cpath stroke='%23b9391b' d='M10 5h1'/%3E%3Cpath stroke='%23ba391b' d='M11 5h1M6 9h1m-2 1h1'/%3E%3Cpath stroke='%23bc3b1b' d='M12 5h1m-2 1h1m-6 5h1m-2 1h1'/%3E%3Cpath stroke='%23dc9887' d='M14 5h1'/%3E%3Cpath stroke='%23c85d42' d='M15 5h1M5 15h1'/%3E%3Cpath stroke='%23611f0e' d='M1 6h1'/%3E%3Cpath stroke='%23a23217' d='M3 6h1'/%3E%3Cpath stroke='%23d79585' d='M6 6h1'/%3E%3Cpath stroke='%23d89585' d='M7 6h1'/%3E%3Cpath stroke='%23b8371a' d='M8 6h1'/%3E%3Cpath stroke='%23ba391a' d='M9 6h1'/%3E%3Cpath stroke='%23bb3a1b' d='M10 6h1m-5 4h1'/%3E%3Cpath stroke='%23dd9887' d='M13 6h3m-4 1h1m-2 1h1M9 9h1m-2 2h1m-2 1h1m-2 1h1m-2 1h2'/%3E%3Cpath stroke='%23c03e1d' d='M17 6h1m-2 1h3m0 1h1m-1 1h1M7 16h1m-2 1h2m0 1h1'/%3E%3Cpath stroke='%2365200e' d='M1 7h1'/%3E%3Cpath stroke='%23902d15' d='M2 7h1'/%3E%3Cpath stroke='%23a73418' d='M3 7h1'/%3E%3Cpath stroke='%23af3518' d='M4 7h1'/%3E%3Cpath stroke='%23b43619' d='M5 7h1'/%3E%3Cpath stroke='%23d99585' d='M6 7h1'/%3E%3Cpath stroke='%23da9686' d='M7 7h1'/%3E%3Cpath stroke='%23db9686' d='M8 7h1M7 8h1'/%3E%3Cpath stroke='%23bc3a1b' d='M9 7h1M7 9h1'/%3E%3Cpath stroke='%23bd3b1b' d='M10 7h1m-4 3h1'/%3E%3Cpath stroke='%23be3c1c' d='M11 7h1m-2 1h1m-3 2h1m-2 1h1'/%3E%3Cpath stroke='%23de9987' d='M13 7h2m-3 1h2m-4 1h2m-3 1h1m-2 2h1m-2 2h1'/%3E%3Cpath stroke='%23c03f1d' d='M15 7h1m-9 8h1'/%3E%3Cpath stroke='%236a220f' d='M1 8h1'/%3E%3Cpath stroke='%23952f15' d='M2 8h1'/%3E%3Cpath stroke='%23ac3518' d='M3 8h1'/%3E%3Cpath stroke='%23b63719' d='M5 8h1'/%3E%3Cpath stroke='%23dc9786' d='M8 8h2M8 9h1'/%3E%3Cpath stroke='%23c2401d' d='M14 8h1m2 0h1m1 3h1M8 14h1m-1 2h1m-1 1h1m0 1h1m1 1h1'/%3E%3Cpath stroke='%23c2401e' d='M15 8h2m1 1h1M8 15h1'/%3E%3Cpath stroke='%23c13f1d' d='M18 8h1m0 2h1M9 19h2'/%3E%3Cpath stroke='%23702410' d='M1 9h1'/%3E%3Cpath stroke='%239b3016' d='M2 9h1'/%3E%3Cpath stroke='%23b03619' d='M3 9h1'/%3E%3Cpath stroke='%23b9381a' d='M5 9h1'/%3E%3Cpath stroke='%23df9a88' d='M12 9h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23c4421e' d='M13 9h1m2 0h2m0 1h1M9 13h1m9 1h1m-1 1h1M9 16h1m9 0h1M9 17h1m0 1h1m3 1h3'/%3E%3Cpath stroke='%23c5431e' d='M14 9h1'/%3E%3Cpath stroke='%23c5431f' d='M15 9h1m-4 1h1m5 1h1m-9 1h1m-2 2h1m-1 1h1m0 2h1m0 1h1m6 0h1'/%3E%3Cpath stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
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[
1727314251,
474.8828125,
34.5
],
[
1727314251,
474.8828125,
34.8
],
[
1727314251,
474.8828125,
37
],
[
1727315199,
489.8671875,
33.8
],
[
1727315199,
489.8671875,
37.8
],
[
1727315199,
489.8671875,
34.7
],
[
1727315199,
489.8671875,
25.7
],
[
1727316232,
472.98046875,
32.5
],
[
1727316232,
472.98046875,
41.3
],
[
1727316232,
472.98046875,
35.5
],
[
1727316232,
472.98046875,
26.5
],
[
1727317621,
480.01171875,
31.8
],
[
1727317621,
480.01171875,
28.9
],
[
1727317621,
480.01171875,
25.5
],
[
1727317621,
480.01171875,
30.2
],
[
1727319075,
513.09765625,
33.1
],
[
1727319075,
513.09765625,
28.2
],
[
1727319075,
513.09765625,
26.6
],
[
1727319075,
513.09765625,
19.4
],
[
1727320596,
505.25390625,
31.9
],
[
1727320596,
505.25390625,
34.7
],
[
1727320652,
505.2578125,
34.5
],
[
1727320652,
505.2578125,
41.3
]
];
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> 383</td><td>0.87306</td><td>0.8</td><td>0.263906 </td></tr></tbody></table><h2>Experiment parameters: </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type </th></tr></thead><tbody><tr><td> n_reference_samples</td><td>range</td><td>50</td><td>500</td><td></td><td>int </td></tr><tr><td> recent_samples_proport…</td><td>range</td><td>0.1</td><td>1</td><td></td><td>float </td></tr><tr><td> threshold</td><td>range</td><td>0.5</td><td>0.8</td><td></td><td>float </td></tr></tbody></table><br><h2>Number of evaluations:</h2>
<table>
<tbody>
<tr>
<th>Failed</th>
<th>Succeeded</th>
<th>Running</th>
<th>Total</th>
</tr>
<tr>
<td>0</td>
<td>499</td>
<td>7</td>
<td>506</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,0.289624407974446551605751665193,279,0.891164058446884177477897992503,0.516926014050841375890854578756
1,1_0,COMPLETED,Sobol,0.287256305760546282179745958274,277,0.796329740807414032666144976247,0.553851592727005459515510210622
2,2_0,COMPLETED,Sobol,0.287641810772111483629487338476,56,0.564008093904703855514526367188,0.777804055716842412948608398438
3,3_0,COMPLETED,Sobol,0.281969379887652804583808574534,176,0.199219920486211787835628683752,0.711541084758937403265122156881
4,4_0,COMPLETED,Sobol,0.288357748650732492734505285625,459,0.128826317191123973504573996252,0.683132903557270854122407399700
5,5_0,COMPLETED,Sobol,0.283841832800969307015748199774,97,0.237775728758424537145899080315,0.772070944309234619140625000000
6,6_0,COMPLETED,Sobol,0.290560634431104691799419015297,403,0.592548271361738598805857236584,0.589519310090690806802626866556
7,7_0,COMPLETED,Sobol,0.290725850864632651138208530028,451,0.159694225434213882275358287188,0.624980535637587375497048469697
8,8_0,COMPLETED,Sobol,0.283731688511950630449121035781,386,0.186210354324430227279663085938,0.606148756481707073895393023122
9,9_0,COMPLETED,Sobol,0.295021478136358594746013750409,330,0.131327967438846837655574972814,0.689751045312732458114624023438
10,10_0,COMPLETED,Sobol,0.289789624407974399922238717409,346,0.146652578376233577728271484375,0.736295207403600215911865234375
11,11_0,COMPLETED,Sobol,0.291772221610309467898503044125,237,0.779963809158653043063225140941,0.720785484369844242635849695944
12,12_0,COMPLETED,Sobol,0.286430223592906707530403309647,63,0.513835391122847817690910687816,0.657586010452359870370742100931
13,13_0,COMPLETED,Sobol,0.300088115431214941253301731194,423,0.968054596427828095706047406566,0.696664147078990936279296875000
14,14_0,COMPLETED,Sobol,0.291882365899328144465130208118,346,0.803905521146953128130974164378,0.681365316361188910754265180003
15,15_0,COMPLETED,Sobol,0.281804163454124956267321522319,113,0.762007247097790219036994585622,0.619599760323762849267836827494
16,16_0,COMPLETED,Sobol,0.286760656459962515185679876595,390,0.650765308737754843981804242503,0.509744476806372359689589757181
17,17_0,COMPLETED,Sobol,0.284172265668025114671024766722,354,0.956087445653974965509291905619,0.714265111647546357964699836884
18,18_0,COMPLETED,Sobol,0.289954840841502359261028232140,214,0.226930457167327404022216796875,0.726172034256160281451286664378
19,19_0,COMPLETED,Sobol,0.287586738627602200857324987737,50,0.372523916605860039297226649069,0.715504896827042102813720703125
20,20_0,COMPLETED,BoTorch,0.288137460072695250623553420155,50,0.801743443852676596073081327631,0.540452633372169310987942481006
21,21_0,COMPLETED,BoTorch,0.277343319748871053320726787206,97,0.100000000000000005551115123126,0.624729871840003392335916032607
22,22_0,COMPLETED,BoTorch,0.284117193523515831898862415983,56,1.000000000000000000000000000000,0.649486102295221967573013444053
23,23_0,COMPLETED,BoTorch,0.280262143407864261490658464027,141,0.470512614417974095459840100375,0.564499219695059339940712561656
24,24_0,COMPLETED,BoTorch,0.283180967066857580682892603363,50,0.100000000000000005551115123126,0.800000000000000044408920985006
25,25_0,COMPLETED,BoTorch,0.292763520211477001886635207484,349,0.127297051678502609606269402320,0.500000000000000000000000000000
26,26_0,COMPLETED,BoTorch,0.277783896904945426520328055631,140,1.000000000000000000000000000000,0.564564511779196087637444634311
27,27_0,COMPLETED,BoTorch,0.287862099350148725740439203946,50,0.899798597715675940733603965782,0.737071712894347363942415540805
28,28_0,COMPLETED,BoTorch,0.287972243639167291284763905423,50,1.000000000000000000000000000000,0.557236292659133436444562903489
29,29_0,COMPLETED,BoTorch,0.282630245621764530916664170945,50,0.342702119779559111201194809837,0.500000000000000000000000000000
30,30_0,COMPLETED,BoTorch,0.288908470095825542500733718043,500,0.100000000000000005551115123126,0.500000000000000000000000000000
31,31_0,COMPLETED,BoTorch,0.279050556228659596413876897714,105,0.100000000000000005551115123126,0.697349459957396056175582543801
32,32_0,COMPLETED,BoTorch,0.289183830818372067383847934252,393,0.100000000000000005551115123126,0.508758417289519870863045980514
33,33_0,COMPLETED,BoTorch,0.279876638396299171063219546340,149,0.100000000000000005551115123126,0.676926164921466511081860062404
34,34_0,COMPLETED,BoTorch,0.281033153430994553367838761915,147,0.352689434361582510035759696621,0.639532277146965477676587852329
35,35_0,COMPLETED,BoTorch,0.275801299702610469566366191430,153,0.660061439632941282518174830329,0.500000000000000000000000000000
36,36_0,COMPLETED,BoTorch,0.283896904945478589787910550513,50,0.752864104031171543773837129265,0.648979577944798879940435654134
37,37_0,COMPLETED,BoTorch,0.285824430003304374992012526491,102,0.830370083122697555388924683939,0.688815222221741740860068148322
38,38_0,COMPLETED,BoTorch,0.292267870910893234892569125805,500,0.517269410043618482752947329573,0.500000000000000000000000000000
39,39_0,COMPLETED,BoTorch,0.281418658442559754817580142117,50,0.100000000000000005551115123126,0.698637980430236171791591459623
40,40_0,COMPLETED,BoTorch,0.279491133384733969613478166139,157,0.919310758947713968503023806988,0.500000000000000000000000000000
41,41_0,COMPLETED,BoTorch,0.279546205529243363407942979393,107,0.100000000000000005551115123126,0.514477506174741527900096116355
42,42_0,COMPLETED,BoTorch,0.282905606344311055799778387154,141,0.824542545677123039915557001223,0.500000000000000000000000000000
43,43_0,COMPLETED,BoTorch,0.281638947020596996928532007587,169,0.485213483805112533175929456775,0.500000000000000000000000000000
44,44_0,COMPLETED,BoTorch,0.280702720563938745712562194967,162,0.857264041744994487181941167364,0.567150924444416837388871499570
45,45_0,COMPLETED,BoTorch,0.282685317766273813688826521684,174,0.865947602399560367736341959244,0.500000000000000000000000000000
46,46_0,COMPLETED,BoTorch,0.278830267650622354302925032243,163,0.100000000000000005551115123126,0.500000000000000000000000000000
47,47_0,COMPLETED,BoTorch,0.282134596321180763922598089266,88,0.100000000000000005551115123126,0.500000000000000000000000000000
48,48_0,COMPLETED,BoTorch,0.277618680471417578203841003415,159,1.000000000000000000000000000000,0.670156931037201419876225827466
49,49_0,COMPLETED,BoTorch,0.284667914968608881665090848401,131,0.964775461149469548693957676733,0.500000000000000000000000000000
50,50_0,COMPLETED,BoTorch,0.284062121379006549126700065244,166,1.000000000000000000000000000000,0.523538748169329437587293796241
51,51_0,COMPLETED,BoTorch,0.282409957043727288805712305475,150,0.895772707180448768404801285214,0.606021793766657990154556046036
52,52_0,COMPLETED,BoTorch,0.277894041193964103086955219624,115,0.100000000000000005551115123126,0.579229492328251716060094622662
53,53_0,COMPLETED,BoTorch,0.285879502147813657764174877229,155,0.778645814750002274173823479941,0.500000000000000000000000000000
54,54_0,COMPLETED,BoTorch,0.281528802731578320361904843594,104,0.344790234989131638698722781555,0.500000000000000000000000000000
55,55_0,COMPLETED,BoTorch,0.281198369864522512706628276646,169,0.729309840698723399476932627294,0.500000000000000000000000000000
56,56_0,COMPLETED,BoTorch,0.277949113338473385859117570362,137,0.100000000000000005551115123126,0.539134108423303004542503913399
57,57_0,COMPLETED,BoTorch,0.281308514153541189273255440639,137,0.332452673083033889422921447476,0.500000000000000000000000000000
58,58_0,COMPLETED,BoTorch,0.282079524176671481150435738527,125,0.100000000000000005551115123126,0.628846958941705946877220867464
59,59_0,COMPLETED,BoTorch,0.285328780702720607997946444812,153,1.000000000000000000000000000000,0.799425505444962825052357402456
60,60_0,COMPLETED,BoTorch,0.275030289679480066666883431026,153,0.598139552546557595746890001465,0.600108510738485478874792988790
61,61_0,COMPLETED,BoTorch,0.280978081286485270595676411176,138,0.100000000000000005551115123126,0.500000000000000000000000000000
62,62_0,COMPLETED,BoTorch,0.281033153430994553367838761915,133,0.100000000000000005551115123126,0.800000000000000044408920985006
63,63_0,COMPLETED,BoTorch,0.289018614384844108045058419521,145,0.446657079522024047513184541458,0.800000000000000044408920985006
64,64_0,COMPLETED,BoTorch,0.291607005176781619582015991909,156,0.683439246111532283656231356872,0.755922336173302844564148017525
65,65_0,COMPLETED,BoTorch,0.281969379887652804583808574534,147,0.100000000000000005551115123126,0.582915451697777253770027527935
66,66_0,COMPLETED,BoTorch,0.279821566251789888291057195602,124,0.283535641492037748800925101023,0.594005294001025641037472269090
67,67_0,COMPLETED,BoTorch,0.280757792708448028484724545706,120,0.197567830373741482041793915414,0.537675287353794773004267426586
68,68_0,COMPLETED,BoTorch,0.281528802731578320361904843594,141,0.416696188482252094509306061809,0.500000000000000000000000000000
69,69_0,COMPLETED,BoTorch,0.281088225575503947162303575169,129,0.100000000000000005551115123126,0.699143854074393278352772540529
70,70_0,COMPLETED,BoTorch,0.278499834783566435625346002780,125,0.100000000000000005551115123126,0.522943711585440729194829145854
71,71_0,COMPLETED,BoTorch,0.276241876858684842765967459854,126,0.100000000000000005551115123126,0.589314744833681292135452167713
72,72_0,COMPLETED,BoTorch,0.281694019165106279700694358326,73,0.100000000000000005551115123126,0.601001536135989500309051436489
73,73_0,COMPLETED,BoTorch,0.281473730587069037589742492855,168,0.395413665049338058921080119035,0.569241035680599027912762721826
74,74_0,COMPLETED,BoTorch,0.283180967066857580682892603363,140,0.645654732849099244340607128834,0.572350880373848291959859579947
75,75_0,COMPLETED,BoTorch,0.282244740610199329466922790743,159,0.646448755631037541569128279662,0.569466049017948505728270447435
76,76_0,COMPLETED,BoTorch,0.278830267650622354302925032243,176,1.000000000000000000000000000000,0.609506905769382378856846571580
77,77_0,COMPLETED,BoTorch,0.277728824760436143748165704892,136,1.000000000000000000000000000000,0.623141711529160868821008989471
78,78_0,COMPLETED,BoTorch,0.285989646436832223308499578707,196,0.100000000000000005551115123126,0.500000000000000000000000000000
79,79_0,COMPLETED,BoTorch,0.283456327789404105566006819572,50,0.100000000000000005551115123126,0.500000000000000000000000000000
80,80_0,COMPLETED,BoTorch,0.283401255644894822793844468833,50,0.100000000000000005551115123126,0.566756900244500316787821247999
81,81_0,COMPLETED,BoTorch,0.283125894922348297910730252624,167,0.258458763596267737661094088253,0.564775181152750271884599442274
82,82_0,COMPLETED,BoTorch,0.282740389910783096460988872423,194,0.286850907074828187504067500413,0.504527680255935195852146080142
83,83_0,COMPLETED,BoTorch,0.279050556228659596413876897714,187,0.100000000000000005551115123126,0.544939498567801439143920561037
84,84_0,COMPLETED,BoTorch,0.287531666483092807062860174483,218,0.100000000000000005551115123126,0.533253841634868774868039054127
85,85_0,COMPLETED,BoTorch,0.278169401916510627970069435833,175,0.822366476223294617931003358535,0.613299254536257665293419449881
86,86_0,COMPLETED,BoTorch,0.284117193523515831898862415983,86,0.100000000000000005551115123126,0.667065606852710879515200304013
87,87_0,COMPLETED,BoTorch,0.285879502147813657764174877229,175,0.566697434739261041158897569403,0.573756627997540791241704027925
88,88_0,COMPLETED,BoTorch,0.280592576274920180168237493490,155,0.507639005828601552750001246750,0.563206736610502134432465481950
89,89_0,COMPLETED,BoTorch,0.285383852847229890770108795550,153,1.000000000000000000000000000000,0.627645731124489514307640547486
90,90_0,COMPLETED,BoTorch,0.278554906928075829419810816034,111,0.100000000000000005551115123126,0.647911238618280593826170843386
91,91_0,COMPLETED,BoTorch,0.278995484084150202619412084459,169,1.000000000000000000000000000000,0.648269300139687310036151757231
92,92_0,COMPLETED,BoTorch,0.279270844806696727502526300668,106,0.204881163444568081821728355862,0.603062110959838304502511618921
93,93_0,COMPLETED,BoTorch,0.280592576274920180168237493490,156,1.000000000000000000000000000000,0.596234821278724180793062714656
94,94_0,COMPLETED,BoTorch,0.279821566251789888291057195602,102,0.100000000000000005551115123126,0.575733639758434612154758269753
95,95_0,COMPLETED,BoTorch,0.279270844806696727502526300668,140,0.100000000000000005551115123126,0.629480158745430951050536805269
96,96_0,COMPLETED,BoTorch,0.282630245621764530916664170945,146,1.000000000000000000000000000000,0.672932905304021167225414501445
97,97_0,COMPLETED,BoTorch,0.278279546205529193514394137310,103,0.100000000000000005551115123126,0.800000000000000044408920985006
98,98_0,COMPLETED,BoTorch,0.285493997136248456314433497027,126,1.000000000000000000000000000000,0.619025403251991068742654533708
99,99_0,COMPLETED,BoTorch,0.281253442009031795478790627385,112,0.350429475797578660056785793131,0.583888343469668891216883821471
100,100_0,COMPLETED,BoTorch,0.276902742592796569098823056265,147,1.000000000000000000000000000000,0.645935380995855679131523174874
101,101_0,COMPLETED,BoTorch,0.282024452032162087355970925273,179,0.755580169805030954499613926600,0.579296420340779216751059266244
102,102_0,COMPLETED,BoTorch,0.287586738627602200857324987737,183,1.000000000000000000000000000000,0.647020022197494326654521046294
103,103_0,COMPLETED,BoTorch,0.277343319748871053320726787206,137,0.272426633484075086943931864880,0.567132029990135788644067815767
104,104_0,COMPLETED,BoTorch,0.285053419980174083114832228603,204,1.000000000000000000000000000000,0.598490366596845735358556339634
105,105_0,COMPLETED,BoTorch,0.283070822777839015138567901886,152,1.000000000000000000000000000000,0.614593440376986377415846618533
106,106_0,COMPLETED,BoTorch,0.281749091309615562472856709064,117,0.559933035129034029075967282552,0.518328787245505018255187223986
107,107_0,COMPLETED,BoTorch,0.287476594338583524290697823744,218,1.000000000000000000000000000000,0.527725613458486453311024888535
108,108_0,COMPLETED,BoTorch,0.281694019165106279700694358326,129,0.182432777657681566285674534811,0.573961679878187136516487498739
109,109_0,COMPLETED,BoTorch,0.282465029188236571577874656214,132,0.214593935761159343433845947402,0.604675191583381010929088006378
110,110_0,COMPLETED,BoTorch,0.282299812754708723261387603998,140,0.215783266746549240444608130929,0.526736118009531684158730513445
111,111_0,COMPLETED,BoTorch,0.279270844806696727502526300668,84,0.100000000000000005551115123126,0.800000000000000044408920985006
112,112_0,COMPLETED,BoTorch,0.279931710540808453835381897079,121,0.461099321040784437819581853546,0.520238375675113884355482696265
113,113_0,COMPLETED,BoTorch,0.280262143407864261490658464027,152,0.242451434744108240693094558083,0.500000000000000000000000000000
114,114_0,COMPLETED,BoTorch,0.281528802731578320361904843594,133,0.100000000000000005551115123126,0.568533926371933340249142929679
115,115_0,COMPLETED,BoTorch,0.278995484084150202619412084459,116,0.100000000000000005551115123126,0.552902456056725388755523908912
116,116_0,COMPLETED,BoTorch,0.279546205529243363407942979393,114,0.100000000000000005551115123126,0.500000000000000000000000000000
117,117_0,COMPLETED,BoTorch,0.279766494107280494496592382347,155,0.100000000000000005551115123126,0.553111818141280853211583234952
118,118_0,COMPLETED,BoTorch,0.285769357858794981197547713236,111,0.173870024645234877436195120026,0.500000000000000000000000000000
119,119_0,COMPLETED,BoTorch,0.281914307743143521811646223796,158,0.334927417987588516012920081266,0.510832448987396881001643578202
120,120_0,COMPLETED,BoTorch,0.283621544222932064904796334304,118,0.268408705336400044139111287222,0.644332027836468590464846784016
121,121_0,COMPLETED,BoTorch,0.282465029188236571577874656214,122,0.708557093278831318095001279289,0.556082995274509994665379508660
122,122_0,COMPLETED,BoTorch,0.280867936997466705051351709699,106,0.100000000000000005551115123126,0.732072635940252292030550052004
123,123_0,COMPLETED,BoTorch,0.283511399933913388338169170311,121,0.566909573171175185102299565187,0.541865272891536631227893394680
124,124_0,COMPLETED,BoTorch,0.275636083269082510227576676698,149,0.754695824747960486433839832898,0.588425420491383244225858106802
125,125_0,COMPLETED,BoTorch,0.283125894922348297910730252624,129,0.589246593185611011733726627426,0.500000000000000000000000000000
126,126_0,COMPLETED,BoTorch,0.281418658442559754817580142117,110,0.100000000000000005551115123126,0.609115066559503426368848977290
127,127_0,COMPLETED,BoTorch,0.282850534199801773027616036416,203,0.531040033459246552105526006926,0.500000000000000000000000000000
128,128_0,COMPLETED,BoTorch,0.281143297720013229934465925908,165,0.100000000000000005551115123126,0.624624212429148006897605682752
129,129_0,COMPLETED,BoTorch,0.279325916951206121296991113923,121,0.100000000000000005551115123126,0.685114631577661836736581335572
130,130_0,COMPLETED,BoTorch,0.279601277673752646180105330131,87,0.100000000000000005551115123126,0.751111028744832598391667488613
131,131_0,COMPLETED,BoTorch,0.279766494107280494496592382347,142,0.614650447696278301457084580761,0.540367909531072276863028491789
132,132_0,COMPLETED,BoTorch,0.280592576274920180168237493490,146,0.549776497456099355609637768794,0.500000000000000000000000000000
133,133_0,COMPLETED,BoTorch,0.281143297720013229934465925908,127,0.100000000000000005551115123126,0.752156689269240175121922220569
134,134_0,COMPLETED,BoTorch,0.279546205529243363407942979393,111,0.100000000000000005551115123126,0.742716400990437897178253479069
135,135_0,COMPLETED,BoTorch,0.277618680471417578203841003415,158,0.100000000000000005551115123126,0.600584001707562875616019937297
136,136_0,COMPLETED,BoTorch,0.281253442009031795478790627385,142,0.826278419562594956815360092151,0.552684077132715168012566664402
137,137_0,COMPLETED,BoTorch,0.278885339795131637075087382982,105,1.000000000000000000000000000000,0.530364569594345525160861143377
138,138_0,COMPLETED,BoTorch,0.275911443991629035110690892907,117,0.100000000000000005551115123126,0.728469742401629316574940276041
139,139_0,COMPLETED,BoTorch,0.281198369864522512706628276646,136,0.611143793025807013918893062510,0.540528177830530864866886986420
140,140_0,COMPLETED,BoTorch,0.282134596321180763922598089266,201,0.608590575169650760400941180706,0.538717043023511754640253457183
141,141_0,COMPLETED,BoTorch,0.280372287696882938057285628020,130,0.100000000000000005551115123126,0.659927403001376200464278554136
142,142_0,COMPLETED,BoTorch,0.277673752615926860976003354153,135,0.100000000000000005551115123126,0.597870434712020837331181155605
143,143_0,COMPLETED,BoTorch,0.285989646436832223308499578707,126,1.000000000000000000000000000000,0.574420502534266352867575733399
144,144_0,COMPLETED,BoTorch,0.275085361823989460461348244280,157,0.100000000000000005551115123126,0.775100145791817185525474087626
145,145_0,COMPLETED,BoTorch,0.284778059257627447209415549878,150,0.401954412510997616259089681989,0.551410232145369882950092232932
146,146_0,COMPLETED,BoTorch,0.287091089327018433863258906058,147,0.241245701405028684094489221934,0.626640860581471947909903974505
147,147_0,COMPLETED,BoTorch,0.283070822777839015138567901886,147,0.100000000000000005551115123126,0.736845088012871629601363565598
148,148_0,COMPLETED,BoTorch,0.280482431985901503601610329497,102,0.100000000000000005551115123126,0.665105291605398774024138219829
149,149_0,COMPLETED,BoTorch,0.277728824760436143748165704892,95,0.100000000000000005551115123126,0.703587212337524525729293145559
150,150_0,COMPLETED,BoTorch,0.274975217534970783894721080287,144,0.798855281559089580589727574989,0.607419363917430454868906508636
151,151_0,COMPLETED,BoTorch,0.283070822777839015138567901886,149,0.100000000000000005551115123126,0.622283128859416168054963236500
152,152_0,COMPLETED,BoTorch,0.280151999118845695946333762549,142,0.100000000000000005551115123126,0.713164479485242419620760756516
153,153_0,COMPLETED,BoTorch,0.296728714616147137839163860917,500,0.100000000000000005551115123126,0.800000000000000044408920985006
154,154_0,COMPLETED,BoTorch,0.281418658442559754817580142117,152,0.100000000000000005551115123126,0.500000000000000000000000000000
155,155_0,COMPLETED,BoTorch,0.278114329772001345197907085094,125,0.100000000000000005551115123126,0.553270322635416866852153816581
156,156_0,COMPLETED,BoTorch,0.282299812754708723261387603998,88,0.280239437988785589084272942273,0.607837710627459615331247277936
157,157_0,COMPLETED,BoTorch,0.284282409957043680215349468199,140,0.440010489543598670714175113972,0.597240754300619181016429593001
158,158_0,COMPLETED,BoTorch,0.277618680471417578203841003415,147,0.100000444397151569408954685514,0.736858308486604385301177444489
159,159_0,COMPLETED,BoTorch,0.281088225575503947162303575169,151,0.100000000000000005551115123126,0.800000000000000044408920985006
160,160_0,COMPLETED,BoTorch,0.281088225575503947162303575169,119,0.100000000000000005551115123126,0.800000000000000044408920985006
161,161_0,COMPLETED,BoTorch,0.278885339795131637075087382982,156,0.759328526471418685517278390762,0.573682427118430426915551834099
162,162_0,COMPLETED,BoTorch,0.280702720563938745712562194967,158,0.811226580330461799483998674987,0.601976145592748035362262726267
163,163_0,COMPLETED,BoTorch,0.281638947020596996928532007587,103,1.000000000000000000000000000000,0.641159654533012712818162981421
164,164_0,COMPLETED,BoTorch,0.285218636413701931431319280819,157,0.715667241700024492345733051479,0.617026921353644897116907941381
165,165_0,COMPLETED,BoTorch,0.282409957043727288805712305475,138,0.882297257699207748693481789815,0.625156568365934584008414276468
166,166_0,COMPLETED,BoTorch,0.282409957043727288805712305475,117,0.855948281786603648590983084432,0.616026613298382930139496238553
167,167_0,COMPLETED,BoTorch,0.282850534199801773027616036416,140,0.768956595616834825968055611156,0.581679216058005543743547605118
168,168_0,COMPLETED,BoTorch,0.282520101332745854350037006952,101,0.753390832249697162126267357962,0.500000000000000000000000000000
169,169_0,COMPLETED,BoTorch,0.280041854829827019379706598556,158,0.573830279752985172869728103251,0.504244906419654026485943631997
170,170_0,COMPLETED,BoTorch,0.280096926974336413174171411811,143,0.738780599315331354404179364792,0.635865821269970710183372375468
171,171_0,COMPLETED,BoTorch,0.282244740610199329466922790743,149,0.850683235577019969397838394798,0.636228354121950778754523980751
172,172_0,COMPLETED,BoTorch,0.280482431985901503601610329497,292,1.000000000000000000000000000000,0.800000000000000044408920985006
173,173_0,COMPLETED,BoTorch,0.295627271725961038306706996082,252,1.000000000000000000000000000000,0.700659248987379634776573311683
174,174_0,COMPLETED,BoTorch,0.282850534199801773027616036416,150,0.791706784628439241835451412044,0.621133305534232627032054097072
175,175_0,COMPLETED,BoTorch,0.281088225575503947162303575169,147,0.847495953438784033195929623616,0.589111630732766622386975541303
176,176_0,COMPLETED,BoTorch,0.280702720563938745712562194967,164,0.739701675300268091106659085199,0.554711366237580216775882036018
177,177_0,COMPLETED,BoTorch,0.268476704482872530199699667719,301,1.000000000000000000000000000000,0.800000000000000044408920985006
178,178_0,COMPLETED,BoTorch,0.281914307743143521811646223796,90,0.294478249442521322620791579538,0.500000000000000000000000000000
179,179_0,COMPLETED,BoTorch,0.277178103315343093981937272474,75,0.395910251503120091953746850777,0.544326927050409636876793229021
180,180_0,COMPLETED,BoTorch,0.279931710540808453835381897079,174,0.100000000000000005551115123126,0.800000000000000044408920985006
181,181_0,COMPLETED,BoTorch,0.283070822777839015138567901886,92,0.360545560127547681794624168106,0.560290197268636047667200728029
182,182_0,COMPLETED,BoTorch,0.276572309725740761443546489318,324,1.000000000000000000000000000000,0.800000000000000044408920985006
183,183_0,COMPLETED,BoTorch,0.274865073245952218350396378810,335,1.000000000000000000000000000000,0.800000000000000044408920985006
184,184_0,RUNNING,BoTorch,,303,0.958694909086270308229416059476,0.800000000000000044408920985006
185,185_0,COMPLETED,BoTorch,0.280482431985901503601610329497,291,0.996548394472000276245182703860,0.798523219987048227253012555593
186,186_0,COMPLETED,BoTorch,0.281749091309615562472856709064,142,0.100000000000000005551115123126,0.558193795146972204790358773607
187,187_0,COMPLETED,BoTorch,0.281859235598634239039483873057,138,0.100000000000000005551115123126,0.744358055170847854320470560197
188,188_0,COMPLETED,BoTorch,0.264511510078202394247171014285,293,0.996391492882949703080441850034,0.796162224358458403550287130201
189,189_0,COMPLETED,BoTorch,0.274865073245952218350396378810,311,0.994690502591530245624085182499,0.800000000000000044408920985006
190,190_0,COMPLETED,BoTorch,0.271781033153430939819372724742,166,0.100000000000000005551115123126,0.800000000000000044408920985006
191,191_0,COMPLETED,BoTorch,0.276241876858684842765967459854,312,0.996242845505960095131570142257,0.799984622502764697316024467000
192,192_0,COMPLETED,BoTorch,0.284227337812534397443187117460,75,0.100000000000000005551115123126,0.713608965085757218638207177719
193,193_0,COMPLETED,BoTorch,0.267375261592686430667242802883,332,0.945649661879584435553169896593,0.800000000000000044408920985006
194,194_0,COMPLETED,BoTorch,0.280482431985901503601610329497,379,1.000000000000000000000000000000,0.745245213501755654483815760614
195,195_0,COMPLETED,BoTorch,0.285549069280757739086595847766,304,0.994362178770515914294492176850,0.785640164104857152693739408278
196,196_0,COMPLETED,BoTorch,0.279380989095715404069153464661,323,0.837817851960625636920099168492,0.800000000000000044408920985006
197,197_0,COMPLETED,BoTorch,0.283456327789404105566006819572,311,0.878770023384396470333967954502,0.800000000000000044408920985006
198,198_0,COMPLETED,BoTorch,0.276737526159268609760033541534,353,0.874053436737264433098459903704,0.800000000000000044408920985006
199,199_0,COMPLETED,BoTorch,0.278554906928075829419810816034,319,0.926535312920402587266721639025,0.800000000000000044408920985006
200,200_0,COMPLETED,BoTorch,0.277398391893380336092889137944,338,0.808711414977091092026739715948,0.800000000000000044408920985006
201,201_0,COMPLETED,BoTorch,0.278389690494547870081021301303,314,0.766525375307473999519913832046,0.800000000000000044408920985006
202,202_0,COMPLETED,BoTorch,0.273983918933803249906588916929,347,1.000000000000000000000000000000,0.800000000000000044408920985006
203,203_0,COMPLETED,BoTorch,0.291221500165216418132274611708,343,0.647847515836837861691321904800,0.800000000000000044408920985006
204,204_0,COMPLETED,BoTorch,0.280262143407864261490658464027,317,1.000000000000000000000000000000,0.800000000000000044408920985006
205,205_0,COMPLETED,BoTorch,0.276517237581231367649081676063,335,0.906182912697792275480423995759,0.800000000000000044408920985006
206,206_0,COMPLETED,BoTorch,0.273323053199691634596035783034,290,1.000000000000000000000000000000,0.797369097296063200630555911630
207,207_0,COMPLETED,BoTorch,0.279821566251789888291057195602,315,0.767212150206641907423943393951,0.800000000000000044408920985006
208,208_0,COMPLETED,BoTorch,0.283456327789404105566006819572,311,0.878747217887670739600025626714,0.800000000000000044408920985006
209,209_0,COMPLETED,BoTorch,0.268531776627381923994164480973,356,0.771509734929296975280976766953,0.800000000000000044408920985006
210,210_0,COMPLETED,BoTorch,0.297059147483202945494440427865,312,0.668046677948009426373232599872,0.800000000000000044408920985006
211,211_0,COMPLETED,BoTorch,0.264181077211146586591894447338,343,0.874318592330021848724186384061,0.800000000000000044408920985006
212,212_0,COMPLETED,BoTorch,0.270569445974226274742591158429,358,1.000000000000000000000000000000,0.800000000000000044408920985006
213,213_0,COMPLETED,BoTorch,0.264511510078202394247171014285,377,1.000000000000000000000000000000,0.800000000000000044408920985006
214,214_0,COMPLETED,BoTorch,0.264511510078202394247171014285,370,0.914378186276954885158829711145,0.800000000000000044408920985006
215,215_0,COMPLETED,BoTorch,0.285769357858794981197547713236,500,1.000000000000000000000000000000,0.800000000000000044408920985006
216,216_0,COMPLETED,BoTorch,0.265833241546425846912882207107,368,1.000000000000000000000000000000,0.800000000000000044408920985006
217,217_0,COMPLETED,BoTorch,0.276352021147703519332594623847,412,1.000000000000000000000000000000,0.800000000000000044408920985006
218,218_0,COMPLETED,BoTorch,0.285273708558211214203481631557,380,0.871642830438248972235726341751,0.800000000000000044408920985006
219,219_0,COMPLETED,BoTorch,0.263905716488600061708780231129,383,0.873060294683975057772329364525,0.800000000000000044408920985006
220,220_0,COMPLETED,BoTorch,0.269302786650512215871344778861,373,0.949563283463774387982425650989,0.800000000000000044408920985006
221,215_0,COMPLETED,BoTorch,0.285769357858794981197547713236,500,1.000000000000000000000000000000,0.800000000000000044408920985006
222,222_0,COMPLETED,BoTorch,0.268917281638947014421603398659,383,0.929627497434246552465708646196,0.800000000000000044408920985006
223,223_0,COMPLETED,BoTorch,0.264181077211146586591894447338,358,0.947436191357290624637244036421,0.800000000000000044408920985006
224,224_0,COMPLETED,BoTorch,0.277178103315343093981937272474,372,0.768712087063325277824787917780,0.799020612880612102202348978608
225,225_0,COMPLETED,BoTorch,0.290780923009141933910370880767,250,1.000000000000000000000000000000,0.800000000000000044408920985006
226,226_0,COMPLETED,BoTorch,0.264511510078202394247171014285,370,0.914452224469342156432105639396,0.799998037603066425305087250308
227,227_0,COMPLETED,BoTorch,0.264015860777618627253104932606,342,1.000000000000000000000000000000,0.800000000000000044408920985006
228,228_0,COMPLETED,BoTorch,0.266934684436611946445339071943,380,0.965319240107303300213459351653,0.798559567634888001208537389175
229,229_0,COMPLETED,BoTorch,0.269688291662077306298783696548,365,0.973863576583314793921886121097,0.800000000000000044408920985006
230,230_0,COMPLETED,BoTorch,0.286099790725850899875126742700,373,0.977648729541179761781677370891,0.800000000000000044408920985006
231,231_0,COMPLETED,BoTorch,0.277618680471417578203841003415,365,0.955519798111623908098977153713,0.800000000000000044408920985006
232,232_0,COMPLETED,BoTorch,0.273653486066747442251312349981,364,1.000000000000000000000000000000,0.800000000000000044408920985006
233,233_0,COMPLETED,BoTorch,0.265833241546425846912882207107,303,0.959553949322008659095217808499,0.800000000000000044408920985006
234,234_0,COMPLETED,BoTorch,0.275801299702610469566366191430,382,1.000000000000000000000000000000,0.800000000000000044408920985006
235,235_0,COMPLETED,BoTorch,0.264511510078202394247171014285,365,0.938316447105499884528967413644,0.800000000000000044408920985006
236,236_0,COMPLETED,BoTorch,0.265943385835444412457206908584,372,1.000000000000000000000000000000,0.800000000000000044408920985006
237,237_0,COMPLETED,BoTorch,0.277673752615926860976003354153,387,0.969519165844187291725120303454,0.800000000000000044408920985006
238,238_0,COMPLETED,BoTorch,0.267154973014649188556290937413,396,0.933779004908241727633821938070,0.800000000000000044408920985006
239,239_0,COMPLETED,BoTorch,0.276021588280647600655015594384,416,0.818087587539432337102596193290,0.800000000000000044408920985006
240,240_0,COMPLETED,BoTorch,0.279436061240224686841315815400,361,1.000000000000000000000000000000,0.800000000000000044408920985006
241,241_0,COMPLETED,BoTorch,0.279711421962771211724430031609,378,0.966108892349967551815836941387,0.800000000000000044408920985006
242,242_0,COMPLETED,BoTorch,0.264511510078202394247171014285,392,1.000000000000000000000000000000,0.800000000000000044408920985006
243,243_0,COMPLETED,BoTorch,0.282244740610199329466922790743,378,0.904400921222387244036156062066,0.800000000000000044408920985006
244,244_0,COMPLETED,BoTorch,0.269027425927965579965928100137,360,0.977674684351510725477396590577,0.800000000000000044408920985006
245,245_0,COMPLETED,BoTorch,0.269027425927965579965928100137,354,1.000000000000000000000000000000,0.800000000000000044408920985006
246,246_0,COMPLETED,BoTorch,0.272331754598524100607903619675,374,1.000000000000000000000000000000,0.800000000000000044408920985006
247,247_0,COMPLETED,BoTorch,0.283511399933913388338169170311,338,0.497856292931495136855346572702,0.800000000000000044408920985006
248,248_0,COMPLETED,BoTorch,0.278224474061019910742231786571,354,0.973304040590569297108913815464,0.800000000000000044408920985006
249,249_0,COMPLETED,BoTorch,0.278609979072585112191973166773,396,0.965560588305434008837835335726,0.800000000000000044408920985006
250,250_0,COMPLETED,BoTorch,0.274424496089877734128492647869,448,0.677222300592064785362822476600,0.800000000000000044408920985006
251,251_0,COMPLETED,BoTorch,0.283896904945478589787910550513,360,0.925360115892686518890286606620,0.800000000000000044408920985006
252,252_0,COMPLETED,BoTorch,0.273212908910672958029408619041,500,0.618052195965712591885221627308,0.800000000000000044408920985006
253,253_0,RUNNING,BoTorch,,398,0.798818729532374871560307383334,0.781131639569701441416782472515
254,254_0,COMPLETED,BoTorch,0.274865073245952218350396378810,352,0.952081214674563636179982495378,0.800000000000000044408920985006
255,255_0,COMPLETED,BoTorch,0.285163564269192648659156930080,371,0.988840638652387027285328713333,0.787800422303434944026889752422
256,256_0,COMPLETED,BoTorch,0.268862209494437731649441047921,354,1.000000000000000000000000000000,0.781122717880043282434598950204
257,257_0,COMPLETED,BoTorch,0.269963652384623831181897912757,316,0.988956860205542631625519334193,0.795428244523919802944078583096
258,258_0,COMPLETED,BoTorch,0.276737526159268609760033541534,403,0.849806287291500428437984737684,0.800000000000000044408920985006
259,259_0,COMPLETED,BoTorch,0.278830267650622354302925032243,351,1.000000000000000000000000000000,0.800000000000000044408920985006
260,260_0,COMPLETED,BoTorch,0.275195506113008026005672945757,361,0.957184276880728490688454712654,0.800000000000000044408920985006
261,261_0,COMPLETED,BoTorch,0.278665051217094394964135517512,352,1.000000000000000000000000000000,0.793664962769950133036900297157
262,262_0,COMPLETED,BoTorch,0.269082498072474973760392913391,361,0.976879885690498994677000155207,0.786449735510538427973870057031
263,263_0,COMPLETED,BoTorch,0.279215772662187444730363949930,417,0.813759295452348263566477726272,0.799184387915465310747720195650
264,264_0,COMPLETED,BoTorch,0.287476594338583524290697823744,365,1.000000000000000000000000000000,0.783930065114958329886007959431
265,265_0,COMPLETED,BoTorch,0.276627381870250044215708840056,440,0.927922219217322763462618695485,0.800000000000000044408920985006
266,266_0,COMPLETED,BoTorch,0.273708558211256725023474700720,500,0.744786375018513191470503898017,0.800000000000000044408920985006
267,267_0,COMPLETED,BoTorch,0.286595440026434666869192824379,476,0.738024596849858993685700170317,0.800000000000000044408920985006
268,268_0,COMPLETED,BoTorch,0.283951977089987872560072901251,484,0.542015798575687601790207281738,0.800000000000000044408920985006
269,269_0,COMPLETED,BoTorch,0.276462165436722084876919325325,469,0.570047933644828752619559963932,0.800000000000000044408920985006
270,270_0,COMPLETED,BoTorch,0.293204097367551486108538938424,500,0.119001310869403534309363124066,0.731041902652132313811250696745
271,271_0,COMPLETED,BoTorch,0.264511510078202394247171014285,349,0.928681174851617163845673985634,0.800000000000000044408920985006
272,272_0,COMPLETED,BoTorch,0.264511510078202394247171014285,490,0.711339466987648827434043141693,0.800000000000000044408920985006
273,273_0,COMPLETED,BoTorch,0.276462165436722084876919325325,469,0.570039185814783078143364036805,0.799999996382466327382587678585
274,274_0,COMPLETED,BoTorch,0.298050446084370479482572591223,500,0.449057296389054894092396352789,0.800000000000000044408920985006
275,275_0,COMPLETED,BoTorch,0.290340345853067560710769612342,500,0.558757918264831343613252556679,0.747939360968299049581275994569
276,276_0,COMPLETED,BoTorch,0.287751955061130049173812039953,468,0.787010987648970483654409235896,0.791783090219285723421194234106
277,277_0,COMPLETED,BoTorch,0.291662077321290902354178342648,475,0.546273556956913375337592242431,0.779025746004914809716979107179
278,278_0,COMPLETED,BoTorch,0.295737416014979603851031697559,500,0.542080462615144309523884658120,0.692623722192892588012114174489
279,279_0,COMPLETED,BoTorch,0.278224474061019910742231786571,447,0.899986609472720244795596045151,0.800000000000000044408920985006
280,280_0,COMPLETED,BoTorch,0.296233065315563370845097779238,452,0.605195164894083759143939005298,0.782081064253801194752213632455
281,281_0,COMPLETED,BoTorch,0.285934574292322940536337227968,500,0.710666496069529474155501702626,0.800000000000000044408920985006
282,282_0,COMPLETED,BoTorch,0.296453353893600612956049644708,463,0.466763662967187298313831433916,0.800000000000000044408920985006
283,283_0,COMPLETED,BoTorch,0.276737526159268609760033541534,344,0.970965635780379643371418296738,0.800000000000000044408920985006
284,284_0,COMPLETED,BoTorch,0.293093953078532920564214236947,500,1.000000000000000000000000000000,0.500000000000000000000000000000
285,285_0,COMPLETED,BoTorch,0.288743253662297583161944203312,362,0.904783247603351381549430243467,0.800000000000000044408920985006
286,286_0,COMPLETED,BoTorch,0.284778059257627447209415549878,353,0.935047208092617987418293523660,0.781619565752822653692533094727
287,287_0,COMPLETED,BoTorch,0.289954840841502359261028232140,500,0.869717856049883164537561697216,0.500000000000000000000000000000
288,288_0,COMPLETED,BoTorch,0.289954840841502359261028232140,500,0.869727479016347282403387453087,0.500000000000000000000000000000
289,289_0,COMPLETED,BoTorch,0.291386716598744377471064126439,500,0.808222844830971176577349979198,0.602645215464353900536309538438
290,290_0,COMPLETED,BoTorch,0.288522965084260341050992337841,402,0.828145117292187205038089814479,0.500000000000000000000000000000
291,291_0,COMPLETED,BoTorch,0.293754818812644535874767370842,500,0.942351149349494932039306149818,0.503186307146251676769566074654
292,292_0,COMPLETED,BoTorch,0.289073686529353501839523232775,457,0.974183312275675339364511273743,0.500000000000000000000000000000
293,293_0,COMPLETED,BoTorch,0.289404119396409309494799799722,500,0.921327802328761102934606697090,0.557325846917591016804749415314
294,294_0,COMPLETED,BoTorch,0.289789624407974399922238717409,418,1.000000000000000000000000000000,0.734837078255498710177562315948
295,295_0,COMPLETED,BoTorch,0.286815728604471908980144689849,254,0.100000000000000005551115123126,0.800000000000000044408920985006
296,296_0,COMPLETED,BoTorch,0.288798325806806865934106554050,456,0.969985726895343347564448777121,0.500567422951536222797130903928
297,297_0,COMPLETED,BoTorch,0.293314241656570051652863639902,334,0.514786495187640080750668403198,0.800000000000000044408920985006
298,298_0,COMPLETED,BoTorch,0.295021478136358594746013750409,371,0.802047668324960216779118127306,0.500326266852395340478665275441
299,299_0,COMPLETED,BoTorch,0.294801189558321352635061884939,402,0.828175035233876144502573879436,0.500002483116878781999048442231
300,300_0,COMPLETED,BoTorch,0.287641810772111483629487338476,50,1.000000000000000000000000000000,0.800000000000000044408920985006
301,301_0,COMPLETED,BoTorch,0.280427359841392220829447978758,395,0.878371272819775117390861396416,0.800000000000000044408920985006
302,302_0,COMPLETED,BoTorch,0.291221500165216418132274611708,250,0.999880893261605230293298518518,0.799966775751612901856901771680
303,303_0,COMPLETED,BoTorch,0.285053419980174083114832228603,444,0.804053724192205443443981494056,0.800000000000000044408920985006
304,304_0,COMPLETED,BoTorch,0.278004185482982668631279921101,393,0.908712312931448051855909398000,0.800000000000000044408920985006
305,305_0,COMPLETED,BoTorch,0.275030289679480066666883431026,399,1.000000000000000000000000000000,0.800000000000000044408920985006
306,306_0,COMPLETED,BoTorch,0.290505562286595409027256664558,119,0.706887094618293021497379413631,0.799383409817482903925167647685
307,307_0,COMPLETED,BoTorch,0.263960788633109344480942581868,437,1.000000000000000000000000000000,0.800000000000000044408920985006
308,308_0,COMPLETED,BoTorch,0.284943275691155406548205064610,70,0.781203632255474289536323340144,0.759073324553110717616277725028
309,309_0,COMPLETED,BoTorch,0.278004185482982668631279921101,400,0.730550347859349136214746067708,0.794760142491494181449240841175
310,300_0,COMPLETED,BoTorch,0.288082387928185967851391069416,50,1.000000000000000000000000000000,0.800000000000000044408920985006
311,307_0,COMPLETED,BoTorch,0.277563608326908295431678652676,437,1.000000000000000000000000000000,0.800000000000000044408920985006
312,312_0,COMPLETED,BoTorch,0.277233175459852376754099623213,448,0.898010723511952102526834096352,0.800000000000000044408920985006
313,313_0,COMPLETED,BoTorch,0.286925872893490474524469391326,50,1.000000000000000000000000000000,0.740704797156905292432327314600
314,314_0,COMPLETED,BoTorch,0.278224474061019910742231786571,385,0.946059566970685161813037211687,0.800000000000000044408920985006
315,315_0,COMPLETED,BoTorch,0.278334618350038587308858950564,434,0.822046806520217887559454084112,0.798450131505901383732748399780
316,316_0,COMPLETED,BoTorch,0.282520101332745854350037006952,50,0.999992873823973282831900633028,0.740709432493519392970426906686
317,24_0,COMPLETED,BoTorch,0.278940411939640919847249733721,50,0.100000000000000005551115123126,0.800000000000000044408920985006
318,318_0,COMPLETED,BoTorch,0.264015860777618627253104932606,387,1.000000000000000000000000000000,0.800000000000000044408920985006
319,319_0,COMPLETED,BoTorch,0.282409957043727288805712305475,62,0.115082222306682732670957136634,0.751205196599644242638760260888
320,320_0,COMPLETED,BoTorch,0.275636083269082510227576676698,424,1.000000000000000000000000000000,0.800000000000000044408920985006
321,321_0,COMPLETED,BoTorch,0.278279546205529193514394137310,424,0.842470598010515270281928223994,0.800000000000000044408920985006
322,322_0,COMPLETED,BoTorch,0.285218636413701931431319280819,424,0.842376037895278040323887580598,0.800000000000000044408920985006
323,323_0,COMPLETED,BoTorch,0.264511510078202394247171014285,349,0.984035261259347815432363404398,0.800000000000000044408920985006
324,324_0,COMPLETED,BoTorch,0.266769468003083987106549557211,336,0.933590232477291004364872151200,0.800000000000000044408920985006
325,325_0,COMPLETED,BoTorch,0.287201233616036999407583607535,405,0.798355786996317928760902304930,0.784354885594374606760936785577
326,326_0,COMPLETED,BoTorch,0.274865073245952218350396378810,421,1.000000000000000000000000000000,0.800000000000000044408920985006
327,327_0,COMPLETED,BoTorch,0.274810001101442935578234028071,406,0.890354400847754412495760334423,0.800000000000000044408920985006
328,328_0,COMPLETED,BoTorch,0.299317105408084538353818970791,462,0.479025935772380284660698634980,0.783723759567748645693541220680
329,329_0,COMPLETED,BoTorch,0.280978081286485270595676411176,410,0.802610349451436055900899191329,0.785748504749085086018567380961
330,330_0,COMPLETED,BoTorch,0.278114329772001345197907085094,396,0.803045710812418600532680557080,0.779427191600907676161114068236
331,331_0,COMPLETED,BoTorch,0.273708558211256725023474700720,367,0.986626421116001850464272138197,0.800000000000000044408920985006
332,332_0,COMPLETED,BoTorch,0.278830267650622354302925032243,408,0.982111240425600273695749820035,0.800000000000000044408920985006
333,333_0,COMPLETED,BoTorch,0.277673752615926860976003354153,409,0.962433585620513842862067122041,0.800000000000000044408920985006
334,334_0,COMPLETED,BoTorch,0.278554906928075829419810816034,128,0.100000000000000005551115123126,0.799553483664968700495023767871
335,335_0,COMPLETED,BoTorch,0.265007159378786161241237095965,339,0.965146170227359490034757527610,0.800000000000000044408920985006
336,336_0,COMPLETED,BoTorch,0.264511510078202394247171014285,339,1.000000000000000000000000000000,0.800000000000000044408920985006
337,337_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.949585111209304444379597498482,0.800000000000000044408920985006
338,338_0,COMPLETED,BoTorch,0.264401365789183828702846312808,316,1.000000000000000000000000000000,0.800000000000000044408920985006
339,339_0,COMPLETED,BoTorch,0.292488159488930477003520991275,321,0.761778960652554792964963326085,0.669717101590609376060569957190
340,340_0,COMPLETED,BoTorch,0.271836105297940333613837537996,356,1.000000000000000000000000000000,0.800000000000000044408920985006
341,341_0,COMPLETED,BoTorch,0.270018724529133113954060263495,387,0.904023238134499806939459176647,0.800000000000000044408920985006
342,342_0,COMPLETED,BoTorch,0.264511510078202394247171014285,329,0.989850196413605720735517934372,0.796152193813440933745084748807
343,343_0,COMPLETED,BoTorch,0.275801299702610469566366191430,355,0.985445767284867435442663463618,0.800000000000000044408920985006
344,344_0,COMPLETED,BoTorch,0.264511510078202394247171014285,356,0.961258130087330964208547356975,0.800000000000000044408920985006
345,345_0,COMPLETED,BoTorch,0.278609979072585112191973166773,366,1.000000000000000000000000000000,0.800000000000000044408920985006
346,346_0,COMPLETED,BoTorch,0.278169401916510627970069435833,345,1.000000000000000000000000000000,0.800000000000000044408920985006
347,347_0,COMPLETED,BoTorch,0.266218746557990937340321124793,340,1.000000000000000000000000000000,0.800000000000000044408920985006
348,348_0,COMPLETED,BoTorch,0.264070932922128021047569745861,343,1.000000000000000000000000000000,0.800000000000000044408920985006
349,349_0,COMPLETED,BoTorch,0.277783896904945426520328055631,371,0.932349307009185390704431029008,0.800000000000000044408920985006
350,350_0,COMPLETED,BoTorch,0.275856371847119752338528542168,343,1.000000000000000000000000000000,0.784515612435524145595877598680
351,351_0,COMPLETED,BoTorch,0.275415794691045268116624811228,338,0.990292207468124408009657599905,0.800000000000000044408920985006
352,352_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.913256487777031500385760409699,0.800000000000000044408920985006
353,353_0,COMPLETED,BoTorch,0.276186804714175559993805109116,330,1.000000000000000000000000000000,0.800000000000000044408920985006
354,354_0,COMPLETED,BoTorch,0.279821566251789888291057195602,333,1.000000000000000000000000000000,0.800000000000000044408920985006
355,355_0,COMPLETED,BoTorch,0.268366560193853964655374966242,332,0.981558225651133042788387683686,0.800000000000000044408920985006
356,356_0,COMPLETED,BoTorch,0.275030289679480066666883431026,349,0.948855759533783649217753008998,0.800000000000000044408920985006
357,357_0,COMPLETED,BoTorch,0.277012886881815134643147757743,348,0.961062590450701081934425928921,0.800000000000000044408920985006
358,358_0,COMPLETED,BoTorch,0.274369423945368451356330297131,349,1.000000000000000000000000000000,0.800000000000000044408920985006
359,359_0,COMPLETED,BoTorch,0.264511510078202394247171014285,337,1.000000000000000000000000000000,0.800000000000000044408920985006
360,360_0,COMPLETED,BoTorch,0.265833241546425846912882207107,303,0.958683511489446082975973695284,0.800000000000000044408920985006
361,361_0,COMPLETED,BoTorch,0.277123031170833811209774921736,363,0.866583358738276965382851813047,0.800000000000000044408920985006
362,362_0,COMPLETED,BoTorch,0.274865073245952218350396378810,335,1.000000000000000000000000000000,0.787408843480241760204307865934
363,363_0,COMPLETED,BoTorch,0.278665051217094394964135517512,352,1.000000000000000000000000000000,0.793670438510651221619696116250
364,364_0,COMPLETED,BoTorch,0.264511510078202394247171014285,343,0.983673936120684100359312651563,0.800000000000000044408920985006
365,365_0,COMPLETED,BoTorch,0.278609979072585112191973166773,339,0.981389278196067804671542944561,0.800000000000000044408920985006
366,366_0,COMPLETED,BoTorch,0.274534640378896299672817349347,310,1.000000000000000000000000000000,0.798703991220894859992540659732
367,367_0,COMPLETED,BoTorch,0.274754928956933541783769214817,363,0.874871131545547742192070472811,0.800000000000000044408920985006
368,368_0,COMPLETED,BoTorch,0.277838969049454820314792868885,345,0.947390430355685042584923394315,0.800000000000000044408920985006
369,369_0,COMPLETED,BoTorch,0.284337482101553074009814281453,50,0.100000000000000005551115123126,0.619975147175153340484143882350
370,370_0,COMPLETED,BoTorch,0.287421522194074241518535473006,337,1.000000000000000000000000000000,0.777112613027567400436623756832
371,371_0,COMPLETED,BoTorch,0.292212798766383952120406775066,321,0.907515187698901826252040336840,0.754975537278088637549444683827
372,372_0,COMPLETED,BoTorch,0.264511510078202394247171014285,329,0.984936700452129887395358309732,0.800000000000000044408920985006
373,373_0,COMPLETED,BoTorch,0.278995484084150202619412084459,321,1.000000000000000000000000000000,0.800000000000000044408920985006
374,374_0,COMPLETED,BoTorch,0.276847670448287286326660705527,349,0.902959480871610931096427066223,0.800000000000000044408920985006
375,375_0,COMPLETED,BoTorch,0.276076660425156994449480407638,334,0.961390718435399471708535656944,0.800000000000000044408920985006
376,376_0,COMPLETED,BoTorch,0.278665051217094394964135517512,352,1.000000000000000000000000000000,0.800000000000000044408920985006
377,377_0,COMPLETED,BoTorch,0.282299812754708723261387603998,328,0.972119922431419980490829857445,0.800000000000000044408920985006
378,378_0,COMPLETED,BoTorch,0.264511510078202394247171014285,349,0.969779718485501041058682858420,0.800000000000000044408920985006
379,379_0,COMPLETED,BoTorch,0.277178103315343093981937272474,358,0.972177304922462792724502378405,0.800000000000000044408920985006
380,380_0,COMPLETED,BoTorch,0.282244740610199329466922790743,166,0.171053403606766074585010528608,0.623142828047839048011269369454
381,381_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.991085614532080994010243557568,0.800000000000000044408920985006
382,382_0,COMPLETED,BoTorch,0.278609979072585112191973166773,407,1.000000000000000000000000000000,0.800000000000000044408920985006
383,383_0,COMPLETED,BoTorch,0.277178103315343093981937272474,166,0.171149750162323199820235686275,0.623159477146704721128855908319
384,384_0,COMPLETED,BoTorch,0.265172375812314120580026610696,322,0.962743576366238640318329089496,0.800000000000000044408920985006
385,385_0,COMPLETED,BoTorch,0.275250578257517308777835296496,358,0.931070939664085051035158357990,0.800000000000000044408920985006
386,386_0,COMPLETED,BoTorch,0.273323053199691634596035783034,360,0.986921747965769924348933272995,0.800000000000000044408920985006
387,387_0,COMPLETED,BoTorch,0.264511510078202394247171014285,354,0.922174370540257859474309043435,0.800000000000000044408920985006
388,388_0,COMPLETED,BoTorch,0.275581011124573227455414325959,396,0.960426822040453154194494800322,0.800000000000000044408920985006
389,389_0,COMPLETED,BoTorch,0.277894041193964103086955219624,343,0.959536667758540517958465443371,0.800000000000000044408920985006
390,390_0,COMPLETED,BoTorch,0.289018614384844108045058419521,398,0.940836702304848948585913603893,0.798559891419931000555720856937
391,391_0,COMPLETED,BoTorch,0.281033153430994553367838761915,353,0.992945998275720942416455727653,0.800000000000000044408920985006
392,392_0,COMPLETED,BoTorch,0.264511510078202394247171014285,338,0.998609985436715308360078324768,0.800000000000000044408920985006
393,393_0,COMPLETED,BoTorch,0.291056283731688458793485096976,257,0.100000000000000005551115123126,0.607358953805776424772489008319
394,394_0,COMPLETED,BoTorch,0.267099900870139905784128586674,336,0.971126349658125942454489631928,0.800000000000000044408920985006
395,395_0,COMPLETED,BoTorch,0.278334618350038587308858950564,359,0.883746314346583794474554451881,0.800000000000000044408920985006
396,396_0,COMPLETED,BoTorch,0.286705584315453232413517525856,353,1.000000000000000000000000000000,0.757523871265718184986326377839
397,397_0,COMPLETED,BoTorch,0.264621654367221070813798178278,328,1.000000000000000000000000000000,0.800000000000000044408920985006
398,398_0,COMPLETED,BoTorch,0.270018724529133113954060263495,353,1.000000000000000000000000000000,0.800000000000000044408920985006
399,399_0,COMPLETED,BoTorch,0.276131732569666277221642758377,333,0.928557871260234257704269111855,0.800000000000000044408920985006
400,400_0,COMPLETED,BoTorch,0.278059257627492062425744734355,371,0.878165462428932119820501611684,0.800000000000000044408920985006
401,401_0,COMPLETED,BoTorch,0.264181077211146586591894447338,353,0.962648112879937345454095520836,0.800000000000000044408920985006
402,402_0,COMPLETED,BoTorch,0.276407093292212802104756974586,403,1.000000000000000000000000000000,0.800000000000000044408920985006
403,403_0,COMPLETED,BoTorch,0.285989646436832223308499578707,413,0.867455724098970981650325029477,0.788965255439551649274676492496
404,404_0,COMPLETED,BoTorch,0.277949113338473385859117570362,331,0.959104206769312073177502497856,0.800000000000000044408920985006
405,405_0,COMPLETED,BoTorch,0.264511510078202394247171014285,346,0.979611769728258141576304751652,0.800000000000000044408920985006
406,406_0,COMPLETED,BoTorch,0.278169401916510627970069435833,359,0.959884444806337833178133678302,0.799999931958860655001330997038
407,407_0,COMPLETED,BoTorch,0.278334618350038587308858950564,318,1.000000000000000000000000000000,0.800000000000000044408920985006
408,408_0,COMPLETED,BoTorch,0.278665051217094394964135517512,361,0.955926763434453374834731675946,0.799999949035196555868765244668
409,409_0,COMPLETED,BoTorch,0.273818702500275401590101864713,327,0.942653704910634937519375853299,0.800000000000000044408920985006
410,410_0,COMPLETED,BoTorch,0.278334618350038587308858950564,351,0.983008458711519983452831183968,0.800000000000000044408920985006
411,411_0,COMPLETED,BoTorch,0.278169401916510627970069435833,332,1.000000000000000000000000000000,0.800000000000000044408920985006
412,412_0,COMPLETED,BoTorch,0.264511510078202394247171014285,344,1.000000000000000000000000000000,0.800000000000000044408920985006
413,413_0,COMPLETED,BoTorch,0.265888313690935129685044557846,341,1.000000000000000000000000000000,0.800000000000000044408920985006
414,414_0,COMPLETED,BoTorch,0.288743253662297583161944203312,50,1.000000000000000000000000000000,0.500000000000000000000000000000
415,415_0,COMPLETED,BoTorch,0.264511510078202394247171014285,334,0.986497041824346387883792885987,0.800000000000000044408920985006
416,416_0,COMPLETED,BoTorch,0.284447626390571639554138982930,370,0.716832978099981255049044648331,0.799940566570973787818843447894
417,417_0,COMPLETED,BoTorch,0.274865073245952218350396378810,335,0.992281399600686064843557687709,0.800000000000000044408920985006
418,418_0,COMPLETED,BoTorch,0.278334618350038587308858950564,340,0.935073468217983538863791181939,0.800000000000000044408920985006
419,414_0,COMPLETED,BoTorch,0.288357748650732492734505285625,50,1.000000000000000000000000000000,0.500000000000000000000000000000
420,420_0,COMPLETED,BoTorch,0.278224474061019910742231786571,346,1.000000000000000000000000000000,0.800000000000000044408920985006
421,421_0,COMPLETED,BoTorch,0.277894041193964103086955219624,413,0.830726048070937417300285687816,0.794418931845575659878022634075
422,422_0,COMPLETED,BoTorch,0.289238902962881350156010284991,289,0.807764866203070308614542227588,0.799631746672094023686838681897
423,423_0,COMPLETED,BoTorch,0.264511510078202394247171014285,378,0.893650746997445843966545453441,0.797782324720174074172973632812
424,424_0,COMPLETED,BoTorch,0.291827293754818861692967857380,485,0.377073714230209633413437586569,0.758561083581298589706420898438
425,425_0,COMPLETED,BoTorch,0.302621434078643058995794490329,373,0.836787578929215625223037022806,0.683350554481148786400979133759
426,426_0,COMPLETED,BoTorch,0.289844696552483793716703530663,324,0.776717238221317551882805219066,0.788401307910680859691865407513
427,427_0,COMPLETED,BoTorch,0.282575173477255248144501820207,68,0.227466815151274204254150390625,0.646022873278707221444960850931
428,428_0,COMPLETED,BoTorch,0.274975217534970783894721080287,185,0.526101604010909773556647905934,0.640938762295991226736191492819
429,429_0,COMPLETED,BoTorch,0.272552043176561342718855485145,180,0.919039545115083456039428710938,0.642256155516952365047700368450
430,430_0,COMPLETED,BoTorch,0.280978081286485270595676411176,135,0.988373387511819578854499468434,0.617067003436386540826674718119
431,431_0,COMPLETED,BoTorch,0.287696882916620766401649689215,50,0.554376172912027120887046294229,0.500000000000000000000000000000
432,432_0,COMPLETED,BoTorch,0.296067848882035411506308264507,379,0.589864200726151532983010383759,0.637542753200978085104111414694
433,433_0,COMPLETED,BoTorch,0.268531776627381923994164480973,313,0.756362622044980503765998491872,0.782387177832424729473359548138
434,434_0,COMPLETED,BoTorch,0.287201233616036999407583607535,348,0.832218886818592240572911578056,0.793625532742846306888395702117
435,435_0,COMPLETED,BoTorch,0.284557770679590316120766146923,381,0.909182007052004359515251508128,0.761303404252976267940766774700
436,436_0,COMPLETED,BoTorch,0.284502698535080922326301333669,393,0.960436632484197638781608930003,0.784294681064784615642793141888
437,437_0,COMPLETED,BoTorch,0.275746227558101075771901378175,350,1.000000000000000000000000000000,0.800000000000000044408920985006
438,438_0,COMPLETED,BoTorch,0.279270844806696727502526300668,92,0.833984427433460906442519444681,0.774533524829894348684433680319
439,439_0,COMPLETED,BoTorch,0.279656349818261928952267680870,328,0.821081615705043121877793055319,0.789951575547456763537468305003
440,440_0,COMPLETED,BoTorch,0.278830267650622354302925032243,65,0.187412137351930130346744363123,0.515699910372495629040656694997
441,441_0,COMPLETED,BoTorch,0.281033153430994553367838761915,211,0.564800202194601363991921516572,0.753880960494279883654655805003
442,442_0,COMPLETED,BoTorch,0.288963542240334825272896068782,403,0.540310607943683907095078211569,0.680518964864313624651970258128
443,443_0,COMPLETED,BoTorch,0.269523075228549346959994181816,368,0.756516205570551436565551739477,0.795682962196360699103081515204
444,444_0,COMPLETED,BoTorch,0.278720123361603677736297868250,309,0.837765400856733344348015180003,0.788302881177514791488647460938
445,445_0,COMPLETED,BoTorch,0.278830267650622354302925032243,179,0.770974516402930021286010742188,0.685794231854379265911347829388
446,446_0,COMPLETED,BoTorch,0.289128758673862784611685583513,497,0.434020082745701141213601204072,0.516020196303725198205825108744
447,447_0,COMPLETED,BoTorch,0.300198259720233506797626432672,316,0.673510281927883647234978070628,0.796794812195003032684326171875
448,448_0,COMPLETED,BoTorch,0.267375261592686430667242802883,375,0.834379109460860468594489702809,0.796353137586265802383422851562
449,449_0,COMPLETED,BoTorch,0.281859235598634239039483873057,118,0.996667972207069374768195757497,0.641122202202677682336684483744
450,450_0,COMPLETED,BoTorch,0.280262143407864261490658464027,86,0.334626543708145596234260210622,0.751033002696931406561020594381
451,451_0,COMPLETED,BoTorch,0.287751955061130049173812039953,300,0.931941129732877016067504882812,0.772496823500841922616189094697
452,452_0,COMPLETED,BoTorch,0.293589602379116687558280318626,212,0.197693394869565969296232310626,0.734770802967250391546372156881
453,453_0,COMPLETED,BoTorch,0.294030179535191060757881587051,330,0.183395398594439040795833761877,0.719966177362948656082153320312
454,454_0,COMPLETED,BoTorch,0.277949113338473385859117570362,348,0.990078176118527553128956242290,0.800000000000000044408920985006
455,455_0,COMPLETED,BoTorch,0.275085361823989460461348244280,395,0.935011040614540434035006910563,0.773092241958079351427102210437
456,456_0,COMPLETED,BoTorch,0.277398391893380336092889137944,364,0.954862922019846016574717850744,0.800000000000000044408920985006
457,457_0,COMPLETED,BoTorch,0.275250578257517308777835296496,372,0.983954708022317992011096521310,0.800000000000000044408920985006
458,458_0,COMPLETED,BoTorch,0.264511510078202394247171014285,338,1.000000000000000000000000000000,0.800000000000000044408920985006
459,459_0,COMPLETED,BoTorch,0.274589712523405693467282162601,367,0.911286600830005233397912434157,0.800000000000000044408920985006
460,460_0,COMPLETED,BoTorch,0.264181077211146586591894447338,346,0.985968012036414820364882416470,0.800000000000000044408920985006
461,461_0,COMPLETED,BoTorch,0.278114329772001345197907085094,341,0.978491497777593366436121868901,0.800000000000000044408920985006
462,462_0,COMPLETED,BoTorch,0.265833241546425846912882207107,303,0.959985816255465307378358374990,0.800000000000000044408920985006
463,463_0,COMPLETED,BoTorch,0.264511510078202394247171014285,398,1.000000000000000000000000000000,0.800000000000000044408920985006
464,464_0,COMPLETED,BoTorch,0.274975217534970783894721080287,354,0.937588310446198391900907154195,0.800000000000000044408920985006
465,465_0,COMPLETED,BoTorch,0.281143297720013229934465925908,380,1.000000000000000000000000000000,0.782510078290110655530043004546
466,466_0,COMPLETED,BoTorch,0.277453464037889618865051488683,337,0.945919677778365386444647811004,0.800000000000000044408920985006
467,467_0,COMPLETED,BoTorch,0.264511510078202394247171014285,341,0.966764598122828111570470355218,0.800000000000000044408920985006
468,468_0,COMPLETED,BoTorch,0.276352021147703519332594623847,348,1.000000000000000000000000000000,0.800000000000000044408920985006
469,469_0,COMPLETED,BoTorch,0.264511510078202394247171014285,315,1.000000000000000000000000000000,0.800000000000000044408920985006
470,470_0,COMPLETED,BoTorch,0.274644784667914976239444513340,356,0.930059076473119139549794454069,0.792409512493759571327700541588
471,471_0,COMPLETED,BoTorch,0.264511510078202394247171014285,345,0.940634984371485849408145440975,0.800000000000000044408920985006
472,472_0,COMPLETED,BoTorch,0.289073686529353501839523232775,163,0.561638532282869973322192436171,0.665457099752961189764732807816
473,473_0,COMPLETED,BoTorch,0.266824540147593380901014370465,327,1.000000000000000000000000000000,0.800000000000000044408920985006
474,474_0,COMPLETED,BoTorch,0.274920145390461501122558729548,309,1.000000000000000000000000000000,0.800000000000000044408920985006
475,475_0,COMPLETED,BoTorch,0.264015860777618627253104932606,340,0.948333643678593407599919373752,0.800000000000000044408920985006
476,476_0,COMPLETED,BoTorch,0.281969379887652804583808574534,154,0.527191479534418139252238688641,0.671666047511963903993148505833
477,477_0,COMPLETED,BoTorch,0.266163674413481654568158774055,370,1.000000000000000000000000000000,0.800000000000000044408920985006
478,478_0,COMPLETED,BoTorch,0.279491133384733969613478166139,308,0.997367845445341294485785965662,0.800000000000000044408920985006
479,479_0,COMPLETED,BoTorch,0.274699856812424259011606864078,327,0.944162466459332327950448870979,0.800000000000000044408920985006
480,480_0,COMPLETED,BoTorch,0.277343319748871053320726787206,432,1.000000000000000000000000000000,0.800000000000000044408920985006
481,481_0,COMPLETED,BoTorch,0.278389690494547870081021301303,331,1.000000000000000000000000000000,0.800000000000000044408920985006
482,482_0,COMPLETED,BoTorch,0.279215772662187444730363949930,186,0.462209141972206860593530564074,0.693832919467097797294741212681
483,483_0,COMPLETED,BoTorch,0.279491133384733969613478166139,308,0.997372926182211405610189558502,0.800000000000000044408920985006
484,484_0,COMPLETED,BoTorch,0.269963652384623831181897912757,380,0.928896930401370002350347476749,0.800000000000000044408920985006
485,485_0,COMPLETED,BoTorch,0.273763630355766007795637051458,375,1.000000000000000000000000000000,0.800000000000000044408920985006
486,486_0,COMPLETED,BoTorch,0.288192532217204533395715770894,139,0.533862741867790924210623870749,0.683835514797203170012096506980
487,487_0,COMPLETED,BoTorch,0.280096926974336413174171411811,149,0.928959041528482298843982789549,0.594357399350710746688264407567
488,488_0,COMPLETED,BoTorch,0.277949113338473385859117570362,346,0.952379497985298306694801340200,0.800000000000000044408920985006
489,489_0,COMPLETED,BoTorch,0.278169401916510627970069435833,336,0.984720799814420288953442650381,0.800000000000000044408920985006
490,490_0,COMPLETED,BoTorch,0.288247604361713816167878121632,370,0.971128635496970149532103278034,0.800000000000000044408920985006
491,491_0,COMPLETED,BoTorch,0.274810001101442935578234028071,336,1.000000000000000000000000000000,0.800000000000000044408920985006
492,242_0,COMPLETED,BoTorch,0.264511510078202394247171014285,392,1.000000000000000000000000000000,0.800000000000000044408920985006
493,493_0,COMPLETED,BoTorch,0.264511510078202394247171014285,348,0.935429293320749422058213440323,0.800000000000000044408920985006
494,494_0,COMPLETED,BoTorch,0.280151999118845695946333762549,373,0.960964076871820616077002341626,0.800000000000000044408920985006
495,358_0,COMPLETED,BoTorch,0.274369423945368451356330297131,349,1.000000000000000000000000000000,0.800000000000000044408920985006
496,496_0,COMPLETED,BoTorch,0.277563608326908295431678652676,334,1.000000000000000000000000000000,0.800000000000000044408920985006
497,497_0,COMPLETED,BoTorch,0.264511510078202394247171014285,328,0.988131727106894564016670301498,0.800000000000000044408920985006
498,498_0,COMPLETED,BoTorch,0.277012886881815134643147757743,362,1.000000000000000000000000000000,0.800000000000000044408920985006
499,499_0,COMPLETED,BoTorch,0.285934574292322940536337227968,240,0.615423200093209721295295366872,0.793603436648845694811882367503
500,500_0,COMPLETED,BoTorch,0.264731798656239636358122879756,331,0.989621768114827493612040143489,0.800000000000000044408920985006
501,501_0,RUNNING,BoTorch,,346,0.967961020360862511857646950375,0.800000000000000044408920985006
502,502_0,RUNNING,BoTorch,,363,0.983294949103297177828153508017,0.800000000000000044408920985006
503,503_0,RUNNING,BoTorch,,360,1.000000000000000000000000000000,0.800000000000000044408920985006
504,504_0,RUNNING,BoTorch,,352,0.932941605873227164380523390719,0.800000000000000044408920985006
505,505_0,RUNNING,BoTorch,,367,0.943557581308920090279457326687,0.800000000000000044408920985006
</pre>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> CPU/RAM-Usage (main)</h1>
<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727302144,475.390625,34.6
1727302144,475.421875,33.3
1727302144,475.421875,34.6
1727302144,475.421875,25.7
1727302144,475.421875,39.5
1727302144,475.421875,34.4
1727302144,475.421875,35.3
1727302192,481.39453125,35.0
1727302192,481.39453125,40.9
1727302192,481.39453125,34.0
1727302192,481.39453125,35.0
1727302194,481.39453125,34.7
1727302194,481.39453125,38.6
1727302194,481.39453125,33.0
1727302194,481.39453125,37.5
1727302196,481.3984375,34.8
1727302196,481.3984375,37.0
1727302197,481.3984375,37.1
1727302197,481.3984375,26.2
1727302200,483.55859375,35.4
1727302200,483.55859375,40.0
1727302200,483.55859375,33.3
1727302200,483.55859375,45.5
1727302203,483.55859375,35.1
1727302203,483.55859375,25.7
1727302203,483.55859375,36.9
1727302203,483.55859375,30.6
1727302205,483.57421875,35.0
1727302205,483.57421875,29.3
1727302205,483.57421875,38.0
1727302205,483.57421875,31.4
1727302207,483.57421875,34.8
1727302207,483.57421875,39.1
1727302207,483.57421875,35.7
1727302207,483.57421875,25.0
1727302210,483.58203125,34.8
1727302210,483.58203125,40.0
1727302210,483.58203125,32.1
1727302210,483.58203125,43.2
1727302212,483.58203125,34.6
1727302212,483.58203125,31.5
1727302212,483.58203125,35.3
1727302212,483.58203125,36.2
1727302214,483.58203125,34.7
1727302214,483.58203125,25.7
1727302214,483.58203125,39.0
1727302214,483.58203125,27.3
1727302216,483.58203125,34.8
1727302216,483.58203125,40.9
1727302216,483.58203125,32.4
1727302216,483.58203125,40.9
1727302219,483.5859375,35.5
1727302219,483.5859375,42.2
1727302219,483.5859375,39.8
1727302219,483.5859375,29.4
1727302221,483.5859375,37.8
1727302221,483.5859375,31.6
1727302221,483.5859375,40.0
1727302221,483.5859375,27.3
1727302223,483.5859375,37.8
1727302223,483.5859375,27.3
1727302223,483.5859375,41.4
1727302223,483.5859375,30.3
1727302226,483.5859375,37.8
1727302226,483.5859375,26.5
1727302226,483.5859375,39.0
1727302226,483.5859375,45.5
1727302229,483.703125,35.7
1727302229,483.703125,22.9
1727302229,483.703125,35.8
1727302229,483.703125,27.8
1727302232,483.703125,34.6
1727302232,483.703125,39.5
1727302232,483.703125,35.8
1727302232,483.703125,26.5
1727302234,483.703125,30.6
1727302234,483.703125,33.3
1727302234,483.703125,28.5
1727302234,483.703125,20.0
1727302236,483.71484375,26.9
1727302236,483.71484375,30.2
1727302236,483.71484375,28.0
1727302236,483.71484375,20.6
1727302362,525.3984375,26.7
1727302362,525.3984375,27.9
1727302362,525.3984375,23.8
1727302362,525.3984375,31.0
1727302523,527.86328125,32.8
1727302523,527.86328125,31.8
1727302523,527.86328125,26.6
1727302523,527.86328125,27.5
1727302725,536.4140625,33.6
1727302725,536.4140625,27.0
1727302725,536.4140625,35.1
1727302725,536.4140625,40.0
1727302944,532.51953125,35.0
1727302944,532.51953125,24.3
1727302944,532.51953125,34.6
1727302944,532.51953125,40.9
1727303197,548.3515625,35.0
1727303197,548.3515625,24.3
1727303197,548.3515625,37.5
1727303197,548.3515625,22.9
1727303512,538.046875,30.2
1727303512,538.046875,37.5
1727303512,538.046875,35.7
1727303512,538.046875,26.5
1727303868,552.98828125,33.9
1727303868,552.98828125,40.0
1727303868,552.98828125,35.0
1727303868,552.98828125,37.5
1727304280,559.4375,35.0
1727304280,559.4375,38.6
1727304280,559.4375,35.7
1727304280,559.4375,26.5
1727304811,569.22265625,31.7
1727304811,569.22265625,37.8
1727304811,569.22265625,33.3
1727304811,569.22265625,37.5
1727305428,580.78125,35.0
1727305428,580.78125,42.2
1727305428,580.78125,34.4
1727305428,580.78125,40.5
1727305894,581.64453125,31.1
1727305894,581.64453125,38.0
1727305894,581.64453125,34.5
1727305894,581.64453125,27.0
1727306354,525.79296875,35.0
1727306354,525.79296875,33.3
1727306354,525.79296875,35.5
1727306354,525.79296875,26.5
1727306859,493.34375,32.1
1727306859,493.34375,37.8
1727306859,493.34375,33.2
1727306859,493.34375,41.9
1727307425,492.25,32.1
1727307425,492.25,31.1
1727307425,492.25,27.2
1727307425,492.25,20.0
1727308021,497.4765625,26.1
1727308021,497.4765625,34.1
1727308021,497.4765625,28.0
1727308021,497.4765625,22.6
1727308553,490.39453125,27.0
1727308553,490.39453125,32.5
1727308553,490.39453125,27.8
1727308553,490.39453125,25.8
1727309039,489.03125,22.1
1727309039,489.03125,28.9
1727309039,489.03125,34.6
1727309039,489.03125,41.9
1727309815,501.171875,31.5
1727309815,501.171875,21.6
1727309815,501.171875,28.5
1727309815,501.171875,33.3
1727310539,491.6875,33.5
1727310539,491.6875,32.6
1727310539,491.6875,36.0
1727310539,491.6875,27.8
1727311361,500.1640625,33.4
1727311361,500.1640625,25.0
1727311361,500.1640625,25.2
1727311361,500.1640625,30.2
1727312217,509.046875,32.9
1727312217,509.046875,27.8
1727312217,509.046875,35.5
1727312217,509.046875,35.3
1727313226,468.30078125,33.5
1727313226,468.30078125,37.8
1727313226,468.30078125,35.4
1727313226,468.30078125,41.9
1727314251,474.8828125,32.7
1727314251,474.8828125,34.5
1727314251,474.8828125,34.8
1727314251,474.8828125,37.0
1727315199,489.8671875,33.8
1727315199,489.8671875,37.8
1727315199,489.8671875,34.7
1727315199,489.8671875,25.7
1727316232,472.98046875,32.5
1727316232,472.98046875,41.3
1727316232,472.98046875,35.5
1727316232,472.98046875,26.5
1727317621,480.01171875,31.8
1727317621,480.01171875,28.9
1727317621,480.01171875,25.5
1727317621,480.01171875,30.2
1727319075,513.09765625,33.1
1727319075,513.09765625,28.2
1727319075,513.09765625,26.6
1727319075,513.09765625,19.4
1727320596,505.25390625,31.9
1727320596,505.25390625,34.7
1727320652,505.2578125,34.5
1727320652,505.2578125,41.3
</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>