| n_samples | confidence | feature_proportion | n_clusters | result |
|---|---|---|---|---|
| 100 | 0.25 | 0.113997 | 3 | 0.263726 |
| Name | Type | Lower bound | Upper bound | Values | Type |
|---|---|---|---|---|---|
| n_samples | range | 100 | 1000 | int | |
| confidence | choice | 0.001, 0.005, | |||
| 0.01, 0.025, | |||||
| 0.05, 0.1, | |||||
| 0.25 | |||||
| feature_propo… | range | 0 | 0.2 | float | |
| n_clusters | range | 1 | 4 | int |
| Failed | Succeeded | Running | Total |
|---|---|---|---|
| 37 | 460 | 4 | 501 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,confidence,feature_proportion,n_clusters 0,0_0,COMPLETED,Sobol,0.299364447660395605055327905575,392,0.250000000000000000000000000000,0.125973188877105707339509876874,1 1,1_0,COMPLETED,Sobol,0.310570429329474206880945530429,661,0.010000000000000000208166817117,0.129536976665258402041658314374,1 2,2_0,COMPLETED,Sobol,0.272050168837433714585927191365,124,0.250000000000000000000000000000,0.004527391307055950511739617070,2 3,3_0,COMPLETED,Sobol,0.272345634346357945432259839436,127,0.025000000000000001387778780781,0.188410873524844646453857421875,2 4,4_0,COMPLETED,Sobol,0.316195127834056899374104432354,721,0.100000000000000005551115123126,0.025189650245010852813720703125,1 5,5_0,COMPLETED,Sobol,0.317855764592378187138876910467,861,0.001000000000000000020816681712,0.095915075019001969081067215939,1 6,6_0,COMPLETED,Sobol,0.322983598649300485128321724915,974,0.100000000000000005551115123126,0.002316499501466751011896638701,1 7,7_0,COMPLETED,Sobol,0.308909792571152919116173052316,610,0.100000000000000005551115123126,0.103769115358591082487471624063,2 8,8_0,COMPLETED,Sobol,0.291261456825856246410921812640,335,0.250000000000000000000000000000,0.094662074930965900421142578125,2 9,9_0,COMPLETED,Sobol,0.305904486251808993557688154397,521,0.250000000000000000000000000000,0.175773870013654243127376730627,3 10,10_0,COMPLETED,Sobol,0.289911963338157274705508825718,268,0.025000000000000001387778780781,0.088243950903415691033870871252,2 11,11_0,COMPLETED,Sobol,0.314307766521948916960127462517,703,0.025000000000000001387778780781,0.151831477321684366055265513751,4 12,12_0,COMPLETED,Sobol,0.299522431259044874529706703470,466,0.001000000000000000020816681712,0.134880634583532804660066517499,1 13,13_0,COMPLETED,Sobol,0.320090448625180878927665162337,880,0.100000000000000005551115123126,0.054677539132535458998862765156,2 14,14_0,COMPLETED,Sobol,0.284035214664737134526717454719,218,0.050000000000000002775557561563,0.188544376567006122247249777502,1 15,15_0,COMPLETED,Sobol,0.318912204534491094953807532875,766,0.010000000000000000208166817117,0.025302996300160886938845905547,4 16,16_0,COMPLETED,Sobol,0.314120839363241644015545261936,182,0.001000000000000000020816681712,0.011049367859959602702901726445,4 17,17_0,COMPLETED,Sobol,0.313270622286541233769696646050,717,0.025000000000000001387778780781,0.115910582058131705895931418127,4 18,18_0,COMPLETED,Sobol,0.318783164495899606905027212633,843,0.250000000000000000000000000000,0.087754449248313909359708873126,3 19,19_0,COMPLETED,Sobol,0.279965026531596716807825941942,159,0.100000000000000005551115123126,0.071889520995318895169035045001,4 20,20_0,COMPLETED,BoTorch,0.267884708152436035177856865630,100,0.050000000000000002775557561563,0.177883868649023973995682013083,3 21,21_0,COMPLETED,BoTorch,0.264487457790641533605935364903,100,0.250000000000000000000000000000,0.027738805364601382130729234632,3 22,22_0,COMPLETED,BoTorch,0.265705499276410961329020210542,100,0.250000000000000000000000000000,0.062866928559237936213754949222,2 23,23_0,COMPLETED,BoTorch,0.273271828268210281720484999823,100,0.010000000000000000208166817117,0.200000000000000011102230246252,2 24,24_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 25,25_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 26,26_0,COMPLETED,BoTorch,0.271438736131210833768534484989,100,0.005000000000000000104083408559,0.200000000000000011102230246252,1 27,27_0,COMPLETED,BoTorch,0.270335262904003892359128258249,100,0.100000000000000005551115123126,0.096077322497792383426684637016,2 28,28_0,COMPLETED,BoTorch,0.269007477086348312234065360826,100,0.025000000000000001387778780781,0.200000000000000011102230246252,3 29,29_0,COMPLETED,BoTorch,0.265430535455861038585112510191,100,0.100000000000000005551115123126,0.200000000000000011102230246252,3 30,30_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 31,31_0,COMPLETED,BoTorch,0.268408104196816155173621609720,100,0.050000000000000002775557561563,0.156571684842136849002969256617,2 32,32_0,COMPLETED,BoTorch,0.285082006753497374518246942898,100,0.001000000000000000020816681712,0.200000000000000011102230246252,3 33,33_0,COMPLETED,BoTorch,0.270660877954655099131286988268,100,0.100000000000000005551115123126,0.045550926298050381513249362797,1 34,34_0,COMPLETED,BoTorch,0.267842498794018335495081828412,100,0.050000000000000002775557561563,0.200000000000000011102230246252,4 35,35_0,COMPLETED,BoTorch,0.266488181379643029877968274377,100,0.250000000000000000000000000000,0.057876130083879789856027286987,3 36,36_0,COMPLETED,BoTorch,0.265090448625180941100154541346,100,0.250000000000000000000000000000,0.147416184455075177650584805633,3 37,37_0,COMPLETED,BoTorch,0.270454655089242601562204981747,100,0.100000000000000005551115123126,0.088106778025058174463879367977,2 38,38_0,COMPLETED,BoTorch,0.269813072841292855841288655938,100,0.025000000000000001387778780781,0.171349745133378444927885198013,1 39,39_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,3 40,24_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 41,25_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 42,42_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 43,24_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 44,44_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.017034812548118678787734125990,1 45,25_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 46,46_0,COMPLETED,BoTorch,0.270665701881331433042987555382,100,0.010000000000000000208166817117,0.200000000000000011102230246252,1 47,47_0,COMPLETED,BoTorch,0.265104920405209831812953780172,100,0.250000000000000000000000000000,0.111624761330627708666440867091,2 48,48_0,COMPLETED,BoTorch,0.270619874577906371904134630313,100,0.100000000000000005551115123126,0.034287894684050565508037777818,2 49,49_0,COMPLETED,BoTorch,0.265680173661360319314894695708,100,0.250000000000000000000000000000,0.106217880490681981786593723882,3 50,50_0,COMPLETED,BoTorch,0.265209840810419650303231264843,100,0.250000000000000000000000000000,0.159470336260858297494635849034,2 51,51_0,COMPLETED,BoTorch,0.265921369995176126721503351291,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4 52,52_0,COMPLETED,BoTorch,0.294149782923299518699877808103,308,0.025000000000000001387778780781,0.076951969676878395620889250495,2 53,53_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.068499618636857578790611000841,1 54,54_0,COMPLETED,BoTorch,0.264509165460685036208587916917,100,0.250000000000000000000000000000,0.200000000000000011102230246252,2 55,55_0,COMPLETED,BoTorch,0.279602026049204033242290279304,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1 56,56_0,COMPLETED,BoTorch,0.270036179450072411079020184843,100,0.025000000000000001387778780781,0.199535571062655570084842793221,4 57,57_0,COMPLETED,BoTorch,0.266441148094548996283492670045,100,0.050000000000000002775557561563,0.179968288355066341477339619814,4 58,58_0,COMPLETED,BoTorch,0.292829232995658439442365761352,274,0.025000000000000001387778780781,0.063819841783673739166182770077,3 59,59_0,COMPLETED,BoTorch,0.287747226242161091569471409457,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4 60,60_0,COMPLETED,BoTorch,0.265389532079112422380262614752,100,0.250000000000000000000000000000,0.200000000000000011102230246252,3 61,51_0,COMPLETED,BoTorch,0.264904727448142751633497482544,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4 62,62_0,COMPLETED,BoTorch,0.264904727448142751633497482544,100,0.250000000000000000000000000000,0.157875109277411301844651347892,4 63,63_0,COMPLETED,BoTorch,0.267074288470815268681235465920,100,0.250000000000000000000000000000,0.127902252698476631698198957565,2 64,64_0,COMPLETED,BoTorch,0.279779305354558638363471345656,118,0.001000000000000000020816681712,0.195173412651064803746692177810,2 65,65_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.145585477484363984279625015006,1 66,66_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.147851125674286931754863871902,1 67,67_0,COMPLETED,BoTorch,0.265311143270622329382035786693,100,0.100000000000000005551115123126,0.178017096733771307270899342257,4 68,68_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063038119545012802347194735830,1 69,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 70,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 71,71_0,COMPLETED,BoTorch,0.282743608297153836694803885621,100,0.001000000000000000020816681712,0.095557102229740842336269679436,1 72,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 73,73_0,COMPLETED,BoTorch,0.265877954655089232538500709779,100,0.100000000000000005551115123126,0.200000000000000011102230246252,2 74,74_0,COMPLETED,BoTorch,0.266189097925711548597860200971,100,0.250000000000000000000000000000,0.022821504645745865902384252877,3 75,75_0,COMPLETED,BoTorch,0.277569946936806588588808608620,173,0.250000000000000000000000000000,0.200000000000000011102230246252,4 76,76_0,COMPLETED,BoTorch,0.284845634346357901023338854429,220,0.025000000000000001387778780781,0.171978087245400146354512571634,1 77,77_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.200000000000000011102230246252,1 78,77_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.200000000000000011102230246252,1 79,79_0,COMPLETED,BoTorch,0.284362035697057424776801326516,228,0.025000000000000001387778780781,0.192083004366270820861828383386,1 80,80_0,COMPLETED,BoTorch,0.274657501205981646741349777585,135,0.100000000000000005551115123126,0.200000000000000011102230246252,2 81,81_0,COMPLETED,BoTorch,0.273724071394114809585573766526,105,0.005000000000000000104083408559,0.119407873090501992496825778289,1 82,82_0,COMPLETED,BoTorch,0.266254220935841723338910469465,100,0.250000000000000000000000000000,0.042146187575146314141516512564,2 83,83_0,COMPLETED,BoTorch,0.292783405692233489325815298798,346,0.250000000000000000000000000000,0.071503605591182545220263477859,2 84,75_0,COMPLETED,BoTorch,0.277454172696575018797204847942,173,0.250000000000000000000000000000,0.200000000000000011102230246252,4 85,85_0,COMPLETED,BoTorch,0.282850940665701933163234116364,157,0.050000000000000002775557561563,0.053702779364252509197719120948,3 86,86_0,COMPLETED,BoTorch,0.279430776652194845510734921845,152,0.100000000000000005551115123126,0.023917583569774580887212778180,4 87,87_0,COMPLETED,BoTorch,0.266654606849975883697823064722,100,0.250000000000000000000000000000,0.037505726348380785672986803547,2 88,88_0,COMPLETED,BoTorch,0.279657501205981651182241876086,150,0.050000000000000002775557561563,0.079821518535756086354382432546,2 89,89_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.174884221516155097253530925627,1 90,90_0,COMPLETED,BoTorch,0.269813072841292855841288655938,100,0.025000000000000001387778780781,0.148195435547283954624830926150,1 91,77_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.200000000000000011102230246252,1 92,92_0,COMPLETED,BoTorch,0.265694645441389321049996397051,100,0.250000000000000000000000000000,0.033707657150358803777034921723,2 93,93_0,COMPLETED,BoTorch,0.278112638687891933209073158650,153,0.050000000000000002775557561563,0.071329545044852046831174163799,2 94,94_0,COMPLETED,BoTorch,0.266637723106608826029173542338,100,0.250000000000000000000000000000,0.032172239915852975100651889306,2 95,95_0,COMPLETED,BoTorch,0.295928605885190543212104330451,319,0.001000000000000000020816681712,0.200000000000000011102230246252,4 96,96_0,COMPLETED,BoTorch,0.266509889049686421458318363875,100,0.250000000000000000000000000000,0.017924730352540090699831054621,2 97,97_0,COMPLETED,BoTorch,0.277861794500723569001365831355,166,0.100000000000000005551115123126,0.111611382580355036897579168453,4 98,98_0,COMPLETED,BoTorch,0.267038109020742875365783675079,100,0.250000000000000000000000000000,0.050489515786813543574140794590,4 99,99_0,COMPLETED,BoTorch,0.294187168355040995493254740722,301,0.005000000000000000104083408559,0.080799212261498470910581204407,3 100,100_0,COMPLETED,BoTorch,0.266648576941630466308197355829,100,0.250000000000000000000000000000,0.044482555475510422715590408416,4 101,101_0,COMPLETED,BoTorch,0.287492764109985476927988656826,273,0.025000000000000001387778780781,0.085753487422381785565406175920,1 102,102_0,COMPLETED,BoTorch,0.266125180897250346312432611739,100,0.250000000000000000000000000000,0.033543377052116289671612747725,4 103,103_0,COMPLETED,BoTorch,0.267068258562469851291609757027,100,0.250000000000000000000000000000,0.033440639794442034160582011282,4 104,104_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.036505117536770277653079830316,1 105,105_0,COMPLETED,BoTorch,0.267492764109985570186722725339,100,0.250000000000000000000000000000,0.028830864552846571791100416249,4 106,106_0,COMPLETED,BoTorch,0.292256391702846118896275129373,288,0.010000000000000000208166817117,0.096754763556448367123330456252,2 107,107_0,COMPLETED,BoTorch,0.266766763145200203055651400064,100,0.250000000000000000000000000000,0.023362646599296647831334183820,4 108,108_0,COMPLETED,BoTorch,0.266691992281717360491199997341,100,0.250000000000000000000000000000,0.020726584857856129168185432832,4 109,109_0,COMPLETED,BoTorch,0.278664978292329945652738842909,159,0.100000000000000005551115123126,0.044211268138367058877502557834,3 110,110_0,COMPLETED,BoTorch,0.274739507959478990173352030979,126,0.100000000000000005551115123126,0.059878997487557289458592890696,4 111,111_0,COMPLETED,BoTorch,0.267440906898215091658244091377,100,0.250000000000000000000000000000,0.025656768214582188758488001668,4 112,112_0,COMPLETED,BoTorch,0.265606608779546560228368434764,100,0.250000000000000000000000000000,0.024419600336531684103835715405,4 113,113_0,COMPLETED,BoTorch,0.278607091172214160756936962571,156,0.100000000000000005551115123126,0.027141222999015869599137573687,3 114,114_0,COMPLETED,BoTorch,0.278310419681620846432679172722,152,0.100000000000000005551115123126,0.047694359628426323538530340329,3 115,115_0,COMPLETED,BoTorch,0.265387120115774255424412331195,100,0.250000000000000000000000000000,0.038988677223391096648974496475,4 116,116_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049738440660780827706766160645,1 117,117_0,COMPLETED,BoTorch,0.302309454896285578762160639599,411,0.001000000000000000020816681712,0.140145107403291890868501923251,4 118,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 119,119_0,COMPLETED,BoTorch,0.278637240713941136682763044519,172,0.050000000000000002775557561563,0.200000000000000011102230246252,3 120,120_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.048593689470893541848539598504,1 121,121_0,COMPLETED,BoTorch,0.311116739025566801934985505795,183,0.001000000000000000020816681712,0.035801898679152968640870824402,4 122,122_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.048296241396162453707319173191,1 123,123_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.047665086285069331595298791626,1 124,124_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.047316575317859009552901028428,1 125,125_0,COMPLETED,BoTorch,0.286870477568740955831572136958,211,0.005000000000000000104083408559,0.160722948797047154867456697502,3 126,126_0,COMPLETED,BoTorch,0.292190062711046749654997256584,337,0.005000000000000000104083408559,0.102292789507217193878396699347,3 127,127_0,COMPLETED,BoTorch,0.310909310178485331910280820011,678,0.050000000000000002775557561563,0.128759511771469686847169100474,2 128,128_0,COMPLETED,BoTorch,0.310771828268210370538326969836,182,0.001000000000000000020816681712,0.035995133853017412250974160770,4 129,129_0,FAILED,BoTorch,,230,0.250000000000000000000000000000,0.000000000000000000000000000000,1 130,130_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.045359913357807417344513822854,1 131,131_0,FAILED,BoTorch,,229,0.250000000000000000000000000000,0.000000000000000000000000000000,1 132,132_0,FAILED,BoTorch,,227,0.250000000000000000000000000000,0.000000000000000000000000000000,1 133,133_0,COMPLETED,BoTorch,0.294034008683068059930576509942,352,0.250000000000000000000000000000,0.098168705538623141082510414890,2 134,134_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050315271550182422144459337687,1 135,135_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050766865902727165149599386496,1 136,136_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050504912170116725289581438574,1 137,137_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050970185064206509351070195635,1 138,138_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050614450151675149935925901445,1 139,139_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050138878747595573559525661267,1 140,140_0,COMPLETED,BoTorch,0.289524843222383032603772790026,254,0.010000000000000000208166817117,0.009544117887174069850275692772,3 141,141_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.027417080626232416540322134324,1 142,142_0,COMPLETED,BoTorch,0.284444042450554768208803579910,173,0.001000000000000000020816681712,0.123181387492010754880489287189,3 143,143_0,COMPLETED,BoTorch,0.272988422575976885653403769538,121,0.250000000000000000000000000000,0.032939386489557666293226390053,1 144,144_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050397608925995256157204238434,1 145,145_0,COMPLETED,BoTorch,0.265619874577906367463242531812,100,0.250000000000000000000000000000,0.163236779356049632738745458482,4 146,146_0,COMPLETED,BoTorch,0.280151953690303878730105680006,171,0.100000000000000005551115123126,0.020056208405210197709411801270,2 147,147_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049805387446812503238202651801,1 148,148_0,COMPLETED,BoTorch,0.292800289435600546994464821182,293,0.010000000000000000208166817117,0.075862777938960734847029243610,4 149,149_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.051917039133237842529755567966,1 150,150_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.052258665421560851482851717265,1 151,151_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.052211428481386795708019121776,1 152,152_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.051899044103794193805168077915,1 153,153_0,COMPLETED,BoTorch,0.278017366136034782542196808208,170,0.100000000000000005551115123126,0.017157494960939496941865201052,2 154,154_0,FAILED,BoTorch,,151,0.250000000000000000000000000000,0.000000000000000000000000000000,4 155,155_0,COMPLETED,BoTorch,0.278950795947901619697972819267,171,0.100000000000000005551115123126,0.016893846161754374340935314081,2 156,156_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049652065133151290776680752970,1 157,157_0,COMPLETED,BoTorch,0.264856488181379634561096736434,100,0.250000000000000000000000000000,0.183010858806825327249612200831,3 158,158_0,COMPLETED,BoTorch,0.280827303424987961832925975614,178,0.050000000000000002775557561563,0.070791305551041960875124914310,2 159,159_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.052944212052922060007809079707,1 160,160_0,COMPLETED,BoTorch,0.264006271104679224315248120547,100,0.250000000000000000000000000000,0.189759541032041040287126065778,4 161,161_0,COMPLETED,BoTorch,0.287022431259044807916325225960,235,0.005000000000000000104083408559,0.105382706189895716808457848401,3 162,162_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049088870410681550993103883229,1 163,163_0,COMPLETED,BoTorch,0.277050168837433719026819289866,148,0.050000000000000002775557561563,0.005102395700533231270856759920,2 164,164_0,COMPLETED,BoTorch,0.264944524843222395382724698720,100,0.100000000000000005551115123126,0.198080013733558657484934428794,4 165,165_0,COMPLETED,BoTorch,0.265680173661360319314894695708,100,0.250000000000000000000000000000,0.191124439388942857220854421030,3 166,166_0,COMPLETED,BoTorch,0.278221177038109002133126068657,158,0.250000000000000000000000000000,0.200000000000000011102230246252,1 167,167_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.048862258501669669774614135349,1 168,168_0,COMPLETED,BoTorch,0.287219006271104637662006098253,234,0.050000000000000002775557561563,0.176981218309804899657677879077,1 169,169_0,COMPLETED,BoTorch,0.283358658948383967945972017333,198,0.001000000000000000020816681712,0.200000000000000011102230246252,1 170,170_0,COMPLETED,BoTorch,0.288865171249397034713979337539,139,0.010000000000000000208166817117,0.093497685206801461887948789808,4 171,171_0,COMPLETED,BoTorch,0.280168837433671047421057664906,153,0.005000000000000000104083408559,0.167414708244667392866844579657,1 172,172_0,COMPLETED,BoTorch,0.287132175590931071340605740261,202,0.001000000000000000020816681712,0.145594283020721193544488869520,4 173,173_0,COMPLETED,BoTorch,0.277379401833092176232753445220,162,0.005000000000000000104083408559,0.176090543290137402365758134692,1 174,174_0,COMPLETED,BoTorch,0.304261939218523846939490340446,419,0.001000000000000000020816681712,0.123217977003118275836079931196,4 175,175_0,COMPLETED,BoTorch,0.279623733719247424822640368802,147,0.005000000000000000104083408559,0.161372700373134586593337758131,1 176,176_0,COMPLETED,BoTorch,0.287715870718765032165720185731,204,0.001000000000000000020816681712,0.168326320223342013049361298727,3 177,177_0,COMPLETED,BoTorch,0.284813072841292758141662488924,183,0.001000000000000000020816681712,0.101967054482906424595078931361,3 178,178_0,COMPLETED,BoTorch,0.282455378678244106716022088222,185,0.005000000000000000104083408559,0.174652985088769391319729606948,3 179,179_0,COMPLETED,BoTorch,0.280799565846599152862950177223,144,0.005000000000000000104083408559,0.153040437513289662163629145653,2 180,180_0,COMPLETED,BoTorch,0.287922093584177529734802192252,220,0.001000000000000000020816681712,0.173229805605066261575686326069,2 181,181_0,COMPLETED,BoTorch,0.285477568740955089943156508525,192,0.001000000000000000020816681712,0.186435694528571727079935271831,2 182,182_0,COMPLETED,BoTorch,0.280915340086830722654553937900,177,0.001000000000000000020816681712,0.139001928384399953131378424587,2 183,183_0,COMPLETED,BoTorch,0.285547515677761709618209806649,192,0.001000000000000000020816681712,0.184572013116698935286663640909,2 184,184_0,COMPLETED,BoTorch,0.271438736131210833768534484989,100,0.005000000000000000104083408559,0.124884889729805784175376004441,1 185,185_0,COMPLETED,BoTorch,0.284359623733719257820951042959,194,0.005000000000000000104083408559,0.200000000000000011102230246252,1 186,186_0,COMPLETED,BoTorch,0.279052098408104187754474878602,155,0.005000000000000000104083408559,0.169816536097741915822822988957,1 187,187_0,COMPLETED,BoTorch,0.265887602508441900361901844008,100,0.100000000000000005551115123126,0.183367946131215348026799460968,4 188,188_0,COMPLETED,BoTorch,0.281319343945972022424939495977,184,0.005000000000000000104083408559,0.198297852464462798227629036774,1 189,189_0,COMPLETED,BoTorch,0.324157018813314046212781249778,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4 190,190_0,COMPLETED,BoTorch,0.321632899179932429944983596215,857,0.001000000000000000020816681712,0.078690445868493677505917105464,1 191,191_0,COMPLETED,BoTorch,0.268461167390255717180025385460,111,0.250000000000000000000000000000,0.199369732630930496242172011989,4 192,192_0,COMPLETED,BoTorch,0.286938012542209408550775151525,125,0.010000000000000000208166817117,0.013685528305224049783195283680,3 193,193_0,COMPLETED,BoTorch,0.266368789194404209652589088364,100,0.100000000000000005551115123126,0.182745895072306097084435805300,4 194,194_0,COMPLETED,BoTorch,0.322161119150988883852448907419,976,0.001000000000000000020816681712,0.200000000000000011102230246252,3 195,195_0,COMPLETED,BoTorch,0.323787988422575945257619878248,965,0.001000000000000000020816681712,0.200000000000000011102230246252,4 196,195_0,COMPLETED,BoTorch,0.323238060781476099769804477546,965,0.001000000000000000020816681712,0.200000000000000011102230246252,4 197,197_0,COMPLETED,BoTorch,0.322635069946936803297887763620,965,0.001000000000000000020816681712,0.199020702640858915266619533213,2 198,198_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,4 199,199_0,COMPLETED,BoTorch,0.278070429329474233526298121433,177,0.050000000000000002775557561563,0.026468955384571127287252068072,2 200,189_0,COMPLETED,BoTorch,0.325141099855282167396808290505,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4 201,201_0,COMPLETED,BoTorch,0.305247226242161162623744985467,544,0.050000000000000002775557561563,0.196824549372222490362105418171,2 202,202_0,COMPLETED,BoTorch,0.314370477568740924745327447454,188,0.001000000000000000020816681712,0.022440683374965957053159826273,4 203,203_0,COMPLETED,BoTorch,0.312692957067052579311905446957,187,0.001000000000000000020816681712,0.023391348905394158258497228076,4 204,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 205,205_0,COMPLETED,BoTorch,0.264770863482875040695319057704,100,0.250000000000000000000000000000,0.183832211683872565055608561124,4 206,206_0,COMPLETED,BoTorch,0.319965026531596752334962729947,919,0.001000000000000000020816681712,0.198852693699973381846746178780,1 207,207_0,COMPLETED,BoTorch,0.279013506994693627483172804205,155,0.050000000000000002775557561563,0.012644201833267138326832856876,2 208,42_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 209,209_0,COMPLETED,BoTorch,0.323362276893391253906884230673,920,0.001000000000000000020816681712,0.198834649936369234346500434185,1 210,189_0,COMPLETED,BoTorch,0.324854076218041520895951634884,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4 211,211_0,COMPLETED,BoTorch,0.278528702363724067758710134513,111,0.001000000000000000020816681712,0.117070640942127046635334863822,1 212,212_0,COMPLETED,BoTorch,0.323101784852870221875775769149,976,0.010000000000000000208166817117,0.200000000000000011102230246252,4 213,213_0,COMPLETED,BoTorch,0.281054027978774767504432929854,159,0.025000000000000001387778780781,0.180340861569562305177782945975,2 214,214_0,COMPLETED,BoTorch,0.277169561022672428229896013363,174,0.250000000000000000000000000000,0.188441310756199270626254360650,4 215,215_0,COMPLETED,BoTorch,0.269611673902556692183907216531,107,0.050000000000000002775557561563,0.111728123906690679500997021023,1 216,216_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060642060464390273866186475971,1 217,217_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060225730870675557582227810371,1 218,218_0,FAILED,BoTorch,,569,0.001000000000000000020816681712,0.000000000000000000000000000000,4 219,219_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062580185499531260084893347084,1 220,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 221,221_0,RUNNING,BoTorch,,187,0.250000000000000000000000000000,0.052850737238033990150753993476,1 222,59_0,COMPLETED,BoTorch,0.288771104679208856502725666360,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4 223,223_0,COMPLETED,BoTorch,0.298052339604437999120989388757,375,0.100000000000000005551115123126,0.153831505030393611566097433752,1 224,224_0,COMPLETED,BoTorch,0.301693198263386364033067366108,420,0.250000000000000000000000000000,0.012946567745694659637423207244,2 225,225_0,FAILED,BoTorch,,250,0.250000000000000000000000000000,0.000000000000000000000000000000,1 226,226_0,COMPLETED,BoTorch,0.295326821032320330218112758303,348,0.250000000000000000000000000000,0.090501819572133074265352092880,2 227,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 228,228_0,COMPLETED,BoTorch,0.283795224312590410598033940914,140,0.010000000000000000208166817117,0.041977368993984839895539096233,3 229,229_0,FAILED,BoTorch,,106,0.005000000000000000104083408559,0.000000000000000000000000000000,1 230,230_0,COMPLETED,BoTorch,0.273742161119150950732148430689,100,0.010000000000000000208166817117,0.070078831681142525478200866473,1 231,231_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063828190775983215110578328222,1 232,232_0,COMPLETED,BoTorch,0.289294500723588976498490410449,145,0.010000000000000000208166817117,0.090575529823945233331983217795,4 233,233_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063783024454271863845633561141,1 234,225_0,FAILED,BoTorch,,250,0.250000000000000000000000000000,0.000000000000000000000000000000,1 235,235_0,COMPLETED,BoTorch,0.287575976845151903837916051998,259,0.050000000000000002775557561563,0.011784025878387003813396738394,2 236,236_0,COMPLETED,BoTorch,0.294482633863965226339587388793,350,0.250000000000000000000000000000,0.089586741383260046212022587042,2 237,237_0,FAILED,BoTorch,,260,0.050000000000000002775557561563,0.000000000000000000000000000000,2 238,238_0,COMPLETED,BoTorch,0.292444524843222364296480009216,298,0.005000000000000000104083408559,0.103926612404703050396292951518,3 239,239_0,FAILED,BoTorch,,212,0.025000000000000001387778780781,0.000000000000000000000000000000,1 240,240_0,COMPLETED,BoTorch,0.284140135069946953016994939389,187,0.010000000000000000208166817117,0.018465700310964838770644291799,2 241,241_0,COMPLETED,BoTorch,0.305875542691751101109787214227,296,0.001000000000000000020816681712,0.050740103637683255100032653218,4 242,242_0,FAILED,BoTorch,,249,0.250000000000000000000000000000,0.000000000000000000000000000000,1 243,243_0,COMPLETED,BoTorch,0.278481669078630034164234530181,105,0.001000000000000000020816681712,0.112032745622363760773509966384,1 244,244_0,FAILED,BoTorch,,268,0.250000000000000000000000000000,0.000000000000000000000000000000,1 245,245_0,COMPLETED,BoTorch,0.284956584659913136903242047993,231,0.250000000000000000000000000000,0.025574268507030123442902436182,1 246,246_0,COMPLETED,BoTorch,0.279073806078147579334824968100,118,0.025000000000000001387778780781,0.045409233378626540078570883452,2 247,247_0,FAILED,BoTorch,,261,0.010000000000000000208166817117,0.000000000000000000000000000000,2 248,248_0,FAILED,BoTorch,,255,0.250000000000000000000000000000,0.000000000000000000000000000000,1 249,249_0,COMPLETED,BoTorch,0.290010853835021675806160601496,260,0.010000000000000000208166817117,0.024920782002454670844837636423,3 250,248_0,FAILED,BoTorch,,255,0.250000000000000000000000000000,0.000000000000000000000000000000,1 251,251_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062382576859791719070535975789,1 252,252_0,COMPLETED,BoTorch,0.293769898697539777465692623082,316,0.250000000000000000000000000000,0.115827191472457152743302799536,1 253,253_0,FAILED,BoTorch,,258,0.250000000000000000000000000000,0.000000000000000000000000000000,1 254,254_0,COMPLETED,BoTorch,0.283778340569223352929384418530,236,0.250000000000000000000000000000,0.029641443862270928227831134905,1 255,255_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 256,256_0,FAILED,BoTorch,,258,0.250000000000000000000000000000,0.000000000000000000000000000000,4 257,257_0,COMPLETED,BoTorch,0.280135069946936821061456157622,185,0.250000000000000000000000000000,0.024488023150332334509915455101,2 258,258_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062191181517896244879128886396,1 259,259_0,COMPLETED,BoTorch,0.291406174626145708650426513486,275,0.250000000000000000000000000000,0.200000000000000011102230246252,4 260,260_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.061789307872297527524096949492,1 261,261_0,COMPLETED,BoTorch,0.284400627110467874025800938398,232,0.050000000000000002775557561563,0.194390432577510707812251666837,1 262,262_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064297448962912340242326081352,1 263,263_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062809465903159777955266918070,1 264,264_0,COMPLETED,BoTorch,0.280564399421128762845967230533,181,0.010000000000000000208166817117,0.031742202679727507530493824106,1 265,265_0,COMPLETED,BoTorch,0.285085624698504624952022368234,226,0.050000000000000002775557561563,0.175189593122712150563557997884,2 266,266_0,COMPLETED,BoTorch,0.293098166907863055818950215325,316,0.250000000000000000000000000000,0.086508836994181007029602881175,1 267,267_0,COMPLETED,BoTorch,0.285050651230101315114495719172,222,0.100000000000000005551115123126,0.152520216165787036999290648964,2 268,268_0,COMPLETED,BoTorch,0.295255668113844627065134318400,349,0.250000000000000000000000000000,0.086369363620642902734658719055,2 269,269_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.071254519137674068862686738157,1 270,270_0,COMPLETED,BoTorch,0.275168837433671042980165566405,129,0.050000000000000002775557561563,0.017891866121939138667418234263,2 271,271_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.069851468866725219264424140420,1 272,272_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.070360318576455901928845548809,1 273,273_0,COMPLETED,BoTorch,0.286810178485287003979919973062,239,0.050000000000000002775557561563,0.184812060794586452416865540727,3 274,274_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.069608187028502729876144883292,1 275,275_0,COMPLETED,BoTorch,0.267978774722624213389110536809,102,0.100000000000000005551115123126,0.147233929973952792913394205243,2 276,276_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.066981210791419901195986597031,1 277,277_0,COMPLETED,BoTorch,0.287477086348287502737264276220,272,0.250000000000000000000000000000,0.060280831158161164717856905781,4 278,278_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.066774721653305099677844225425,1 279,279_0,FAILED,BoTorch,,545,0.001000000000000000020816681712,0.000000000000000000000000000000,1 280,280_0,COMPLETED,BoTorch,0.290629522431259057491104158544,327,0.050000000000000002775557561563,0.031841886265239142950189688008,4 281,281_0,COMPLETED,BoTorch,0.275183309213699933692964805232,100,0.050000000000000002775557561563,0.073456958530075056046371173579,2 282,282_0,FAILED,BoTorch,,562,0.001000000000000000020816681712,0.000000000000000000000000000000,1 283,283_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.067291773271187099725310076792,1 284,284_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.068106368089074095784063445080,1 285,285_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.067713032974790740570192326686,1 286,286_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.067970476920417952082509316369,1 287,287_0,COMPLETED,BoTorch,0.276031114327062176982963137561,147,0.050000000000000002775557561563,0.029684903280067117398166587350,1 288,288_0,COMPLETED,BoTorch,0.296496623251326529846494395315,337,0.250000000000000000000000000000,0.198653219666456104519625114335,3 289,289_0,COMPLETED,BoTorch,0.290800771828268245222659516003,290,0.100000000000000005551115123126,0.186132790415169169229159251699,4 290,290_0,COMPLETED,BoTorch,0.303004100337674886489480741147,448,0.050000000000000002775557561563,0.133432193286716932467683704999,3 291,291_0,COMPLETED,BoTorch,0.300605402797877507836687982490,461,0.250000000000000000000000000000,0.119272310341075660211629383411,2 292,292_0,COMPLETED,BoTorch,0.288464785335262874355066742282,279,0.050000000000000002775557561563,0.027026415231706832353886227338,2 293,293_0,COMPLETED,BoTorch,0.287159913169319880310581538652,242,0.100000000000000005551115123126,0.156560805597633245778155242078,3 294,294_0,COMPLETED,BoTorch,0.274887843704775702846632157161,100,0.050000000000000002775557561563,0.066374546291804933928482057581,2 295,295_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065952769341442632478056395939,1 296,296_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065957181510519144462811880203,1 297,297_0,COMPLETED,BoTorch,0.266480945489628529010417423706,100,0.250000000000000000000000000000,0.090103966775734373184647552080,3 298,298_0,COMPLETED,BoTorch,0.283187409551374780214416659874,186,0.010000000000000000208166817117,0.070589791280776165161015001104,1 299,299_0,COMPLETED,BoTorch,0.287364930053063183379435940878,272,0.025000000000000001387778780781,0.005747990976141181471148478010,3 300,300_0,COMPLETED,BoTorch,0.301029908345393115709498488286,477,0.025000000000000001387778780781,0.038225660453959808826596145082,1 301,301_0,COMPLETED,BoTorch,0.288591413410516195447996778967,171,0.005000000000000000104083408559,0.099360480747154478198268634515,3 302,302_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064052036270882531376891222408,1 303,303_0,COMPLETED,BoTorch,0.313490110950313538573652749619,755,0.010000000000000000208166817117,0.104958781868517328494938567474,4 304,304_0,COMPLETED,BoTorch,0.308582971538832628866089180519,612,0.005000000000000000104083408559,0.064217537837185381666138539458,2 305,305_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064231553822249640162134198818,1 306,306_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064451514504983584141761809860,1 307,307_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064668571264303489898317423012,1 308,308_0,COMPLETED,BoTorch,0.308116256632899210288201174990,538,0.010000000000000000208166817117,0.107040308120906327293830884173,3 309,309_0,COMPLETED,BoTorch,0.299108779546550906935920011165,386,0.025000000000000001387778780781,0.037533670135556418401279188402,1 310,310_0,COMPLETED,BoTorch,0.267342016401350690557592315599,100,0.250000000000000000000000000000,0.064316705885661087926941092974,2 311,311_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064204083499126984868610179547,1 312,312_0,COMPLETED,BoTorch,0.265508924264351131583339338249,100,0.250000000000000000000000000000,0.161606480231661742141824333885,3 313,313_0,COMPLETED,BoTorch,0.264781717317896791996645333711,100,0.250000000000000000000000000000,0.121070516694132851442944343034,4 314,314_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063337250252014129503486117301,1 315,315_0,COMPLETED,BoTorch,0.313241678726483341321795705881,100,0.001000000000000000020816681712,0.000000025935531194014214255653,4 316,316_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063543069480409913873941718521,1 317,317_0,COMPLETED,BoTorch,0.280318379160636732549960470351,141,0.010000000000000000208166817117,0.071473442489243865782810871679,2 318,318_0,COMPLETED,BoTorch,0.276101061263868796658016435686,145,0.100000000000000005551115123126,0.151033775508403800280632367503,3 319,319_0,COMPLETED,BoTorch,0.265844187168355006178899202496,100,0.250000000000000000000000000000,0.119595762080195497367718360238,4 320,320_0,COMPLETED,BoTorch,0.265735648818137937254846292490,100,0.250000000000000000000000000000,0.122461282514456565317928493641,4 321,321_0,COMPLETED,BoTorch,0.309163048721659450279730663169,665,0.250000000000000000000000000000,0.042090272349207133451010065528,1 322,322_0,COMPLETED,BoTorch,0.279647853352629094381143204373,169,0.100000000000000005551115123126,0.111696985327760761608217876528,3 323,323_0,COMPLETED,BoTorch,0.264963820549927620007224504661,100,0.250000000000000000000000000000,0.121516274124574025217171424629,4 324,324_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063133473748510374190523464222,1 325,325_0,COMPLETED,BoTorch,0.267262421611191514081440345763,100,0.050000000000000002775557561563,0.200000000000000011102230246252,1 326,326_0,COMPLETED,BoTorch,0.271025084418716866174747792684,109,0.025000000000000001387778780781,0.169987450161800446313264956189,2 327,327_0,COMPLETED,BoTorch,0.278762662807525374297767939424,169,0.100000000000000005551115123126,0.113450208912514630132939430496,3 328,328_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063237848533221438507290201869,1 329,329_0,RUNNING,BoTorch,,100,0.005000000000000000104083408559,0.026050041163762610585408197039,4 330,330_0,COMPLETED,BoTorch,0.264857694163048718039021878212,100,0.250000000000000000000000000000,0.125139381197802213030456641718,4 331,331_0,COMPLETED,BoTorch,0.268098166907863033614489722822,104,0.050000000000000002775557561563,0.156014692581839115037567466970,3 332,332_0,COMPLETED,BoTorch,0.283845875542691805648587433097,102,0.001000000000000000020816681712,0.191857993924353847914332504843,3 333,333_0,COMPLETED,BoTorch,0.264757597684515233460444960656,100,0.250000000000000000000000000000,0.191960427848695630581588034147,3 334,334_0,COMPLETED,BoTorch,0.283388808490110943871798099281,196,0.025000000000000001387778780781,0.198088906571731282468817880726,2 335,335_0,COMPLETED,BoTorch,0.287231066087795472441257516039,252,0.050000000000000002775557561563,0.164869137460948889106759907008,3 336,336_0,COMPLETED,BoTorch,0.273684273999035165836346550350,135,0.025000000000000001387778780781,0.157174280875351735087264160029,3 337,337_0,COMPLETED,BoTorch,0.278933912204534451007020834368,153,0.005000000000000000104083408559,0.149081084025102755807878907035,2 338,338_0,COMPLETED,BoTorch,0.274193198263386395119312055613,102,0.010000000000000000208166817117,0.166512542619868475490818582330,2 339,339_0,COMPLETED,BoTorch,0.272694163048721627262693800731,100,0.010000000000000000208166817117,0.161063041324750355620665231982,2 340,340_0,COMPLETED,BoTorch,0.271070911722141816291298255237,116,0.025000000000000001387778780781,0.177934761474252189916711586193,2 341,341_0,COMPLETED,BoTorch,0.282802701398938705068530907738,196,0.010000000000000000208166817117,0.068712668026650033925584182271,2 342,342_0,COMPLETED,BoTorch,0.266037144235407585490804649453,100,0.250000000000000000000000000000,0.120500228667561784523520884704,4 343,343_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063584740001787667940114090470,1 344,344_0,COMPLETED,BoTorch,0.289739507959479003496028326481,283,0.100000000000000005551115123126,0.091085626567366517747359466739,2 345,345_0,COMPLETED,BoTorch,0.265634346357935369198344233155,100,0.250000000000000000000000000000,0.120432566045628186568450246341,4 346,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 347,347_0,COMPLETED,BoTorch,0.265773034249879414048223225109,100,0.250000000000000000000000000000,0.123632744325315749311933188892,4 348,348_0,COMPLETED,BoTorch,0.265250844187168377530383622798,100,0.250000000000000000000000000000,0.124883035504576564433953933531,4 349,349_0,COMPLETED,BoTorch,0.319889049686444715270283722930,771,0.010000000000000000208166817117,0.021548360621025351713253570551,4 350,350_0,COMPLETED,BoTorch,0.281723347805113322195325054054,155,0.050000000000000002775557561563,0.058398526128525141132286080392,4 351,351_0,COMPLETED,BoTorch,0.265920164013507043243578209513,100,0.250000000000000000000000000000,0.127489676322350559134477521184,4 352,352_0,COMPLETED,BoTorch,0.302875060299083398440700420906,453,0.250000000000000000000000000000,0.119561869651079177856445312500,1 353,353_0,COMPLETED,BoTorch,0.265806801736613640407824732392,100,0.250000000000000000000000000000,0.122363521231052277404849348841,4 354,354_0,COMPLETED,BoTorch,0.265477568740955183201890577038,100,0.250000000000000000000000000000,0.123845343575086905274496018592,4 355,355_0,COMPLETED,BoTorch,0.266332609744331927359439760039,100,0.250000000000000000000000000000,0.122858949936550027048909328187,4 356,356_0,COMPLETED,BoTorch,0.319152194886637707860188584164,812,0.010000000000000000208166817117,0.024053327552928480126404053863,2 357,357_0,COMPLETED,BoTorch,0.313627592860588499945606599795,835,0.025000000000000001387778780781,0.126259841211140172445581697502,4 358,358_0,COMPLETED,BoTorch,0.288736131210805546665199017298,258,0.005000000000000000104083408559,0.035321528960216314052544817059,2 359,359_0,COMPLETED,BoTorch,0.265417269657501231350238413143,100,0.250000000000000000000000000000,0.121736300193025004379165920909,3 360,360_0,COMPLETED,BoTorch,0.274540520984080993471820875129,118,0.050000000000000002775557561563,0.096498756906635271834993261564,2 361,361_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063527941358953191053338116490,1 362,362_0,COMPLETED,BoTorch,0.265144717800289475562180996349,100,0.250000000000000000000000000000,0.121546971136156922721482942507,3 363,363_0,COMPLETED,BoTorch,0.264557404727448153280988663028,100,0.250000000000000000000000000000,0.121805488174018150537847304804,3 364,364_0,COMPLETED,BoTorch,0.288467197298601041310917025839,100,0.010000000000000000208166817117,0.052955123851128972634061398139,4 365,365_0,COMPLETED,BoTorch,0.285312349252291319601226859959,275,0.025000000000000001387778780781,0.014338094393113143612539239768,2 366,366_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062952578658279648160522867784,1 367,367_0,COMPLETED,BoTorch,0.266718523878437085983250653953,100,0.250000000000000000000000000000,0.036179828689350747306097133560,3 368,368_0,COMPLETED,BoTorch,0.290048239266763152599537534115,278,0.025000000000000001387778780781,0.014138993314002668358342340582,2 369,369_0,COMPLETED,BoTorch,0.289283646888567336219466596958,279,0.025000000000000001387778780781,0.015026445603824982455432035522,2 370,370_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064318717147673828971043974434,1 371,371_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063984665438246476898420667112,1 372,372_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064131729785597960113996407472,1 373,373_0,COMPLETED,BoTorch,0.275817655571635289568632742885,124,0.010000000000000000208166817117,0.190468934062753447733484790660,3 374,374_0,COMPLETED,BoTorch,0.265461890979257097988863733917,100,0.250000000000000000000000000000,0.036387464631917450230691457591,3 375,375_0,COMPLETED,BoTorch,0.290227930535455813654266421509,101,0.010000000000000000208166817117,0.063215206101426585361124921292,4 376,376_0,COMPLETED,BoTorch,0.303468403280270138111518463120,472,0.025000000000000001387778780781,0.039686041896183771315875787877,4 377,377_0,COMPLETED,BoTorch,0.265675349734683985403194128594,100,0.250000000000000000000000000000,0.115513971526920958265982619650,3 378,378_0,COMPLETED,BoTorch,0.288247708634828736506960922270,277,0.025000000000000001387778780781,0.014054262298149836074578900025,2 379,379_0,COMPLETED,BoTorch,0.264731066087795507968394304044,100,0.250000000000000000000000000000,0.118432409826433507338272477227,3 380,380_0,COMPLETED,BoTorch,0.265693439459720237572071255272,100,0.250000000000000000000000000000,0.189627252782111455076119455043,4 381,381_0,COMPLETED,BoTorch,0.267191268692715921950764368376,100,0.250000000000000000000000000000,0.035301236167060649762117208184,3 382,382_0,COMPLETED,BoTorch,0.264576700434153377905488468969,100,0.250000000000000000000000000000,0.119108095259933766119075926326,3 383,383_0,COMPLETED,BoTorch,0.301599131693198296844116157445,415,0.010000000000000000208166817117,0.045053543032174375726484072402,3 384,384_0,COMPLETED,BoTorch,0.298589001447178037373930692411,405,0.250000000000000000000000000000,0.035485977434126139695713675337,2 385,385_0,COMPLETED,BoTorch,0.266368789194404209652589088364,100,0.250000000000000000000000000000,0.038428593979787852019658345171,3 386,386_0,COMPLETED,BoTorch,0.290342498794018299967945040407,177,0.010000000000000000208166817117,0.071972628610205910404395979185,4 387,387_0,COMPLETED,BoTorch,0.263876025084418763810845121043,100,0.250000000000000000000000000000,0.117734571342938298954550191411,3 388,388_0,COMPLETED,BoTorch,0.300711529184756409804890608939,428,0.250000000000000000000000000000,0.026728490803702098510541418364,2 389,389_0,COMPLETED,BoTorch,0.265786300048239221283097322157,100,0.250000000000000000000000000000,0.117076918030664695047349255219,3 390,390_0,RUNNING,BoTorch,,100,0.100000000000000005551115123126,0.106548122824840746369901012258,1 391,391_0,COMPLETED,BoTorch,0.279409068982151453930384832347,188,0.100000000000000005551115123126,0.132531769013446737437433853302,3 392,392_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065176816424789493775726612057,1 393,393_0,COMPLETED,BoTorch,0.276203569705740448192443636799,111,0.050000000000000002775557561563,0.085906512677011118150893764778,3 394,394_0,COMPLETED,BoTorch,0.264792571152918432275669147202,101,0.100000000000000005551115123126,0.181562152950617172741942795255,2 395,395_0,COMPLETED,BoTorch,0.271261456825856228647353418637,123,0.050000000000000002775557561563,0.116164860618951620852001838102,3 396,396_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064754373903214978991371708616,1 397,397_0,COMPLETED,BoTorch,0.319154606849975874816038867721,780,0.005000000000000000104083408559,0.139552144817559814482521574064,2 398,398_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065257522971363973729452823136,1 399,399_0,COMPLETED,BoTorch,0.311398938736131225546444056818,664,0.100000000000000005551115123126,0.095541476830840077472117854995,4 400,400_0,COMPLETED,BoTorch,0.299359623733719271143627338461,421,0.250000000000000000000000000000,0.158319316611443605680165092053,1 401,401_0,COMPLETED,BoTorch,0.264793777134587515753594288981,100,0.250000000000000000000000000000,0.116635184058910629234517841724,3 402,402_0,COMPLETED,BoTorch,0.289557404727448175485449155531,101,0.010000000000000000208166817117,0.063273788309726394785137415511,4 403,403_0,COMPLETED,BoTorch,0.321859623733719235616490550456,946,0.001000000000000000020816681712,0.159737053241990223373392154826,2 404,404_0,COMPLETED,BoTorch,0.272731548480463104056070733350,107,0.100000000000000005551115123126,0.000371037982404232025146484375,2 405,405_0,COMPLETED,BoTorch,0.294842016401350659471347626095,311,0.001000000000000000020816681712,0.072576894238591191377274469687,2 406,406_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065408465464321260962599069444,1 407,407_0,COMPLETED,BoTorch,0.315252050168837394394927287067,766,0.005000000000000000104083408559,0.067075824715662230035562174635,1 408,408_0,RUNNING,BoTorch,,135,0.010000000000000000208166817117,0.122820258326828482542403264688,4 409,409_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064694343375096899362297619973,1 410,410_0,COMPLETED,BoTorch,0.325395561987457782038291043136,960,0.100000000000000005551115123126,0.079440948180854328852795731564,4 411,411_0,COMPLETED,BoTorch,0.266611191509889100537122885726,100,0.250000000000000000000000000000,0.114746300444862212031260639833,3 412,412_0,COMPLETED,BoTorch,0.323230824891461598902253626875,963,0.025000000000000001387778780781,0.193529451265931129455566406250,3 413,413_0,COMPLETED,BoTorch,0.265595754944524808927042158757,100,0.250000000000000000000000000000,0.113786599035390290080016484353,3 414,414_0,COMPLETED,BoTorch,0.263726483357452967659639853082,100,0.250000000000000000000000000000,0.113997034237553529489872516933,3 415,415_0,COMPLETED,BoTorch,0.299312590448625126526849271613,379,0.050000000000000002775557561563,0.105226681753993037138350530313,2 416,416_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.066460569297477553596387167545,1 417,417_0,COMPLETED,BoTorch,0.266555716353111482597171288944,100,0.250000000000000000000000000000,0.114489330101097144809507710761,3 418,418_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063406270569913533718953146945,1 419,419_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063486220831572071077175678511,1 420,420_0,COMPLETED,BoTorch,0.283662566328991783137780657853,211,0.250000000000000000000000000000,0.111541977947689163741529227991,3 421,421_0,COMPLETED,BoTorch,0.265344910757356444719334831461,100,0.250000000000000000000000000000,0.113331590999864784130579664634,3 422,422_0,COMPLETED,BoTorch,0.265336468885672971396161301527,100,0.250000000000000000000000000000,0.113123227633923822965655858752,3 423,423_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063552364705884781259648264040,1 424,424_0,COMPLETED,BoTorch,0.283125904486251855907141816715,224,0.100000000000000005551115123126,0.101804282702726303644347183308,3 425,425_0,COMPLETED,BoTorch,0.267311866859623714631766233651,100,0.250000000000000000000000000000,0.115437169271624895383610009958,3 426,426_0,COMPLETED,BoTorch,0.281548480463096995052296733775,181,0.025000000000000001387778780781,0.068657192774116998501554576251,2 427,427_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063930818803618991164050555653,1 428,428_0,COMPLETED,BoTorch,0.298344187168355090555849074008,359,0.005000000000000000104083408559,0.048345424234867095947265625000,3 429,429_0,COMPLETED,BoTorch,0.317013989387361361238504287030,750,0.010000000000000000208166817117,0.076163237852733808863625597496,3 430,430_0,COMPLETED,BoTorch,0.266016642547033277388379701733,100,0.250000000000000000000000000000,0.075936220406138399408035866145,4 431,431_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063663841948895905087368873865,1 432,432_0,COMPLETED,BoTorch,0.280370236372407100056136641797,161,0.100000000000000005551115123126,0.014815445886267744959874370636,2 433,433_0,COMPLETED,BoTorch,0.278306801736613595998903747386,182,0.100000000000000005551115123126,0.137126265003239544704172203637,2 434,434_0,COMPLETED,BoTorch,0.266726965750120559306424183887,100,0.250000000000000000000000000000,0.041485150633278511378421171685,3 435,435_0,COMPLETED,BoTorch,0.265208634828750566825306123064,100,0.250000000000000000000000000000,0.114582156814305002878739969674,3 436,436_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063167257120466668851577196619,1 437,437_0,COMPLETED,BoTorch,0.264710564399421088843666893808,100,0.250000000000000000000000000000,0.114575279144877292814008740152,3 438,438_0,COMPLETED,BoTorch,0.308264592378195811939178838657,622,0.025000000000000001387778780781,0.170440341760796060244231853176,2 439,439_0,COMPLETED,BoTorch,0.298102990834539283149240418425,360,0.050000000000000002775557561563,0.029526027726847932364240278957,2 440,440_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063119629126338130942208692886,1 441,441_0,COMPLETED,BoTorch,0.280414857694163077717064425087,161,0.100000000000000005551115123126,0.015398910526911450263876091071,2 442,442_0,COMPLETED,BoTorch,0.293525084418716830647611004679,307,0.250000000000000000000000000000,0.057535290872564773256847558969,4 443,443_0,FAILED,BoTorch,,165,0.050000000000000002775557561563,0.000000000000000000000000000000,1 444,444_0,COMPLETED,BoTorch,0.317969126869271589974630387587,900,0.250000000000000000000000000000,0.057907013836237267168094433600,1 445,445_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060891879156301190190170302685,1 446,446_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.061063330620698567929860445247,1 447,447_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060871664129112879049188222780,1 448,448_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.061194731653367834267598368569,1 449,449_0,COMPLETED,BoTorch,0.264134105161601517863800836494,100,0.250000000000000000000000000000,0.111357322529521560183773942754,3 450,450_0,COMPLETED,BoTorch,0.273310419681620841991787074221,134,0.250000000000000000000000000000,0.021199076569781705181583220110,2 451,451_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060905206873586449634050410396,1 452,452_0,COMPLETED,BoTorch,0.306477327544621314103778786375,532,0.010000000000000000208166817117,0.187354802712664553210331064292,3 453,453_0,COMPLETED,BoTorch,0.323941148094548991842600571545,932,0.100000000000000005551115123126,0.092217177311222808810953210923,3 454,454_0,COMPLETED,BoTorch,0.266509889049686421458318363875,100,0.250000000000000000000000000000,0.044655957057352008154893496794,3 455,455_0,COMPLETED,BoTorch,0.264499517607332368385186782689,100,0.250000000000000000000000000000,0.110785076226966164303355810716,3 456,456_0,COMPLETED,BoTorch,0.264700916546068532042568222096,100,0.250000000000000000000000000000,0.110925927995745882137512694499,3 457,457_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058798372025838624432836354572,1 458,458_0,COMPLETED,BoTorch,0.266275928605885225941563021479,100,0.250000000000000000000000000000,0.040396779155443776621048357356,2 459,459_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058708394592289027402731704797,1 460,460_0,COMPLETED,BoTorch,0.265180897250361757855330324674,100,0.250000000000000000000000000000,0.109856149947308398551193420190,3 461,461_0,FAILED,BoTorch,,124,0.250000000000000000000000000000,0.000000000000000000000000000000,1 462,462_0,COMPLETED,BoTorch,0.265676555716353068881119270372,100,0.250000000000000000000000000000,0.037829374633336125544058603509,3 463,463_0,COMPLETED,BoTorch,0.290453449107573535847848233971,313,0.250000000000000000000000000000,0.028041718799812966056439478280,3 464,464_0,COMPLETED,BoTorch,0.269008683068017395711990502605,114,0.250000000000000000000000000000,0.032206390015410833738140894411,3 465,465_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058679409940176022186797410995,1 466,466_0,COMPLETED,BoTorch,0.280218282682103247971383552795,170,0.100000000000000005551115123126,0.029042969085276128249351046406,2 467,467_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058572691251139154255955787676,1 468,468_0,COMPLETED,BoTorch,0.265722383019778130019972195441,100,0.250000000000000000000000000000,0.036293903523610726580272967112,3 469,469_0,COMPLETED,BoTorch,0.264505547515677785774812491582,100,0.250000000000000000000000000000,0.156324936855616380970701584374,4 470,470_0,COMPLETED,BoTorch,0.297466232513265760317722197215,381,0.050000000000000002775557561563,0.044967321678996086120605468750,3 471,471_0,COMPLETED,BoTorch,0.282545827303425034493500334065,208,0.250000000000000000000000000000,0.161883437819778930322200949377,4 472,472_0,COMPLETED,BoTorch,0.295653642064640620468196630100,355,0.025000000000000001387778780781,0.145991105391367531618485031686,4 473,473_0,COMPLETED,BoTorch,0.264684032802701363351616237196,100,0.250000000000000000000000000000,0.105578575750874356642405871298,3 474,474_0,COMPLETED,BoTorch,0.265911722141823458898102217063,100,0.250000000000000000000000000000,0.105378275374606159009438499652,3 475,475_0,COMPLETED,BoTorch,0.285851423058369524810018447170,234,0.250000000000000000000000000000,0.088126997907052362357660513226,2 476,476_0,COMPLETED,BoTorch,0.288088519054510383554656982596,243,0.250000000000000000000000000000,0.110534043763194256282389460466,2 477,477_0,COMPLETED,BoTorch,0.266122768933912179356582328182,100,0.250000000000000000000000000000,0.106792237016569063756676882804,3 478,478_0,COMPLETED,BoTorch,0.322136999517607325316248534364,919,0.001000000000000000020816681712,0.088472510671890325362198836956,1 479,479_0,COMPLETED,BoTorch,0.284956584659913136903242047993,231,0.250000000000000000000000000000,0.076124516330787636575472276945,1 480,480_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.057947177819312745139512799142,1 481,481_0,COMPLETED,BoTorch,0.311232513265798371726589266473,673,0.010000000000000000208166817117,0.123515655374345306727335014330,1 482,482_0,COMPLETED,BoTorch,0.280635552339604465998945670435,189,0.250000000000000000000000000000,0.108880662720070742199851565601,4 483,483_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.057787828021531165156865483823,1 484,484_0,COMPLETED,BoTorch,0.312425229136517157435548597277,648,0.250000000000000000000000000000,0.188148195296525960751310435626,1 485,485_0,COMPLETED,BoTorch,0.278584177520501685698661731294,164,0.100000000000000005551115123126,0.028079470062678281438994787322,2 486,486_0,COMPLETED,BoTorch,0.267276893391220404794239584589,100,0.250000000000000000000000000000,0.033764158564677522467167847253,3 487,487_0,COMPLETED,BoTorch,0.264431982633863915665983768122,100,0.250000000000000000000000000000,0.034394251726277906555662156052,3 488,488_0,COMPLETED,BoTorch,0.267597684515195388677000210009,101,0.100000000000000005551115123126,0.105557214010480085453203002999,1 489,489_0,COMPLETED,BoTorch,0.265531837916063717663917032041,100,0.250000000000000000000000000000,0.108771684097887760822587210896,3 490,490_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.056757437299702950816993052285,1 491,491_0,COMPLETED,BoTorch,0.267610950313555195911874307058,100,0.250000000000000000000000000000,0.033648978874544668848933781646,3 492,492_0,COMPLETED,BoTorch,0.275092860588519005915486559388,123,0.025000000000000001387778780781,0.124412909925848483294608115557,1 493,493_0,COMPLETED,BoTorch,0.274722624216111932504702508595,117,0.100000000000000005551115123126,0.069470607286485269726838964743,3 494,494_0,COMPLETED,BoTorch,0.266827062228654154907303563959,100,0.250000000000000000000000000000,0.034489320671504640480886649812,3 495,495_0,COMPLETED,BoTorch,0.264657501205981637859565580584,100,0.250000000000000000000000000000,0.108479984228543907542885449402,3 496,496_0,COMPLETED,BoTorch,0.264597202122527686007913416688,100,0.250000000000000000000000000000,0.108682457953691746777025173287,3 497,497_0,COMPLETED,BoTorch,0.291072117703810917532791791018,296,0.025000000000000001387778780781,0.056134262867271904340338295469,2 498,498_0,COMPLETED,BoTorch,0.318613121080559613673699459468,814,0.100000000000000005551115123126,0.117512379400432118159436356564,2 499,499_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.057268722267108600199048140666,1 500,500_0,COMPLETED,BoTorch,0.270440183309213710849405742920,118,0.250000000000000000000000000000,0.078021889235570726639501515365,3
start_time,end_time,run_time,program_string,n_samples,confidence,feature_proportion,n_clusters,result,exit_code,signal,hostname,OO_Info_runtime,OO_Info_lpd 1727577928,1727578482,554,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 843 confidence 0.25 feature_proportion 0.08775444924831391 n_clusters 3,843,0.25,0.08775444924831391,3,0.3187831644958996,0,None,i7180,502,0.00030684750207181474 1727577868,1727578533,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 392 confidence 0.25 feature_proportion 0.1259731888771057 n_clusters 1,392,0.25,0.1259731888771057,1,0.2993644476603956,0,None,i7186,658,0.00015099290585569828 1727577928,1727578540,612,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 159 confidence 0.1 feature_proportion 0.0718895209953189 n_clusters 4,159,0.1,0.0718895209953189,4,0.2799650265315967,0,None,i7180,559,7.084352389107572e-05 1727577868,1727578551,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 124 confidence 0.25 feature_proportion 0.0045273913070559505 n_clusters 2,124,0.25,0.0045273913070559505,2,0.2720501688374337,0,None,i7186,676,5.319226667360991e-05 1727577891,1727578552,661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 521 confidence 0.25 feature_proportion 0.17577387001365424 n_clusters 3,521,0.25,0.17577387001365424,3,0.305904486251809,0,None,i7186,654,0.00019638451401139708 1727577868,1727578553,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 661 confidence 0.01 feature_proportion 0.1295369766652584 n_clusters 1,661,0.01,0.1295369766652584,1,0.3105704293294742,0,None,i7186,678,0.0002488980159404411 1727577891,1727578555,664,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 610 confidence 0.1 feature_proportion 0.10376911535859108 n_clusters 2,610,0.1,0.10376911535859108,2,0.3089097925711529,0,None,i7186,657,0.00022824939914679914 1727577891,1727578556,665,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 335 confidence 0.25 feature_proportion 0.0946620749309659 n_clusters 2,335,0.25,0.0946620749309659,2,0.29126145682585625,0,None,i7186,658,0.00013312877839855857 1727577871,1727578559,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 721 confidence 0.1 feature_proportion 0.025189650245010853 n_clusters 1,721,0.1,0.025189650245010853,1,0.3161951278340569,0,None,i7186,681,0.00026775896289428757 1727577909,1727578566,657,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 218 confidence 0.05 feature_proportion 0.18854437656700612 n_clusters 1,218,0.05,0.18854437656700612,1,0.28403521466473713,0,None,i7181,585,8.930844192329014e-05 1727577909,1727578599,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 717 confidence 0.025 feature_proportion 0.1159105820581317 n_clusters 4,717,0.025,0.1159105820581317,4,0.31327062228654123,0,None,i7181,618,0.0002668028517770383 1727577909,1727578600,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 703 confidence 0.025 feature_proportion 0.15183147732168437 n_clusters 4,703,0.025,0.15183147732168437,4,0.3143077665219489,0,None,i7181,620,0.00026042170882844775 1727577909,1727578602,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 880 confidence 0.1 feature_proportion 0.05467753913253546 n_clusters 2,880,0.1,0.05467753913253546,2,0.3200904486251809,0,None,i7181,622,0.00032202057339539254 1727577909,1727578605,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 466 confidence 0.001 feature_proportion 0.1348806345835328 n_clusters 1,466,0.001,0.1348806345835328,1,0.2995224312590449,0,None,i7181,625,0.00018567757886698197 1727577909,1727578611,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 268 confidence 0.025 feature_proportion 0.08824395090341569 n_clusters 2,268,0.025,0.08824395090341569,2,0.2899119633381573,0,None,i7181,631,0.00011158078764549718 1727577891,1727578634,743,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 974 confidence 0.1 feature_proportion 0.002316499501466751 n_clusters 1,974,0.1,0.002316499501466751,1,0.3229835986493005,0,None,i7186,736,0.0003528419621610269 1727577868,1727578644,776,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 127 confidence 0.025 feature_proportion 0.18841087352484465 n_clusters 2,127,0.025,0.18841087352484465,2,0.27234563434635795,0,None,i7186,769,5.804733702881441e-05 1727577871,1727578649,778,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 861 confidence 0.001 feature_proportion 0.09591507501900197 n_clusters 1,861,0.001,0.09591507501900197,1,0.3178557645923782,0,None,i7186,771,0.000333819482117398 1727577909,1727578916,1007,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 766 confidence 0.01 feature_proportion 0.025302996300160887 n_clusters 4,766,0.01,0.025302996300160887,4,0.3189122045344911,0,None,i7181,935,0.0003198366571993881 1727577909,1727579429,1520,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 182 confidence 0.001 feature_proportion 0.011049367859959603 n_clusters 4,182,0.001,0.011049367859959603,4,0.31412083936324164,0,None,i7181,1448,0.00019489329034809322 1727579572,1727579576,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7186 1727579572,1727579576,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727579585,1727579588,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7186 1727579612,1727579615,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 3,100,0.1,0,3,None,1,None,i7181 1727579644,1727579648,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7181 1727579652,1727579655,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7181 1727579652,1727579655,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7181 1727579672,1727579675,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7181 1727579712,1727579715,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7181 1727579612,1727580213,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.14741618445507518 n_clusters 3,100,0.25,0.14741618445507518,3,0.26509044862518094,0,None,i7181,594,4.276681982677712e-05 1727579711,1727580216,505,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11162476133062771 n_clusters 2,100,0.25,0.11162476133062771,2,0.26510492040520983,0,None,i7180,499,4.294724423207268e-05 1727579592,1727580221,629,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.04555092629805038 n_clusters 1,100,0.1,0.04555092629805038,1,0.2706608779546551,0,None,i7181,623,4.5607170198458814e-05 1727579612,1727580236,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05787613008387979 n_clusters 3,100,0.25,0.05787613008387979,3,0.26648818137964303,0,None,i7181,617,4.418243842065065e-05 1727579612,1727580265,653,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.17134974513337844 n_clusters 1,100,0.025,0.17134974513337844,1,0.26981307284129286,0,None,i7181,647,4.6718519347104956e-05 1727579554,1727580266,712,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.027738805364601382 n_clusters 3,100,0.25,0.027738805364601382,3,0.26448745779064153,0,None,i7186,704,4.436701659662074e-05 1727579612,1727580278,666,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.08810677802505817 n_clusters 2,100,0.1,0.08810677802505817,2,0.2704546550892426,0,None,i7181,659,4.738271392509904e-05 1727579572,1727580285,713,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06286692855923794 n_clusters 2,100,0.25,0.06286692855923794,2,0.26570549927641096,0,None,i7186,706,4.422523286120628e-05 1727579692,1727580289,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.01703481254811868 n_clusters 1,100,0.25,0.01703481254811868,1,0.26567776169802215,0,None,i7181,590,4.416771781819168e-05 1727579711,1727580297,586,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.2 n_clusters 1,100,0.01,0.2,1,0.27066570188133143,0,None,i7180,580,4.9122174983675744e-05 1727579585,1727580313,728,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 3,100,0.1,0.2,3,0.26543053545586104,0,None,i7186,721,4.367306040139469e-05 1727579572,1727580334,762,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.09607732249779238 n_clusters 2,100,0.1,0.09607732249779238,2,0.2703352629040039,0,None,i7186,754,4.699529756819184e-05 1727579553,1727580351,798,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.17788386864902397 n_clusters 3,100,0.05,0.17788386864902397,3,0.26788470815243604,0,None,i7186,790,4.4904408781873134e-05 1727579585,1727580359,774,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.15657168484213685 n_clusters 2,100,0.05,0.15657168484213685,2,0.26840810419681616,0,None,i7186,767,4.5728855941797854e-05 1727579572,1727580400,828,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.2 n_clusters 1,100,0.005,0.2,1,0.27143873613121083,0,None,i7186,821,5.09739251080771e-05 1727579752,1727580411,659,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.034287894684050566 n_clusters 2,100,0.1,0.034287894684050566,2,0.27061987457790637,0,None,i7181,652,4.7250660377909484e-05 1727579612,1727580430,818,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.2 n_clusters 4,100,0.05,0.2,4,0.26784249879401834,0,None,i7186,811,4.519971342911188e-05 1727579585,1727580446,861,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.2 n_clusters 3,100,0.025,0.2,3,0.2690074770863483,0,None,i7186,855,4.720671021774495e-05 1727579572,1727580503,931,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.2 n_clusters 2,100,0.01,0.2,2,0.2732718282682103,0,None,i7186,924,5.212273563401008e-05 1727580253,1727580873,620,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10621788049068198 n_clusters 3,100,0.25,0.10621788049068198,3,0.2656801736613603,0,None,i7181,614,4.268500004296118e-05 1727579592,1727580897,1305,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 3,100,0.001,0.2,3,0.2850820067534974,0,None,i7181,1299,7.358034311724615e-05 1727580293,1727580944,651,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 308 confidence 0.025 feature_proportion 0.0769519696768784 n_clusters 2,308,0.025,0.0769519696768784,2,0.2941497829232995,0,None,i7181,645,0.00012624159691720056 1727580273,1727580948,675,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1594703362608583 n_clusters 2,100,0.25,0.1594703362608583,2,0.26520984081041965,0,None,i7186,668,4.2882280355640565e-05 1727580293,1727580976,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 4,100,0.25,0.2,4,0.2659213699951761,0,None,i7186,676,4.257838764159928e-05 1727580353,1727581038,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 2,100,0.25,0.2,2,0.26450916546068504,0,None,i7186,678,4.2977907776657824e-05 1727580487,1727581045,558,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.14785112567428693 n_clusters 1,100,0.25,0.14785112567428693,1,0.26614085865894843,0,None,i7180,552,4.302539077375205e-05 1727580353,1727581047,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06849961863685758 n_clusters 1,100,0.25,0.06849961863685758,1,0.26567776169802215,0,None,i7186,686,4.416771781819168e-05 1727580427,1727581052,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 4,100,0.25,0.2,4,0.26490472744814275,0,None,i7181,618,4.269102195499108e-05 1727580427,1727581052,625,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 3,100,0.25,0.2,3,0.2653895320791124,0,None,i7181,619,4.2715040821033016e-05 1727580487,1727581068,581,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.14558547748436398 n_clusters 1,100,0.25,0.14558547748436398,1,0.26614085865894843,0,None,i7180,575,4.302539077375205e-05 1727580427,1727581070,643,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1578751092774113 n_clusters 4,100,0.25,0.1578751092774113,4,0.26490472744814275,0,None,i7181,637,4.269102195499108e-05 1727580453,1727581132,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12790225269847663 n_clusters 2,100,0.25,0.12790225269847663,2,0.26707428847081527,0,None,i7186,672,4.271274382776673e-05 1727580353,1727581156,803,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 1,100,0.001,0.2,1,0.27960202604920403,0,None,i7181,797,5.525105371762164e-05 1727580367,1727581171,804,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.17996828835506634 n_clusters 4,100,0.05,0.17996828835506634,4,0.266441148094549,0,None,i7186,797,4.520554667198688e-05 1727580413,1727581205,792,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 274 confidence 0.025 feature_proportion 0.06381984178367374 n_clusters 3,274,0.025,0.06381984178367374,3,0.29282923299565844,0,None,i7186,784,0.00011678528951877174 1727580533,1727581263,730,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.1780170967337713 n_clusters 4,100,0.1,0.1780170967337713,4,0.26531114327062233,0,None,i7186,722,4.3558341865772804e-05 1727580368,1727581310,942,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.19953557106265557 n_clusters 4,100,0.025,0.19953557106265557,4,0.2700361794500724,0,None,i7186,934,4.761506768384379e-05 1727580898,1727581480,582,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0630381195450128 n_clusters 1,100,0.25,0.0630381195450128,1,0.26567776169802215,0,None,i7181,576,4.416771781819168e-05 1727580918,1727581491,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.26614085865894843,0,None,i7181,567,4.302539077375205e-05 1727581000,1727581575,575,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.26614085865894843,0,None,i7181,568,4.302539077375205e-05 1727580473,1727581592,1119,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 confidence 0.001 feature_proportion 0.1951734126510648 n_clusters 2,118,0.001,0.1951734126510648,2,0.27977930535455864,0,None,i7186,1112,7.202985594491168e-05 1727580988,1727581662,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.26614085865894843,0,None,i7186,665,4.302539077375205e-05 1727581148,1727581733,585,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 220 confidence 0.025 feature_proportion 0.17197808724540015 n_clusters 1,220,0.025,0.17197808724540015,1,0.2848456343463579,0,None,i7181,579,9.071727796012011e-05 1727581148,1727581757,609,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 1,100,0.1,0.2,1,0.2667378195851423,0,None,i7181,603,4.416771371111377e-05 1727581068,1727581784,716,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 2,100,0.1,0.2,2,0.26587795465508923,0,None,i7186,709,4.410943677785389e-05 1727581148,1727581805,657,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 173 confidence 0.25 feature_proportion 0.2 n_clusters 4,173,0.25,0.2,4,0.2775699469368066,0,None,i7186,649,7.113678831329412e-05 1727581208,1727581805,597,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 228 confidence 0.025 feature_proportion 0.19208300436627082 n_clusters 1,228,0.025,0.19208300436627082,1,0.2843620356970574,0,None,i7181,590,9.402408210790911e-05 1727581208,1727581819,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 confidence 0.1 feature_proportion 0.2 n_clusters 2,135,0.1,0.2,2,0.27465750120598165,0,None,i7181,605,5.730295072774784e-05 1727580988,1727581838,850,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.09555710222974084 n_clusters 1,100,0.001,0.09555710222974084,1,0.28274360829715384,0,None,i7186,840,5.846108662692102e-05 1727581288,1727581848,560,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 173 confidence 0.25 feature_proportion 0.2 n_clusters 4,173,0.25,0.2,4,0.277454172696575,0,None,i7181,554,7.114611498523693e-05 1727581128,1727581850,722,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.022821504645745866 n_clusters 3,100,0.25,0.022821504645745866,3,0.26618909792571155,0,None,i7186,714,4.4236717943939564e-05 1727581208,1727581916,708,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 1,100,0.1,0.2,1,0.2667378195851423,0,None,i7186,701,4.416771371111377e-05 1727581268,1727581932,664,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 346 confidence 0.25 feature_proportion 0.07150360559118255 n_clusters 2,346,0.25,0.07150360559118255,2,0.2927834056922335,0,None,i7186,657,0.00013720509422940314 1727581258,1727582004,746,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.042146187575146314 n_clusters 2,100,0.25,0.042146187575146314,2,0.2662542209358417,0,None,i7127,677,4.4040227658411876e-05 1727581228,1727582036,808,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 105 confidence 0.005 feature_proportion 0.11940787309050199 n_clusters 1,105,0.005,0.11940787309050199,1,0.2737240713941148,0,None,i7186,800,5.231541149267541e-05 1727580413,1727582071,1658,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 4,100,0.001,0.2,4,0.2877472262421611,0,None,i7181,1651,8.893408369883057e-05 1727581555,1727582154,599,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.037505726348380786 n_clusters 2,100,0.25,0.037505726348380786,2,0.2666546068499759,0,None,i7179,588,4.405076766971073e-05 1727581354,1727582171,817,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 157 confidence 0.05 feature_proportion 0.05370277936425251 n_clusters 3,157,0.05,0.05370277936425251,3,0.28285094066570193,0,None,i7186,809,7.217500287534289e-05 1727581511,1727582233,722,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 152 confidence 0.1 feature_proportion 0.02391758356977458 n_clusters 4,152,0.1,0.02391758356977458,4,0.27943077665219485,0,None,i7181,715,6.84587871311879e-05 1727581632,1727582265,633,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.1748842215161551 n_clusters 1,100,0.1,0.1748842215161551,1,0.2667378195851423,0,None,i7181,626,4.416771371111377e-05 1727581602,1727582335,733,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 150 confidence 0.05 feature_proportion 0.07982151853575609 n_clusters 2,150,0.05,0.07982151853575609,2,0.27965750120598165,0,None,i7186,725,6.699049065543313e-05 1727581758,1727582389,631,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 1,100,0.1,0.2,1,0.2667378195851423,0,None,i7181,624,4.416771371111377e-05 1727581783,1727582415,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.033707657150358804 n_clusters 2,100,0.25,0.033707657150358804,2,0.2656946454413893,0,None,i7181,625,4.419884988388076e-05 1727581693,1727582441,748,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.14819543554728395 n_clusters 1,100,0.025,0.14819543554728395,1,0.26981307284129286,0,None,i7186,741,4.6718519347104956e-05 1727581898,1727582474,576,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 166 confidence 0.1 feature_proportion 0.11161138258035504 n_clusters 4,166,0.1,0.11161138258035504,4,0.27786179450072357,0,None,i7181,570,6.899932735142938e-05 1727581838,1727582544,706,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.032172239915852975 n_clusters 2,100,0.25,0.032172239915852975,2,0.2666377231066088,0,None,i7186,698,4.39536789577518e-05 1727581813,1727582548,735,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.05 feature_proportion 0.07132954504485205 n_clusters 2,153,0.05,0.07132954504485205,2,0.27811263868789193,0,None,i7186,727,6.832465627825231e-05 1727581918,1727582563,645,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.050489515786813544 n_clusters 4,100,0.25,0.050489515786813544,4,0.2670381090207429,0,None,i7181,639,4.40302848174625e-05 1727581878,1727582576,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.01792473035254009 n_clusters 2,100,0.25,0.01792473035254009,2,0.2665098890496864,0,None,i7186,691,4.4102094361429605e-05 1727582038,1727582589,551,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03354337705211629 n_clusters 4,100,0.25,0.03354337705211629,4,0.26612518089725035,0,None,i7180,545,4.417254213668156e-05 1727581959,1727582644,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 273 confidence 0.025 feature_proportion 0.08575348742238179 n_clusters 1,273,0.025,0.08575348742238179,1,0.2874927641099855,0,None,i7186,678,0.00011255678300610814 1727582645,1727582648,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7181 1727581938,1727582664,726,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04448255547551042 n_clusters 4,100,0.25,0.04448255547551042,4,0.26664857694163047,0,None,i7186,718,4.401289176575902e-05 1727582098,1727582701,603,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03650511753677028 n_clusters 1,100,0.25,0.03650511753677028,1,0.26567776169802215,0,None,i7181,596,4.416771781819168e-05 1727582174,1727582705,531,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.028830864552846572 n_clusters 4,100,0.25,0.028830864552846572,4,0.26749276410998557,0,None,i7180,525,4.395669320280113e-05 1727581918,1727582744,826,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 301 confidence 0.005 feature_proportion 0.08079921226149847 n_clusters 3,301,0.005,0.08079921226149847,3,0.294187168355041,0,None,i7181,819,0.000139557030457275 1727582059,1727582783,724,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.033440639794442034 n_clusters 4,100,0.25,0.033440639794442034,4,0.26706825856246985,0,None,i7186,717,4.399889911641995e-05 1727582265,1727582886,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.023362646599296648 n_clusters 4,100,0.25,0.023362646599296648,4,0.2667667631452002,0,None,i7181,615,4.402014488226608e-05 1727582295,1727582922,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.02072658485785613 n_clusters 4,100,0.25,0.02072658485785613,4,0.26669199228171736,0,None,i7181,621,4.40295170135625e-05 1727581843,1727582926,1083,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 319 confidence 0.001 feature_proportion 0.2 n_clusters 4,319,0.001,0.2,4,0.29592860588519054,0,None,i7186,1075,0.00014312483250254597 1727582205,1727582951,746,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 288 confidence 0.01 feature_proportion 0.09675476355644837 n_clusters 2,288,0.01,0.09675476355644837,2,0.2922563917028461,0,None,i7186,739,0.00012085457785271719 1727582997,1727583000,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.25 feature_proportion 0 n_clusters 1,230,0.25,0,1,None,1,None,i7181 1727582465,1727583082,617,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.024419600336531684 n_clusters 4,100,0.25,0.024419600336531684,4,0.26560660877954656,0,None,i7181,610,4.4115796949589634e-05 1727582365,1727583101,736,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 159 confidence 0.1 feature_proportion 0.04421126813836706 n_clusters 3,159,0.1,0.04421126813836706,3,0.27866497829232995,0,None,i7186,728,6.987119042907517e-05 1727583117,1727583121,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 229 confidence 0.25 feature_proportion 0 n_clusters 1,229,0.25,0,1,None,1,None,i7186 1727582415,1727583127,712,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 confidence 0.1 feature_proportion 0.05987899748755729 n_clusters 4,126,0.1,0.05987899748755729,4,0.274739507959479,0,None,i7181,705,5.877054298911643e-05 1727582505,1727583152,647,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.1 feature_proportion 0.02714122299901587 n_clusters 3,156,0.1,0.02714122299901587,3,0.27860709117221416,0,None,i7181,641,6.868091233428552e-05 1727583157,1727583161,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 227 confidence 0.25 feature_proportion 0 n_clusters 1,227,0.25,0,1,None,1,None,i7186 1727582445,1727583163,718,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.02565676821458219 n_clusters 4,100,0.25,0.02565676821458219,4,0.2674409068982151,0,None,i7186,711,4.408484116686e-05 1727582606,1727583324,718,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0389886772233911 n_clusters 4,100,0.25,0.0389886772233911,4,0.26538712011577426,0,None,i7186,711,4.4044017351673294e-05 1727582647,1727583332,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04973844066078083 n_clusters 1,100,0.25,0.04973844066078083,1,0.26567776169802215,0,None,i7186,678,4.416771781819168e-05 1727582596,1727583335,739,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 152 confidence 0.1 feature_proportion 0.047694359628426324 n_clusters 3,152,0.1,0.047694359628426324,3,0.27831041968162085,0,None,i7186,731,6.716414066329802e-05 1727582768,1727583376,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.048296241396162454 n_clusters 1,100,0.25,0.048296241396162454,1,0.26567776169802215,0,None,i7181,602,4.416771781819168e-05 1727582706,1727583394,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04859368947089354 n_clusters 1,100,0.25,0.04859368947089354,1,0.26567776169802215,0,None,i7186,681,4.416771781819168e-05 1727582706,1727583406,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 172 confidence 0.05 feature_proportion 0.2 n_clusters 3,172,0.05,0.2,3,0.27863724071394114,0,None,i7186,692,7.225771684454383e-05 1727582777,1727583477,700,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04766508628506933 n_clusters 1,100,0.25,0.04766508628506933,1,0.26567776169802215,0,None,i7127,693,4.416771781819168e-05 1727582807,1727583496,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04731657531785901 n_clusters 1,100,0.25,0.04731657531785901,1,0.26567776169802215,0,None,i7186,682,4.416771781819168e-05 1727582646,1727583517,871,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 411 confidence 0.001 feature_proportion 0.1401451074032919 n_clusters 4,411,0.001,0.1401451074032919,4,0.3023094548962856,0,None,i7181,864,0.00017381609257689063 1727582911,1727583612,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 337 confidence 0.005 feature_proportion 0.1022927895072172 n_clusters 3,337,0.005,0.1022927895072172,3,0.29219006271104675,0,None,i7181,694,0.00013981520013084644 1727582828,1727583628,800,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 211 confidence 0.005 feature_proportion 0.16072294879704715 n_clusters 3,211,0.005,0.16072294879704715,3,0.28687047756874096,0,None,i7127,793,9.143579161755813e-05 1727583037,1727583638,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04535991335780742 n_clusters 1,100,0.25,0.04535991335780742,1,0.26567776169802215,0,None,i7181,594,4.416771781819168e-05 1727582974,1727583651,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 678 confidence 0.05 feature_proportion 0.1287595117714697 n_clusters 2,678,0.05,0.1287595117714697,2,0.31090931017848533,0,None,i7186,670,0.00025271618834356676 1727583197,1727583858,661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 352 confidence 0.25 feature_proportion 0.09816870553862314 n_clusters 2,352,0.25,0.09816870553862314,2,0.29403400868306806,0,None,i7186,654,0.00013890457561443006 1727583257,1727583865,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.050766865902727165 n_clusters 1,100,0.25,0.050766865902727165,1,0.26567776169802215,0,None,i7181,603,4.416771781819168e-05 1727583277,1727583877,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.050504912170116725 n_clusters 1,100,0.25,0.050504912170116725,1,0.26567776169802215,0,None,i7181,595,4.416771781819168e-05 1727583277,1727583885,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05097018506420651 n_clusters 1,100,0.25,0.05097018506420651,1,0.26567776169802215,0,None,i7181,602,4.416771781819168e-05 1727583217,1727583908,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05031527155018242 n_clusters 1,100,0.25,0.05031527155018242,1,0.26567776169802215,0,None,i7186,683,4.416771781819168e-05 1727583951,1727583954,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.25 feature_proportion 0 n_clusters 4,151,0.25,0,4,None,1,None,i7181 1727583469,1727584038,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 121 confidence 0.25 feature_proportion 0.032939386489557666 n_clusters 1,121,0.25,0.032939386489557666,1,0.2729884225759769,0,None,i7181,563,5.1797455921913225e-05 1727583357,1727584047,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05061445015167515 n_clusters 1,100,0.25,0.05061445015167515,1,0.26567776169802215,0,None,i7186,681,4.416771781819168e-05 1727583398,1727584084,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.050138878747595574 n_clusters 1,100,0.25,0.050138878747595574,1,0.26567776169802215,0,None,i7186,679,4.416771781819168e-05 1727583537,1727584116,579,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.16323677935604963 n_clusters 4,100,0.25,0.16323677935604963,4,0.26561987457790637,0,None,i7181,572,4.2614852979924115e-05 1727583437,1727584129,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.027417080626232417 n_clusters 1,100,0.25,0.027417080626232417,1,0.26567776169802215,0,None,i7186,684,4.416771781819168e-05 1727582768,1727584195,1427,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.001 feature_proportion 0.03580189867915297 n_clusters 4,183,0.001,0.03580189867915297,4,0.3111167390255668,0,None,i7181,1420,0.00019821882428317324 1727583650,1727584205,555,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0498053874468125 n_clusters 1,100,0.25,0.0498053874468125,1,0.26567776169802215,0,None,i7181,548,4.416771781819168e-05 1727583518,1727584210,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.050397608925995256 n_clusters 1,100,0.25,0.050397608925995256,1,0.26567776169802215,0,None,i7186,685,4.416771781819168e-05 1727583710,1727584214,504,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05225866542156085 n_clusters 1,100,0.25,0.05225866542156085,1,0.26567776169802215,0,None,i7180,499,4.416771781819168e-05 1727583558,1727584233,675,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 171 confidence 0.1 feature_proportion 0.020056208405210198 n_clusters 2,171,0.1,0.020056208405210198,2,0.2801519536903039,0,None,i7127,668,7.274063852834089e-05 1727583397,1727584240,843,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 254 confidence 0.01 feature_proportion 0.00954411788717407 n_clusters 3,254,0.01,0.00954411788717407,3,0.28952484322238303,0,None,i7186,836,0.00011601364740146095 1727583692,1727584253,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05191703913323784 n_clusters 1,100,0.25,0.05191703913323784,1,0.26567776169802215,0,None,i7181,555,4.416771781819168e-05 1727583440,1727584499,1059,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 173 confidence 0.001 feature_proportion 0.12318138749201075 n_clusters 3,173,0.001,0.12318138749201075,3,0.28444404245055477,0,None,i7186,1052,8.802202073511856e-05 1727583952,1727584554,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 170 confidence 0.1 feature_proportion 0.017157494960939497 n_clusters 2,170,0.1,0.017157494960939497,2,0.2780173661360348,0,None,i7181,595,7.27615823801122e-05 1727583972,1727584566,594,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 171 confidence 0.1 feature_proportion 0.016893846161754374 n_clusters 2,171,0.1,0.016893846161754374,2,0.2789507959479016,0,None,i7181,588,7.268625280189051e-05 1727583891,1727584579,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.052211428481386796 n_clusters 1,100,0.25,0.052211428481386796,1,0.26567776169802215,0,None,i7186,680,4.416771781819168e-05 1727583992,1727584593,601,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04965206513315129 n_clusters 1,100,0.25,0.04965206513315129,1,0.26567776169802215,0,None,i7181,594,4.416771781819168e-05 1727583932,1727584630,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.051899044103794194 n_clusters 1,100,0.25,0.051899044103794194,1,0.26567776169802215,0,None,i7186,690,4.416771781819168e-05 1727582973,1727584648,1675,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 182 confidence 0.001 feature_proportion 0.03599513385301741 n_clusters 4,182,0.001,0.03599513385301741,4,0.31077182826821037,0,None,i7186,1667,0.0002067211647023611 1727584072,1727584754,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.18301085880682533 n_clusters 3,100,0.25,0.18301085880682533,3,0.26485648818137963,0,None,i7186,674,4.276925761077269e-05 1727584132,1727584756,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05294421205292206 n_clusters 1,100,0.25,0.05294421205292206,1,0.26567776169802215,0,None,i7181,617,4.416771781819168e-05 1727583681,1727584771,1090,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 293 confidence 0.01 feature_proportion 0.07586277793896073 n_clusters 4,293,0.01,0.07586277793896073,4,0.29280028943560055,0,None,i7186,1083,0.0001444509949268407 1727584102,1727584809,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 178 confidence 0.05 feature_proportion 0.07079130555104196 n_clusters 2,178,0.05,0.07079130555104196,2,0.28082730342498796,0,None,i7186,700,7.690058749793134e-05 1727584173,1727584855,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.18975954103204104 n_clusters 4,100,0.25,0.18975954103204104,4,0.2640062711046792,0,None,i7186,676,4.279966238346309e-05 1727584372,1727584867,495,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 234 confidence 0.05 feature_proportion 0.1769812183098049 n_clusters 1,234,0.05,0.1769812183098049,1,0.28721900627110464,0,None,i7179,489,9.495850495113819e-05 1727584172,1727584885,713,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 235 confidence 0.005 feature_proportion 0.10538270618989572 n_clusters 3,235,0.005,0.10538270618989572,3,0.2870224312590448,0,None,i7181,707,0.00010139002688629723 1727584300,1727584900,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.19112443938894286 n_clusters 3,100,0.25,0.19112443938894286,3,0.2656801736613603,0,None,i7181,594,4.265399023544723e-05 1727584280,1727584912,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.19808001373355866 n_clusters 4,100,0.1,0.19808001373355866,4,0.2649445248432224,0,None,i7181,627,4.359285677608009e-05 1727584220,1727584915,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04908887041068155 n_clusters 1,100,0.25,0.04908887041068155,1,0.26567776169802215,0,None,i7186,687,4.416771781819168e-05 1727584353,1727584931,578,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 158 confidence 0.25 feature_proportion 0.2 n_clusters 1,158,0.25,0.2,1,0.278221177038109,0,None,i7181,572,6.527152775750432e-05 1727584370,1727584944,574,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04886225850166967 n_clusters 1,100,0.25,0.04886225850166967,1,0.26567776169802215,0,None,i7168,505,4.416771781819168e-05 1727584280,1727585017,737,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 148 confidence 0.05 feature_proportion 0.005102395700533231 n_clusters 2,148,0.05,0.005102395700533231,2,0.2770501688374337,0,None,i7186,730,6.704214556115738e-05 1727584543,1727585272,729,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 198 confidence 0.001 feature_proportion 0.2 n_clusters 1,198,0.001,0.2,1,0.28335865894838397,0,None,i7186,721,8.693188362367758e-05 1727584683,1727585313,630,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 162 confidence 0.005 feature_proportion 0.1760905432901374 n_clusters 1,162,0.005,0.1760905432901374,1,0.2773794018330922,0,None,i7181,624,7.244843164918673e-05 1727584734,1727585376,642,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 147 confidence 0.005 feature_proportion 0.1613727003731346 n_clusters 1,147,0.005,0.1613727003731346,1,0.2796237337192474,0,None,i7181,636,6.683835347622907e-05 1727584663,1727585406,743,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.005 feature_proportion 0.1674147082446674 n_clusters 1,153,0.005,0.1674147082446674,1,0.28016883743367105,0,None,i7186,735,6.914854484795249e-05 1727584723,1727585569,846,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 419 confidence 0.001 feature_proportion 0.12321797700311828 n_clusters 4,419,0.001,0.12321797700311828,4,0.30426193921852385,0,None,i7181,839,0.00017574091865400288 1727585152,1727585666,514,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 184 confidence 0.005 feature_proportion 0.1982978524644628 n_clusters 1,184,0.005,0.1982978524644628,1,0.281319343945972,0,None,i7180,508,7.960277490422014e-05 1727585113,1727585713,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 194 confidence 0.005 feature_proportion 0.2 n_clusters 1,194,0.005,0.2,1,0.28435962373371926,0,None,i7181,594,8.267654406002471e-05 1727584883,1727585733,850,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 185 confidence 0.005 feature_proportion 0.1746529850887694 n_clusters 3,185,0.005,0.1746529850887694,3,0.2824553786782441,0,None,i7186,843,8.318970997710757e-05 1727585143,1727585738,595,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.18336794613121535 n_clusters 4,100,0.1,0.18336794613121535,4,0.2658876025084419,0,None,i7172,584,4.3487138896674536e-05 1727585113,1727585739,626,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 155 confidence 0.005 feature_proportion 0.16981653609774192 n_clusters 1,155,0.005,0.16981653609774192,1,0.2790520984081042,0,None,i7181,620,6.997881078005674e-05 1727584975,1727585759,784,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 192 confidence 0.001 feature_proportion 0.18643569452857173 n_clusters 2,192,0.001,0.18643569452857173,2,0.2854775687409551,0,None,i7181,777,9.194743093887275e-05 1727584883,1727585767,884,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.005 feature_proportion 0.15304043751328966 n_clusters 2,144,0.005,0.15304043751328966,2,0.28079956584659915,0,None,i7186,877,6.990667907304993e-05 1727584983,1727585771,788,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 177 confidence 0.001 feature_proportion 0.13900192838439995 n_clusters 2,177,0.001,0.13900192838439995,2,0.2809153400868307,0,None,i7181,782,8.816221264166578e-05 1727585093,1727585787,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.12488488972980578 n_clusters 1,100,0.005,0.12488488972980578,1,0.27143873613121083,0,None,i7181,688,5.09739251080771e-05 1727584963,1727585813,850,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 220 confidence 0.001 feature_proportion 0.17322980560506626 n_clusters 2,220,0.001,0.17322980560506626,2,0.28792209358417753,0,None,i7186,842,9.949875955132392e-05 1727584823,1727585832,1009,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 204 confidence 0.001 feature_proportion 0.168326320223342 n_clusters 3,204,0.001,0.168326320223342,3,0.28771587071876503,0,None,i7186,1002,9.744573865363056e-05 1727584825,1727585863,1038,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.001 feature_proportion 0.10196705448290642 n_clusters 3,183,0.001,0.10196705448290642,3,0.28481307284129276,0,None,i7186,1031,9.08250834718174e-05 1727585862,1727585866,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 4,100,0.025,0,4,None,1,None,i7186 1727584603,1727585867,1264,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.01 feature_proportion 0.09349768520680146 n_clusters 4,139,0.01,0.09349768520680146,4,0.28886517124939703,0,None,i7186,1257,9.945430878408036e-05 1727584674,1727585958,1284,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 202 confidence 0.001 feature_proportion 0.1455942830207212 n_clusters 4,202,0.001,0.1455942830207212,4,0.28713217559093107,0,None,i7186,1277,0.00010765613518548256 1727585093,1727585995,902,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 192 confidence 0.001 feature_proportion 0.18457201311669894 n_clusters 2,192,0.001,0.18457201311669894,2,0.2855475156777617,0,None,i7186,894,9.202989018221408e-05 1727585998,1727586001,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7181 1727585353,1727586046,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 857 confidence 0.001 feature_proportion 0.07869044586849368 n_clusters 1,857,0.001,0.07869044586849368,1,0.32163289917993243,0,None,i7181,686,0.00032998505549664426 1727585413,1727586080,667,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.25 feature_proportion 0.1993697326309305 n_clusters 4,111,0.25,0.1993697326309305,4,0.2684611673902557,0,None,i7186,660,4.688735063397682e-05 1727586110,1727586113,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7181 1727585306,1727586169,863,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,1000,0.001,0.2,4,0.32415701881331405,0,None,i7186,855,0.0003686354582215871 1727585607,1727586235,628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.1827458950723061 n_clusters 4,100,0.1,0.1827458950723061,4,0.2663687891944042,0,None,i7181,621,4.340625800265614e-05 1727585453,1727586391,938,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 125 confidence 0.01 feature_proportion 0.01368552830522405 n_clusters 3,125,0.01,0.01368552830522405,3,0.2869380125422094,0,None,i7181,932,8.015047614375535e-05 1727585722,1727586394,672,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 976 confidence 0.001 feature_proportion 0.2 n_clusters 3,976,0.001,0.2,3,0.3221611191509889,0,None,i7181,666,0.0003589785920690899 1727585862,1727586506,644,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 177 confidence 0.05 feature_proportion 0.026468955384571127 n_clusters 2,177,0.05,0.026468955384571127,2,0.27807042932947423,0,None,i7181,638,7.714231722343276e-05 1727586540,1727586543,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 569 confidence 0.001 feature_proportion 0 n_clusters 4,569,0.001,0,4,None,1,None,i7181 1727585842,1727586581,739,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 965 confidence 0.001 feature_proportion 0.19902070264085892 n_clusters 2,965,0.001,0.19902070264085892,2,0.3226350699469368,0,None,i7186,731,0.00035241576261215053 1727585758,1727586599,841,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 965 confidence 0.001 feature_proportion 0.2 n_clusters 4,965,0.001,0.2,4,0.32378798842257595,0,None,i7186,833,0.00035527957801128387 1727585967,1727586633,666,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 544 confidence 0.05 feature_proportion 0.1968245493722225 n_clusters 2,544,0.05,0.1968245493722225,2,0.30524722624216116,0,None,i7186,659,0.0002066835775107454 1727586631,1727586634,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727585842,1727586667,825,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 965 confidence 0.001 feature_proportion 0.2 n_clusters 4,965,0.001,0.2,4,0.3232380607814761,0,None,i7186,817,0.00035338299548494346 1727586070,1727586749,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.18383221168387257 n_clusters 4,100,0.25,0.18383221168387257,4,0.26477086348287504,0,None,i7186,672,4.2712373386509124e-05 1727586110,1727586751,641,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 155 confidence 0.05 feature_proportion 0.012644201833267138 n_clusters 2,155,0.05,0.012644201833267138,2,0.2790135069946936,0,None,i7181,635,6.915853336486949e-05 1727586269,1727586786,517,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 174 confidence 0.25 feature_proportion 0.18844131075619927 n_clusters 4,174,0.25,0.18844131075619927,4,0.27716956102267243,0,None,i7179,510,7.165473464954721e-05 1727585947,1727586786,839,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,1000,0.001,0.2,4,0.32514109985528217,0,None,i7186,832,0.000366022973950796 1727586089,1727586809,720,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 919 confidence 0.001 feature_proportion 0.19885269369997338 n_clusters 1,919,0.001,0.19885269369997338,1,0.31996502653159675,0,None,i7186,713,0.0003382456895395258 1727586832,1727586836,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 1,250,0.25,0,1,None,1,None,i7186 1727586209,1727586843,634,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 976 confidence 0.01 feature_proportion 0.2 n_clusters 4,976,0.01,0.2,4,0.3231017848528702,0,None,i7181,628,0.00035227920449587666 1727586289,1727586851,562,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 107 confidence 0.05 feature_proportion 0.11172812390669068 n_clusters 1,107,0.05,0.11172812390669068,1,0.2696116739025567,0,None,i7179,556,4.796197234109352e-05 1727586250,1727586869,619,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 159 confidence 0.025 feature_proportion 0.1803408615695623 n_clusters 2,159,0.025,0.1803408615695623,2,0.28105402797877477,0,None,i7181,613,6.879833592753609e-05 1727586179,1727586895,716,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 920 confidence 0.001 feature_proportion 0.19883464993636923 n_clusters 1,920,0.001,0.19883464993636923,1,0.32336227689339125,0,None,i7186,708,0.00033438955973045143 1727586912,1727586916,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727586190,1727586929,739,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 1000 confidence 0.001 feature_proportion 0.2 n_clusters 4,1000,0.001,0.2,4,0.3248540762180415,0,None,i7181,733,0.0003677608567100093 1727586209,1727586947,738,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.001 feature_proportion 0.11707064094212705 n_clusters 1,111,0.001,0.11707064094212705,1,0.27852870236372407,0,None,i7181,731,5.865401359118626e-05 1727586953,1727586956,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 106 confidence 0.005 feature_proportion 0 n_clusters 1,106,0.005,0,1,None,1,None,i7186 1727586434,1727587018,584,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.060642060464390274 n_clusters 1,100,0.25,0.060642060464390274,1,0.26567776169802215,0,None,i7181,578,4.416771781819168e-05 1727586450,1727587042,592,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06022573087067556 n_clusters 1,100,0.25,0.06022573087067556,1,0.26567776169802215,0,None,i7181,585,4.416771781819168e-05 1727587154,1727587157,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 1,250,0.25,0,1,None,1,None,i7181 1727587194,1727587197,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 260 confidence 0.05 feature_proportion 0 n_clusters 2,260,0.05,0,2,None,1,None,i7181 1727586592,1727587273,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06258018549953126 n_clusters 1,100,0.25,0.06258018549953126,1,0.26567776169802215,0,None,i7186,673,4.416771781819168e-05 1727587293,1727587297,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 212 confidence 0.025 feature_proportion 0 n_clusters 1,212,0.025,0,1,None,1,None,i7186 1727586652,1727587305,653,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 187 confidence 0.25 feature_proportion 0.05285073723803399 n_clusters 1,187,0.25,0.05285073723803399,1,0.28032561505065123,0,None,i7186,646,7.692728730126857e-05 1727587322,1727587325,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 249 confidence 0.25 feature_proportion 0 n_clusters 1,249,0.25,0,1,None,1,None,i7181 1727586712,1727587364,652,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 375 confidence 0.1 feature_proportion 0.1538315050303936 n_clusters 1,375,0.1,0.1538315050303936,1,0.298052339604438,0,None,i7186,645,0.00014574357148586833 1727586751,1727587375,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 420 confidence 0.25 feature_proportion 0.01294656774569466 n_clusters 2,420,0.25,0.01294656774569466,2,0.30169319826338636,0,None,i7181,617,0.0001616903275830944 1727587409,1727587413,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 268 confidence 0.25 feature_proportion 0 n_clusters 1,268,0.25,0,1,None,1,None,i7181 1727586832,1727587495,663,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 348 confidence 0.25 feature_proportion 0.09050181957213307 n_clusters 2,348,0.25,0.09050181957213307,2,0.29532682103232033,0,None,i7186,655,0.00013665085048013495 1727587504,1727587507,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 261 confidence 0.01 feature_proportion 0 n_clusters 2,261,0.01,0,2,None,1,None,i7186 1727587564,1727587568,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 255 confidence 0.25 feature_proportion 0 n_clusters 1,255,0.25,0,1,None,1,None,i7186 1727587629,1727587633,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 255 confidence 0.25 feature_proportion 0 n_clusters 1,255,0.25,0,1,None,1,None,i7186 1727585987,1727587648,1661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 187 confidence 0.001 feature_proportion 0.023391348905394158 n_clusters 4,187,0.001,0.023391348905394158,4,0.3126929570670526,0,None,i7186,1653,0.0002010978405460253 1727585987,1727587667,1680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 188 confidence 0.001 feature_proportion 0.022440683374965957 n_clusters 4,188,0.001,0.022440683374965957,4,0.3143704775687409,0,None,i7186,1672,0.000203341595075136 1727586962,1727587742,780,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.07007883168114253 n_clusters 1,100,0.01,0.07007883168114253,1,0.27374216111915095,0,None,i7186,773,5.191031788520103e-05 1727587745,1727587748,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 1,258,0.25,0,1,None,1,None,i7186 1727587143,1727587816,673,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06378302445427186 n_clusters 1,100,0.25,0.06378302445427186,1,0.26567776169802215,0,None,i7181,666,4.416771781819168e-05 1727587134,1727587817,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06382819077598322 n_clusters 1,100,0.25,0.06382819077598322,1,0.26567776169802215,0,None,i7186,674,4.416771781819168e-05 1727587194,1727587821,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 350 confidence 0.25 feature_proportion 0.08958674138326005 n_clusters 2,350,0.25,0.08958674138326005,2,0.2944826338639652,0,None,i7181,620,0.00013770832062662993 1727587172,1727587851,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 259 confidence 0.05 feature_proportion 0.011784025878387004 n_clusters 2,259,0.05,0.011784025878387004,2,0.2875759768451519,0,None,i7181,671,0.00010716299129768637 1727587849,1727587853,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7186 1727587865,1727587868,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 4,258,0.25,0,4,None,1,None,i7186 1727587203,1727587954,751,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 298 confidence 0.005 feature_proportion 0.10392661240470305 n_clusters 3,298,0.005,0.10392661240470305,3,0.29244452484322236,0,None,i7181,744,0.00012353508697256037 1727586952,1727587975,1023,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.01 feature_proportion 0.04197736899398484 n_clusters 3,140,0.01,0.04197736899398484,3,0.2837952243125904,0,None,i7186,1015,8.360376650070766e-05 1727587302,1727588064,762,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 187 confidence 0.01 feature_proportion 0.01846570031096484 n_clusters 2,187,0.01,0.01846570031096484,2,0.28414013506994695,0,None,i7186,754,8.51362774664555e-05 1727587474,1727588098,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 231 confidence 0.25 feature_proportion 0.025574268507030123 n_clusters 1,231,0.25,0.025574268507030123,1,0.28495658465991314,0,None,i7181,616,9.377721609574932e-05 1727587744,1727588212,468,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 236 confidence 0.25 feature_proportion 0.029641443862270928 n_clusters 1,236,0.25,0.029641443862270928,1,0.28377834056922335,0,None,i7180,462,9.597390535516818e-05 1727587383,1727588256,873,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 105 confidence 0.001 feature_proportion 0.11203274562236376 n_clusters 1,105,0.001,0.11203274562236376,1,0.27848166907863003,0,None,i7186,865,5.695936408343774e-05 1727587489,1727588266,777,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 confidence 0.025 feature_proportion 0.04540923337862654 n_clusters 2,118,0.025,0.04540923337862654,2,0.2790738060781476,0,None,i7181,770,5.899820211312176e-05 1727587134,1727588330,1196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 145 confidence 0.01 feature_proportion 0.09057552982394523 n_clusters 4,145,0.01,0.09057552982394523,4,0.289294500723589,0,None,i7181,1189,0.00010153382091205745 1727587649,1727588331,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06238257685979172 n_clusters 1,100,0.25,0.06238257685979172,1,0.26567776169802215,0,None,i7186,675,4.416771781819168e-05 1727587684,1727588332,648,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 316 confidence 0.25 feature_proportion 0.11582719147245715 n_clusters 1,316,0.25,0.11582719147245715,1,0.2937698986975398,0,None,i7186,640,0.00012383100852497296 1727587569,1727588395,826,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 260 confidence 0.01 feature_proportion 0.02492078200245467 n_clusters 3,260,0.01,0.02492078200245467,3,0.2900108538350217,0,None,i7181,819,0.00011813711281732612 1727586712,1727588592,1880,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 4,100,0.001,0.2,4,0.28877110467920886,0,None,i7186,1872,8.811251989740635e-05 1727587955,1727588609,654,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 185 confidence 0.25 feature_proportion 0.024488023150332335 n_clusters 2,185,0.25,0.024488023150332335,2,0.2801350699469368,0,None,i7186,647,7.62927753091649e-05 1727587985,1727588636,651,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 275 confidence 0.25 feature_proportion 0.2 n_clusters 4,275,0.25,0.2,4,0.2914061746261457,0,None,i7186,644,0.0001084174317363125 1727588015,1727588655,640,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 232 confidence 0.05 feature_proportion 0.1943904325775107 n_clusters 1,232,0.05,0.1943904325775107,1,0.2844006271104679,0,None,i7181,633,9.465347670415752e-05 1727587975,1727588657,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.062191181517896245 n_clusters 1,100,0.25,0.062191181517896245,1,0.26567776169802215,0,None,i7186,675,4.416771781819168e-05 1727587995,1727588676,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06178930787229753 n_clusters 1,100,0.25,0.06178930787229753,1,0.26567776169802215,0,None,i7186,674,4.416771781819168e-05 1727588095,1727588751,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06280946590315978 n_clusters 1,100,0.25,0.06280946590315978,1,0.26567776169802215,0,None,i7181,648,4.416771781819168e-05 1727588075,1727588758,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06429744896291234 n_clusters 1,100,0.25,0.06429744896291234,1,0.26567776169802215,0,None,i7186,676,4.416771781819168e-05 1727588106,1727588763,657,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.01 feature_proportion 0.03174220267972751 n_clusters 1,181,0.01,0.03174220267972751,1,0.28056439942112876,0,None,i7181,651,7.895976587755446e-05 1727588162,1727588801,639,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 226 confidence 0.05 feature_proportion 0.17518959312271215 n_clusters 2,226,0.05,0.17518959312271215,2,0.2850856246985046,0,None,i7181,632,9.24649595326151e-05 1727588182,1727588806,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 316 confidence 0.25 feature_proportion 0.08650883699418101 n_clusters 1,316,0.25,0.08650883699418101,1,0.29309816690786306,0,None,i7181,617,0.0001247547281520952 1727588876,1727588880,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 545 confidence 0.001 feature_proportion 0 n_clusters 1,545,0.001,0,1,None,1,None,i7186 1727587322,1727588921,1599,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 296 confidence 0.001 feature_proportion 0.050740103637683255 n_clusters 4,296,0.001,0.050740103637683255,4,0.3058755426917511,0,None,i7186,1590,0.00020160461002853222 1727588274,1727588952,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 222 confidence 0.1 feature_proportion 0.15252021616578704 n_clusters 2,222,0.1,0.15252021616578704,2,0.2850506512301013,0,None,i7127,639,8.990246497051517e-05 1727588308,1727588959,651,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 349 confidence 0.25 feature_proportion 0.0863693636206429 n_clusters 2,349,0.25,0.0863693636206429,2,0.2952556681138446,0,None,i7186,643,0.00013720400123387515 1727588368,1727589048,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.07125451913767407 n_clusters 1,100,0.25,0.07125451913767407,1,0.26567776169802215,0,None,i7186,673,4.416771781819168e-05 1727589057,1727589061,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 562 confidence 0.001 feature_proportion 0 n_clusters 1,562,0.001,0,1,None,1,None,i7186 1727588467,1727589101,634,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06985146886672522 n_clusters 1,100,0.25,0.06985146886672522,1,0.26567776169802215,0,None,i7181,628,4.416771781819168e-05 1727588467,1727589112,645,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0703603185764559 n_clusters 1,100,0.25,0.0703603185764559,1,0.26567776169802215,0,None,i7181,639,4.416771781819168e-05 1727588527,1727589164,637,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 239 confidence 0.05 feature_proportion 0.18481206079458645 n_clusters 3,239,0.05,0.18481206079458645,3,0.286810178485287,0,None,i7181,630,9.682728085690993e-05 1727588448,1727589198,750,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 129 confidence 0.05 feature_proportion 0.01789186612193914 n_clusters 2,129,0.05,0.01789186612193914,2,0.27516883743367104,0,None,i7186,743,6.00123672237829e-05 1727588636,1727589337,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06960818702850273 n_clusters 1,100,0.25,0.06960818702850273,1,0.26567776169802215,0,None,i7186,693,4.416771781819168e-05 1727588736,1727589421,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0669812107914199 n_clusters 1,100,0.25,0.0669812107914199,1,0.26567776169802215,0,None,i7186,678,4.416771781819168e-05 1727588716,1727589428,712,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 102 confidence 0.1 feature_proportion 0.1472339299739528 n_clusters 2,102,0.1,0.1472339299739528,2,0.2679787747226242,0,None,i7186,705,4.467458568972361e-05 1727588836,1727589510,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 272 confidence 0.25 feature_proportion 0.060280831158161165 n_clusters 4,272,0.25,0.060280831158161165,4,0.2874770863482875,0,None,i7186,667,0.00010943125585020909 1727588856,1727589545,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0667747216533051 n_clusters 1,100,0.25,0.0667747216533051,1,0.26567776169802215,0,None,i7186,681,4.416771781819168e-05 1727589077,1727589765,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0681063680890741 n_clusters 1,100,0.25,0.0681063680890741,1,0.26567776169802215,0,None,i7186,681,4.416771781819168e-05 1727589069,1727589791,722,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0672917732711871 n_clusters 1,100,0.25,0.0672917732711871,1,0.26567776169802215,0,None,i7186,715,4.416771781819168e-05 1727589017,1727589806,789,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 327 confidence 0.05 feature_proportion 0.03184188626523914 n_clusters 4,327,0.05,0.03184188626523914,4,0.29062952243125906,0,None,i7186,781,0.00013776513324270198 1727589037,1727589849,812,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.07345695853007506 n_clusters 2,100,0.05,0.07345695853007506,2,0.27518330921369993,0,None,i7186,805,5.032684918546748e-05 1727589257,1727589889,632,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06797047692041795 n_clusters 1,100,0.25,0.06797047692041795,1,0.26567776169802215,0,None,i7181,626,4.416771781819168e-05 1727589277,1727589898,621,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 337 confidence 0.25 feature_proportion 0.1986532196664561 n_clusters 3,337,0.25,0.1986532196664561,3,0.29649662325132653,0,None,i7181,614,0.00013078228468860556 1727589277,1727589911,634,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 147 confidence 0.05 feature_proportion 0.029684903280067117 n_clusters 1,147,0.05,0.029684903280067117,1,0.2760311143270622,0,None,i7181,627,6.45635129699503e-05 1727589297,1727589917,620,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 290 confidence 0.1 feature_proportion 0.18613279041516917 n_clusters 4,290,0.1,0.18613279041516917,4,0.29080077182826825,0,None,i7181,614,0.00011511566051641475 1727589237,1727589926,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06771303297479074 n_clusters 1,100,0.25,0.06771303297479074,1,0.26567776169802215,0,None,i7186,682,4.416771781819168e-05 1727589310,1727589934,624,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 448 confidence 0.05 feature_proportion 0.13343219328671693 n_clusters 3,448,0.05,0.13343219328671693,3,0.3030041003376749,0,None,i7181,617,0.00017182508383974037 1727589446,1727590106,660,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 461 confidence 0.25 feature_proportion 0.11927231034107566 n_clusters 2,461,0.25,0.11927231034107566,2,0.3006054027978775,0,None,i7186,652,0.00017670048787196072 1727589486,1727590140,654,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 242 confidence 0.1 feature_proportion 0.15656080559763325 n_clusters 3,242,0.1,0.15656080559763325,3,0.2871599131693199,0,None,i7186,646,9.723679184203383e-05 1727589461,1727590146,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 279 confidence 0.05 feature_proportion 0.027026415231706832 n_clusters 2,279,0.05,0.027026415231706832,2,0.2884647853352629,0,None,i7186,679,0.00011484652969001607 1727589486,1727590230,744,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.06637454629180493 n_clusters 2,100,0.05,0.06637454629180493,2,0.2748878437047757,0,None,i7181,737,5.026690627269498e-05 1727589586,1727590236,650,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.09010396677573437 n_clusters 3,100,0.25,0.09010396677573437,3,0.26648094548962853,0,None,i7181,643,4.421669814143797e-05 1727589566,1727590264,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06595276934144263 n_clusters 1,100,0.25,0.06595276934144263,1,0.26567776169802215,0,None,i7186,691,4.416771781819168e-05 1727589581,1727590265,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06595718151051914 n_clusters 1,100,0.25,0.06595718151051914,1,0.26567776169802215,0,None,i7186,677,4.416771781819168e-05 1727589653,1727590275,622,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 186 confidence 0.01 feature_proportion 0.07058979128077617 n_clusters 1,186,0.01,0.07058979128077617,1,0.2831874095513748,0,None,i7181,615,8.014653326898352e-05 1727589672,1727590440,768,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 272 confidence 0.025 feature_proportion 0.0057479909761411815 n_clusters 3,272,0.025,0.0057479909761411815,3,0.2873649300530632,0,None,i7127,761,0.00011731531191016883 1727589822,1727590514,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 477 confidence 0.025 feature_proportion 0.03822566045395981 n_clusters 1,477,0.025,0.03822566045395981,1,0.3010299083453931,0,None,i7186,684,0.00018898602902237677 1727589913,1727590595,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06405203627088253 n_clusters 1,100,0.25,0.06405203627088253,1,0.26567776169802215,0,None,i7186,676,4.416771781819168e-05 1727590063,1727590713,650,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06423155382224964 n_clusters 1,100,0.25,0.06423155382224964,1,0.26567776169802215,0,None,i7181,643,4.416771781819168e-05 1727590073,1727590723,650,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06445151450498358 n_clusters 1,100,0.25,0.06445151450498358,1,0.26567776169802215,0,None,i7181,642,4.416771781819168e-05 1727590033,1727590771,738,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 755 confidence 0.01 feature_proportion 0.10495878186851733 n_clusters 4,755,0.01,0.10495878186851733,4,0.31349011095031354,0,None,i7186,731,0.0002829640967152467 1727590053,1727590869,816,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 612 confidence 0.005 feature_proportion 0.06421753783718538 n_clusters 2,612,0.005,0.06421753783718538,2,0.30858297153883263,0,None,i7186,808,0.0002486949417018379 1727590173,1727590871,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06466857126430349 n_clusters 1,100,0.25,0.06466857126430349,1,0.26567776169802215,0,None,i7186,690,4.416771781819168e-05 1727590193,1727590888,695,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 386 confidence 0.025 feature_proportion 0.03753367013555642 n_clusters 1,386,0.025,0.03753367013555642,1,0.2991087795465509,0,None,i7186,687,0.00015463176501909217 1727590183,1727590916,733,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 538 confidence 0.01 feature_proportion 0.10704030812090633 n_clusters 3,538,0.01,0.10704030812090633,3,0.3081162566328992,0,None,i7186,726,0.000206424531900746 1727589893,1727590944,1051,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 171 confidence 0.005 feature_proportion 0.09936048074715448 n_clusters 3,171,0.005,0.09936048074715448,3,0.2885914134105162,0,None,i7186,1043,0.00010081667144334337 1727590277,1727590980,703,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06431670588566109 n_clusters 2,100,0.25,0.06431670588566109,2,0.2673420164013507,0,None,i7186,695,4.393152018726579e-05 1727590304,1727590988,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06420408349912698 n_clusters 1,100,0.25,0.06420408349912698,1,0.26567776169802215,0,None,i7186,677,4.416771781819168e-05 1727590317,1727590995,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.16160648023166174 n_clusters 3,100,0.25,0.16160648023166174,3,0.26550892426435113,0,None,i7181,670,4.268506173568703e-05 1727590596,1727591096,500,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1195957620801955 n_clusters 4,100,0.25,0.1195957620801955,4,0.265844187168355,0,None,i7180,491,4.2582572481263024e-05 1727590457,1727591125,668,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12107051669413285 n_clusters 4,100,0.25,0.12107051669413285,4,0.2647817173178968,0,None,i7181,661,4.273690243199687e-05 1727590477,1727591145,668,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06333725025201413 n_clusters 1,100,0.25,0.06333725025201413,1,0.26567776169802215,0,None,i7181,661,4.416771781819168e-05 1727590497,1727591158,661,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06354306948040991 n_clusters 1,100,0.25,0.06354306948040991,1,0.26567776169802215,0,None,i7181,655,4.416771781819168e-05 1727590597,1727591283,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 145 confidence 0.1 feature_proportion 0.1510337755084038 n_clusters 3,145,0.1,0.1510337755084038,3,0.2761010612638688,0,None,i7186,679,6.080726036144614e-05 1727590514,1727591290,776,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.01 feature_proportion 0.07147344248924387 n_clusters 2,141,0.01,0.07147344248924387,2,0.28031837916063673,0,None,i7181,769,7.108233681163836e-05 1727590657,1727591335,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12246128251445657 n_clusters 4,100,0.25,0.12246128251445657,4,0.26573564881813794,0,None,i7186,671,4.260085029000738e-05 1727590755,1727591360,605,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 665 confidence 0.25 feature_proportion 0.04209027234920713 n_clusters 1,665,0.25,0.04209027234920713,1,0.30916304872165945,0,None,i7181,598,0.00025044317136598503 1727590864,1727591460,596,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12151627412457403 n_clusters 4,100,0.25,0.12151627412457403,4,0.2649638205499276,0,None,i7181,589,4.268903839230757e-05 1727590808,1727591482,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.1 feature_proportion 0.11169698532776076 n_clusters 3,169,0.1,0.11169698532776076,3,0.2796478533526291,0,None,i7186,667,6.987022421794504e-05 1727590964,1727591660,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06313347374851037 n_clusters 1,100,0.25,0.06313347374851037,1,0.26567776169802215,0,None,i7186,689,4.416771781819168e-05 1727590984,1727591697,713,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.2 n_clusters 1,100,0.05,0.2,1,0.2672624216111915,0,None,i7186,706,4.534484838437128e-05 1727591104,1727591780,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.1 feature_proportion 0.11345020891251463 n_clusters 3,169,0.1,0.11345020891251463,3,0.2787626628075254,0,None,i7186,669,6.996633997697888e-05 1727591124,1727591812,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06323784853322144 n_clusters 1,100,0.25,0.06323784853322144,1,0.26567776169802215,0,None,i7186,681,4.416771781819168e-05 1727591084,1727591895,811,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 109 confidence 0.025 feature_proportion 0.16998745016180045 n_clusters 2,109,0.025,0.16998745016180045,2,0.27102508441871687,0,None,i7186,804,5.138246365479889e-05 1727591324,1727592011,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1251393811978022 n_clusters 4,100,0.25,0.1251393811978022,4,0.2648576941630487,0,None,i7186,680,4.2701872639118426e-05 1727591371,1727592079,708,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.19196042784869563 n_clusters 3,100,0.25,0.19196042784869563,3,0.26475759768451523,0,None,i7181,700,4.2760518320993185e-05 1727591344,1727592116,772,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.05 feature_proportion 0.15601469258183912 n_clusters 3,104,0.05,0.15601469258183912,3,0.26809816690786303,0,None,i7186,764,4.651174050891464e-05 1727590497,1727592125,1628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 2.5935531194014214e-08 n_clusters 4,100,0.001,0.00000002593553119401,4,0.31324167872648334,0,None,i7181,1621,0.00021102340740265625 1727591550,1727592210,660,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 252 confidence 0.05 feature_proportion 0.1648691374609489 n_clusters 3,252,0.05,0.1648691374609489,3,0.2872310660877955,0,None,i7181,651,0.00010217127368802085 1727591530,1727592237,707,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 196 confidence 0.025 feature_proportion 0.19808890657173128 n_clusters 2,196,0.025,0.19808890657173128,2,0.28338880849011094,0,None,i7186,699,8.214354788259466e-05 1727591567,1727592345,778,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 confidence 0.025 feature_proportion 0.15717428087535174 n_clusters 3,135,0.025,0.15717428087535174,3,0.27368427399903517,0,None,i7181,769,6.007244896473282e-05 1727591590,1727592420,830,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.005 feature_proportion 0.14908108402510276 n_clusters 2,153,0.005,0.14908108402510276,2,0.27893391220453445,0,None,i7181,822,7.261202973043147e-05 1727591770,1727592430,660,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12050022866756178 n_clusters 4,100,0.25,0.12050022866756178,4,0.2660371442354076,0,None,i7181,653,4.255923748706148e-05 1727591778,1727592451,673,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06358474000178767 n_clusters 1,100,0.25,0.06358474000178767,1,0.26567776169802215,0,None,i7127,666,4.416771781819168e-05 1727591710,1727592472,762,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 196 confidence 0.01 feature_proportion 0.06871266802665003 n_clusters 2,196,0.01,0.06871266802665003,2,0.2828027013989387,0,None,i7186,754,8.868927695606781e-05 1727591590,1727592482,892,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 102 confidence 0.01 feature_proportion 0.16651254261986848 n_clusters 2,102,0.01,0.16651254261986848,2,0.2741931982633864,0,None,i7181,885,5.312413548949661e-05 1727591688,1727592492,804,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.025 feature_proportion 0.1779347614742522 n_clusters 2,116,0.025,0.1779347614742522,2,0.2710709117221418,0,None,i7186,797,5.399043848930384e-05 1727591610,1727592500,890,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.16106304132475036 n_clusters 2,100,0.01,0.16106304132475036,2,0.2726941630487216,0,None,i7181,882,5.2305001661444834e-05 1727591830,1727592521,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 283 confidence 0.1 feature_proportion 0.09108562656736652 n_clusters 2,283,0.1,0.09108562656736652,2,0.289739507959479,0,None,i7186,684,0.00011472211501448443 1727591890,1727592573,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12043256604562819 n_clusters 4,100,0.25,0.12043256604562819,4,0.26563434635793537,0,None,i7186,675,4.260279717750709e-05 1727591950,1727592628,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.26614085865894843,0,None,i7186,671,4.302539077375205e-05 1727592070,1727592752,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12363274432531575 n_clusters 4,100,0.25,0.12363274432531575,4,0.2657730342498794,0,None,i7186,674,4.259117726037484e-05 1727591344,1727592797,1453,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 102 confidence 0.001 feature_proportion 0.19185799392435385 n_clusters 3,102,0.001,0.19185799392435385,3,0.2838458755426918,0,None,i7186,1445,7.377204757633631e-05 1727591297,1727592802,1505,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.02605004116376261 n_clusters 4,100,0.005,0.02605004116376261,4,0.29682826821032315,0,None,i7186,1498,0.00011440370887267396 1727592130,1727592804,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12488303550457656 n_clusters 4,100,0.25,0.12488303550457656,4,0.2652508441871684,0,None,i7186,666,4.2638858175622654e-05 1727592350,1727592961,611,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 453 confidence 0.25 feature_proportion 0.11956186965107918 n_clusters 1,453,0.25,0.11956186965107918,1,0.3028750602990834,0,None,i7181,603,0.00017245999929766588 1727592397,1727593007,610,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12236352123105228 n_clusters 4,100,0.25,0.12236352123105228,4,0.26580680173661364,0,None,i7181,603,4.2602549876518914e-05 1727592337,1727593014,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12748967632235056 n_clusters 4,100,0.25,0.12748967632235056,4,0.26592016401350704,0,None,i7186,670,4.258368392432707e-05 1727592237,1727593045,808,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 155 confidence 0.05 feature_proportion 0.05839852612852514 n_clusters 4,155,0.05,0.05839852612852514,4,0.2817233478051133,0,None,i7181,801,7.550777705160693e-05 1727592524,1727593218,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1238453435750869 n_clusters 4,100,0.25,0.1238453435750869,4,0.2654775687409552,0,None,i7186,687,4.263722151626513e-05 1727592544,1727593225,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12285894993655003 n_clusters 4,100,0.25,0.12285894993655003,4,0.2663326097443319,0,None,i7186,674,4.249780871060999e-05 1727592217,1727593324,1107,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 771 confidence 0.01 feature_proportion 0.02154836062102535 n_clusters 4,771,0.01,0.02154836062102535,4,0.3198890496864447,0,None,i7181,1097,0.00032119658332917483 1727592764,1727593469,705,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 835 confidence 0.025 feature_proportion 0.12625984121114017 n_clusters 4,835,0.025,0.12625984121114017,4,0.3136275928605885,0,None,i7186,697,0.00030991027103394143 1727592801,1727593484,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.121736300193025 n_clusters 3,100,0.25,0.121736300193025,3,0.26541726965750123,0,None,i7186,676,4.2716855611101143e-05 1727592824,1727593510,686,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06352794135895319 n_clusters 1,100,0.25,0.06352794135895319,1,0.26567776169802215,0,None,i7186,677,4.416771781819168e-05 1727592741,1727593536,795,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 812 confidence 0.01 feature_proportion 0.02405332755292848 n_clusters 2,812,0.01,0.02405332755292848,2,0.3191521948866377,0,None,i7186,788,0.0003122333167495102 1727592785,1727593562,777,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.005 feature_proportion 0.035321528960216314 n_clusters 2,258,0.005,0.035321528960216314,2,0.28873613121080555,0,None,i7186,769,0.00011259348420147518 1727592804,1727593581,777,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 confidence 0.05 feature_proportion 0.09649875690663527 n_clusters 2,118,0.05,0.09649875690663527,2,0.274540520984081,0,None,i7186,769,5.627973746834447e-05 1727593020,1727593694,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12154697113615692 n_clusters 3,100,0.25,0.12154697113615692,3,0.2651447178002895,0,None,i7186,665,4.2729170548096096e-05 1727593025,1727593698,673,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12180548817401815 n_clusters 3,100,0.25,0.12180548817401815,3,0.26455740472744815,0,None,i7186,666,4.27795756310944e-05 1727593072,1727593711,639,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06295257865827965 n_clusters 1,100,0.25,0.06295257865827965,1,0.26567776169802215,0,None,i7181,632,4.416771781819168e-05 1727593065,1727593731,666,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 275 confidence 0.025 feature_proportion 0.014338094393113144 n_clusters 2,275,0.025,0.014338094393113144,2,0.2853123492522913,0,None,i7181,659,0.00011606668868235437 1727593222,1727593891,669,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03617982868935075 n_clusters 3,100,0.25,0.03617982868935075,3,0.2667185238784371,0,None,i7181,662,4.432062525394093e-05 1727593265,1727593909,644,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06431871714767383 n_clusters 1,100,0.25,0.06431871714767383,1,0.26567776169802215,0,None,i7181,637,4.416771781819168e-05 1727593348,1727593916,568,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06398466543824648 n_clusters 1,100,0.25,0.06398466543824648,1,0.26567776169802215,0,None,i7181,563,4.416771781819168e-05 1727593253,1727593946,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 279 confidence 0.025 feature_proportion 0.015026445603824982 n_clusters 2,279,0.025,0.015026445603824982,2,0.28928364688856734,0,None,i7186,687,0.00011605785032745467 1727593245,1727593954,709,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 278 confidence 0.025 feature_proportion 0.014138993314002668 n_clusters 2,278,0.025,0.014138993314002668,2,0.29004823926676315,0,None,i7186,701,0.00011533986625014274 1727593368,1727594006,638,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06413172978559796 n_clusters 1,100,0.25,0.06413172978559796,1,0.26567776169802215,0,None,i7181,631,4.416771781819168e-05 1727593423,1727594238,815,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 124 confidence 0.01 feature_proportion 0.19046893406275345 n_clusters 3,124,0.01,0.19046893406275345,3,0.2758176555716353,0,None,i7181,808,5.9245337392949556e-05 1727593553,1727594249,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03638746463191745 n_clusters 3,100,0.25,0.03638746463191745,3,0.2654618909792571,0,None,i7186,689,4.416709535605336e-05 1727593042,1727594373,1331,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.05295512385112897 n_clusters 4,100,0.01,0.05295512385112897,4,0.28846719729860104,0,None,i7181,1324,9.112065312978925e-05 1727593783,1727594459,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11551397152692096 n_clusters 3,100,0.25,0.11551397152692096,3,0.265675349734684,0,None,i7186,668,4.265457431665274e-05 1727593783,1727594510,727,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 277 confidence 0.025 feature_proportion 0.014054262298149836 n_clusters 2,277,0.025,0.014054262298149836,2,0.28824770863482874,0,None,i7186,719,0.00011560721261609054 1727593763,1727594658,895,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 472 confidence 0.025 feature_proportion 0.03968604189618377 n_clusters 4,472,0.025,0.03968604189618377,4,0.30346840328027014,0,None,i7186,888,0.00019804223912824233 1727593975,1727594667,692,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11843240982643351 n_clusters 3,100,0.25,0.11843240982643351,3,0.2647310660877955,0,None,i7186,684,4.278963209304948e-05 1727594005,1727594690,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.18962725278211146 n_clusters 4,100,0.25,0.18962725278211146,4,0.26569343945972024,0,None,i7186,678,4.2600802945482976e-05 1727594047,1727594726,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11910809525993377 n_clusters 3,100,0.25,0.11910809525993377,3,0.2645767004341534,0,None,i7186,672,4.2834276191729165e-05 1727594027,1727594742,715,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03530123616706065 n_clusters 3,100,0.25,0.03530123616706065,3,0.2671912686927159,0,None,i7186,708,4.412178746203812e-05 1727594065,1727594926,861,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 415 confidence 0.01 feature_proportion 0.045053543032174376 n_clusters 3,415,0.01,0.045053543032174376,3,0.3015991316931983,0,None,i7186,854,0.00017683641549621056 1727594365,1727594967,602,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1177345713429383 n_clusters 3,100,0.25,0.1177345713429383,3,0.26387602508441876,0,None,i7181,596,4.290360522616021e-05 1727594335,1727594970,635,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03842859397978785 n_clusters 3,100,0.25,0.03842859397978785,3,0.2663687891944042,0,None,i7181,628,4.418078535078094e-05 1727594327,1727594994,667,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 405 confidence 0.25 feature_proportion 0.03548597743412614 n_clusters 2,405,0.25,0.03548597743412614,2,0.29858900144717804,0,None,i7186,659,0.0001575582030432861 1727594387,1727594994,607,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 428 confidence 0.25 feature_proportion 0.0267284908037021 n_clusters 2,428,0.25,0.0267284908037021,2,0.3007115291847564,0,None,i7181,601,0.00016488978681453354 1727594407,1727595015,608,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1170769180306647 n_clusters 3,100,0.25,0.1170769180306647,3,0.2657863000482392,0,None,i7181,602,4.265146893153443e-05 1727593723,1727595103,1380,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.01 feature_proportion 0.06321520610142659 n_clusters 4,101,0.01,0.06321520610142659,4,0.2902279305354558,0,None,i7186,1372,9.31849725562875e-05 1727594566,1727595179,613,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0651768164247895 n_clusters 1,100,0.25,0.0651768164247895,1,0.26567776169802215,0,None,i7181,607,4.416771781819168e-05 1727594546,1727595202,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 188 confidence 0.1 feature_proportion 0.13253176901344674 n_clusters 3,188,0.1,0.13253176901344674,3,0.27940906898215145,0,None,i7186,648,7.740064244027606e-05 1727594526,1727595222,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.10654812282484075 n_clusters 1,100,0.1,0.10654812282484075,1,0.2667378195851423,0,None,i7186,689,4.416771371111377e-05 1727594675,1727595401,726,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.1 feature_proportion 0.18156215295061717 n_clusters 2,101,0.1,0.18156215295061717,2,0.26479257115291843,0,None,i7186,719,4.48181355156092e-05 1727594365,1727595402,1037,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 177 confidence 0.01 feature_proportion 0.07197262861020591 n_clusters 4,177,0.01,0.07197262861020591,4,0.2903424987940183,0,None,i7181,1030,0.00010953426286255456 1727594655,1727595421,766,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.05 feature_proportion 0.08590651267701112 n_clusters 3,111,0.05,0.08590651267701112,3,0.27620356970574045,0,None,i7181,760,5.7699798971834965e-05 1727594815,1727595502,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06475437390321498 n_clusters 1,100,0.25,0.06475437390321498,1,0.26567776169802215,0,None,i7186,680,4.416771781819168e-05 1727594775,1727595507,732,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 123 confidence 0.05 feature_proportion 0.11616486061895162 n_clusters 3,123,0.05,0.11616486061895162,3,0.27126145682585623,0,None,i7186,725,5.3768303689525856e-05 1727594935,1727595623,688,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06525752297136397 n_clusters 1,100,0.25,0.06525752297136397,1,0.26567776169802215,0,None,i7186,680,4.416771781819168e-05 1727594935,1727595664,729,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 780 confidence 0.005 feature_proportion 0.13955214481755981 n_clusters 2,780,0.005,0.13955214481755981,2,0.3191546068499759,0,None,i7186,721,0.00028842169127986784 1727595027,1727595675,648,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 421 confidence 0.25 feature_proportion 0.1583193166114436 n_clusters 1,421,0.25,0.1583193166114436,1,0.29935962373371927,0,None,i7186,640,0.00016221988558156808 1727594955,1727595707,752,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 664 confidence 0.1 feature_proportion 0.09554147683084008 n_clusters 4,664,0.1,0.09554147683084008,4,0.3113989387361312,0,None,i7186,745,0.00025398553461457145 1727595227,1727595903,676,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11663518405891063 n_clusters 3,100,0.25,0.11663518405891063,3,0.2647937771345875,0,None,i7186,669,4.275096258096253e-05 1727595287,1727595928,641,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 107 confidence 0.1 feature_proportion 0.000371037982404232 n_clusters 2,107,0.1,0.000371037982404232,2,0.2727315484804631,0,None,i7181,634,4.9353528311001554e-05 1727595267,1727596011,744,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 946 confidence 0.001 feature_proportion 0.15973705324199022 n_clusters 2,946,0.001,0.15973705324199022,2,0.32185962373371924,0,None,i7186,737,0.0003459110458904202 1727595297,1727596028,731,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 311 confidence 0.001 feature_proportion 0.07257689423859119 n_clusters 2,311,0.001,0.07257689423859119,2,0.29484201640135066,0,None,i7181,725,0.00013737987356061577 1727595447,1727596100,653,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 766 confidence 0.005 feature_proportion 0.06707582471566223 n_clusters 1,766,0.005,0.06707582471566223,1,0.3152520501688374,0,None,i7181,647,0.00029852803633834477 1727595418,1727596122,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06540846546432126 n_clusters 1,100,0.25,0.06540846546432126,1,0.26567776169802215,0,None,i7186,696,4.416771781819168e-05 1727595587,1727596268,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0646943433750969 n_clusters 1,100,0.25,0.0646943433750969,1,0.26567776169802215,0,None,i7186,674,4.416771781819168e-05 1727595627,1727596305,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11474630044486221 n_clusters 3,100,0.25,0.11474630044486221,3,0.2666111915098891,0,None,i7186,670,4.25618761364058e-05 1727595467,1727596318,851,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 confidence 0.01 feature_proportion 0.12282025832682848 n_clusters 4,135,0.01,0.12282025832682848,4,0.27722383019778096,0,None,i7181,845,6.581720085341665e-05 1727595599,1727596386,787,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 960 confidence 0.1 feature_proportion 0.07944094818085433 n_clusters 4,960,0.1,0.07944094818085433,4,0.3253955619874578,0,None,i7186,780,0.0003470509098795506 1727595719,1727596404,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 963 confidence 0.025 feature_proportion 0.19352945126593113 n_clusters 3,963,0.025,0.19352945126593113,3,0.3232308248914616,0,None,i7186,677,0.000344309175379039 1727595747,1727596422,675,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11378659903539029 n_clusters 3,100,0.25,0.11378659903539029,3,0.2655957549445248,0,None,i7186,667,4.268488480574127e-05 1727595939,1727596520,581,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06646056929747755 n_clusters 1,100,0.25,0.06646056929747755,1,0.26567776169802215,0,None,i7181,575,4.416771781819168e-05 1727595959,1727596529,570,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11448933010109714 n_clusters 3,100,0.25,0.11448933010109714,3,0.2665557163531115,0,None,i7181,565,4.2584072062870513e-05 1727595899,1727596583,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11399703423755353 n_clusters 3,100,0.25,0.11399703423755353,3,0.26372648335745297,0,None,i7186,677,4.2906128350104896e-05 1727595919,1727596599,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 379 confidence 0.05 feature_proportion 0.10522668175399304 n_clusters 2,379,0.05,0.10522668175399304,2,0.2993125904486251,0,None,i7186,672,0.0001477268838976791 1727595247,1727596643,1396,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.01 feature_proportion 0.0632737883097264 n_clusters 4,101,0.01,0.0632737883097264,4,0.2895574047274482,0,None,i7186,1388,9.196361643474399e-05 1727596080,1727596680,600,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06348622083157207 n_clusters 1,100,0.25,0.06348622083157207,1,0.26567776169802215,0,None,i7181,594,4.416771781819168e-05 1727596062,1727596751,689,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06340627056991353 n_clusters 1,100,0.25,0.06340627056991353,1,0.26567776169802215,0,None,i7186,681,4.416771781819168e-05 1727596222,1727596795,573,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11333159099986478 n_clusters 3,100,0.25,0.11333159099986478,3,0.26534491075735644,0,None,i7181,567,4.273080080690287e-05 1727596182,1727596853,671,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 211 confidence 0.25 feature_proportion 0.11154197794768916 n_clusters 3,211,0.25,0.11154197794768916,3,0.2836625663289918,0,None,i7186,664,8.519289314141875e-05 1727596302,1727596979,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11312322763392382 n_clusters 3,100,0.25,0.11312322763392382,3,0.26533646888567297,0,None,i7186,670,4.27162943828281e-05 1727596321,1727597003,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06355236470588478 n_clusters 1,100,0.25,0.06355236470588478,1,0.26567776169802215,0,None,i7186,675,4.416771781819168e-05 1727596441,1727597108,667,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 224 confidence 0.1 feature_proportion 0.1018042827027263 n_clusters 3,224,0.1,0.1018042827027263,3,0.28312590448625186,0,None,i7186,660,9.118561397866751e-05 1727596470,1727597149,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1154371692716249 n_clusters 3,100,0.25,0.1154371692716249,3,0.2673118668596237,0,None,i7186,672,4.2492439669933984e-05 1727596637,1727597324,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06393081880361899 n_clusters 1,100,0.25,0.06393081880361899,1,0.26567776169802215,0,None,i7186,679,4.416771781819168e-05 1727596617,1727597350,733,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.025 feature_proportion 0.068657192774117 n_clusters 2,181,0.025,0.068657192774117,2,0.281548480463097,0,None,i7186,726,8.036574260698088e-05 1727596957,1727597561,604,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 182 confidence 0.1 feature_proportion 0.13712626500323954 n_clusters 2,182,0.1,0.13712626500323954,2,0.2783068017366136,0,None,i7181,598,7.556233087415026e-05 1727596652,1727597577,925,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 359 confidence 0.005 feature_proportion 0.048345424234867096 n_clusters 3,359,0.005,0.048345424234867096,3,0.2983441871683551,0,None,i7186,918,0.00016183052751775386 1727596952,1727597580,628,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 161 confidence 0.1 feature_proportion 0.014815445886267745 n_clusters 2,161,0.1,0.014815445886267745,2,0.2803702363724071,0,None,i7181,622,6.867153023667857e-05 1727596937,1727597620,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0636638419488959 n_clusters 1,100,0.25,0.0636638419488959,1,0.26567776169802215,0,None,i7186,675,4.416771781819168e-05 1727596917,1727597643,726,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0759362204061384 n_clusters 4,100,0.25,0.0759362204061384,4,0.2660166425470333,0,None,i7186,718,4.4280678971928244e-05 1727597704,1727597707,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 165 confidence 0.05 feature_proportion 0 n_clusters 1,165,0.05,0,1,None,1,None,i7186 1727596893,1727597823,930,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 750 confidence 0.01 feature_proportion 0.07616323785273381 n_clusters 3,750,0.01,0.07616323785273381,3,0.31701398938736136,0,None,i7186,923,0.000302762062000561 1727597177,1727597885,708,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04148515063327851 n_clusters 3,100,0.25,0.04148515063327851,3,0.26672696575012056,0,None,i7186,700,4.4191307658001815e-05 1727597217,1727597897,680,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.114582156814305 n_clusters 3,100,0.25,0.114582156814305,3,0.26520863482875057,0,None,i7186,673,4.271625625904195e-05 1727597223,1727597907,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06316725712046667 n_clusters 1,100,0.25,0.06316725712046667,1,0.26567776169802215,0,None,i7186,677,4.416771781819168e-05 1727597237,1727597910,673,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11457527914487729 n_clusters 3,100,0.25,0.11457527914487729,3,0.2647105643994211,0,None,i7186,666,4.2776569946294246e-05 1727597523,1727598082,559,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 161 confidence 0.1 feature_proportion 0.01539891052691145 n_clusters 2,161,0.1,0.01539891052691145,2,0.2804148576941631,0,None,i7181,553,6.885855188122315e-05 1727597463,1727598142,679,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 622 confidence 0.025 feature_proportion 0.17044034176079606 n_clusters 2,622,0.025,0.17044034176079606,2,0.3082645923781958,0,None,i7186,671,0.00023533046097091426 1727597493,1727598191,698,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 360 confidence 0.05 feature_proportion 0.029526027726847932 n_clusters 2,360,0.05,0.029526027726847932,2,0.2981029908345393,0,None,i7186,691,0.00014479696160315966 1727597524,1727598226,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06311962912633813 n_clusters 1,100,0.25,0.06311962912633813,1,0.26567776169802215,0,None,i7181,695,4.416771781819168e-05 1727597665,1727598321,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 307 confidence 0.25 feature_proportion 0.05753529087256477 n_clusters 4,307,0.25,0.05753529087256477,4,0.29352508441871683,0,None,i7186,648,0.00012110279753534981 1727597929,1727598606,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 900 confidence 0.25 feature_proportion 0.05790701383623727 n_clusters 1,900,0.25,0.05790701383623727,1,0.3179691268692716,0,None,i7186,670,0.0003282170154777324 1727597969,1727598643,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06089187915630119 n_clusters 1,100,0.25,0.06089187915630119,1,0.26567776169802215,0,None,i7186,667,4.416771781819168e-05 1727597989,1727598674,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06106333062069857 n_clusters 1,100,0.25,0.06106333062069857,1,0.26567776169802215,0,None,i7186,678,4.416771781819168e-05 1727598004,1727598682,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06087166412911288 n_clusters 1,100,0.25,0.06087166412911288,1,0.26567776169802215,0,None,i7186,671,4.416771781819168e-05 1727598029,1727598712,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.061194731653367834 n_clusters 1,100,0.25,0.061194731653367834,1,0.26567776169802215,0,None,i7186,675,4.416771781819168e-05 1727598318,1727599000,682,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11135732252952156 n_clusters 3,100,0.25,0.11135732252952156,3,0.2641341051616015,0,None,i7186,674,4.2846376102484554e-05 1727599019,1727599023,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 124 confidence 0.25 feature_proportion 0 n_clusters 1,124,0.25,0,1,None,1,None,i7186 1727598358,1727599042,684,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 134 confidence 0.25 feature_proportion 0.021199076569781705 n_clusters 2,134,0.25,0.021199076569781705,2,0.27331041968162084,0,None,i7186,676,5.692914756100068e-05 1727598364,1727599051,687,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06090520687358645 n_clusters 1,100,0.25,0.06090520687358645,1,0.26567776169802215,0,None,i7186,680,4.416771781819168e-05 1727598394,1727599127,733,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 532 confidence 0.01 feature_proportion 0.18735480271266455 n_clusters 3,532,0.01,0.18735480271266455,3,0.3064773275446213,0,None,i7186,726,0.0002050573742404856 1727598418,1727599142,724,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04465595705735201 n_clusters 3,100,0.25,0.04465595705735201,3,0.2665098890496864,0,None,i7181,714,4.422974955527719e-05 1727598398,1727599171,773,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 932 confidence 0.1 feature_proportion 0.09221717731122281 n_clusters 3,932,0.1,0.09221717731122281,3,0.323941148094549,0,None,i7186,766,0.00033795210450732183 1727598664,1727599358,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11078507622696616 n_clusters 3,100,0.25,0.11078507622696616,3,0.26449951760733237,0,None,i7186,686,4.2812495325652445e-05 1727598690,1727599364,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11092592799574588 n_clusters 3,100,0.25,0.11092592799574588,3,0.26470091654606853,0,None,i7186,667,4.276738044429182e-05 1727598710,1727599401,691,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.058798372025838624 n_clusters 1,100,0.25,0.058798372025838624,1,0.26567776169802215,0,None,i7186,684,4.416771781819168e-05 1727598750,1727599433,683,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05870839459228903 n_clusters 1,100,0.25,0.05870839459228903,1,0.26567776169802215,0,None,i7186,676,4.416771781819168e-05 1727598730,1727599434,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04039677915544378 n_clusters 2,100,0.25,0.04039677915544378,2,0.2662759286058852,0,None,i7186,697,4.4054058675940594e-05 1727599017,1727599644,627,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1098561499473084 n_clusters 3,100,0.25,0.1098561499473084,3,0.26518089725036176,0,None,i7180,565,4.273514012908022e-05 1727599079,1727599739,660,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 313 confidence 0.25 feature_proportion 0.028041718799812966 n_clusters 3,313,0.25,0.028041718799812966,3,0.29045344910757354,0,None,i7186,653,0.00012476420186984587 1727599039,1727599743,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.037829374633336126 n_clusters 3,100,0.25,0.037829374633336126,3,0.26567655571635307,0,None,i7186,696,4.43234679541477e-05 1727599099,1727599789,690,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 114 confidence 0.25 feature_proportion 0.032206390015410834 n_clusters 3,114,0.25,0.032206390015410834,3,0.2690086830680174,0,None,i7186,683,4.965150767613853e-05 1727599295,1727599973,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05867940994017602 n_clusters 1,100,0.25,0.05867940994017602,1,0.26567776169802215,0,None,i7186,671,4.416771781819168e-05 1727599319,1727599996,677,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 170 confidence 0.1 feature_proportion 0.029042969085276128 n_clusters 2,170,0.1,0.029042969085276128,2,0.28021828268210325,0,None,i7186,669,7.253891673836248e-05 1727599354,1727600068,714,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03629390352361073 n_clusters 3,100,0.25,0.03629390352361073,3,0.26572238301977813,0,None,i7180,708,4.416766093995497e-05 1727599339,1727600069,730,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.058572691251139154 n_clusters 1,100,0.25,0.058572691251139154,1,0.26567776169802215,0,None,i7181,722,4.416771781819168e-05 1727599559,1727600252,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.15632493685561638 n_clusters 4,100,0.25,0.15632493685561638,4,0.2645055475156778,0,None,i7186,686,4.274445900353624e-05 1727599619,1727600269,650,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 208 confidence 0.25 feature_proportion 0.16188343781977893 n_clusters 4,208,0.25,0.16188343781977893,4,0.28254582730342503,0,None,i7186,642,8.425361761177944e-05 1727599595,1727600333,738,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 381 confidence 0.05 feature_proportion 0.044967321678996086 n_clusters 3,381,0.05,0.044967321678996086,3,0.29746623251326576,0,None,i7186,731,0.00015520254067925095 1727599639,1727600369,730,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 355 confidence 0.025 feature_proportion 0.14599110539136753 n_clusters 4,355,0.025,0.14599110539136753,4,0.2956536420646406,0,None,i7186,723,0.00014198495019880105 1727599847,1727600519,672,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10557857575087436 n_clusters 3,100,0.25,0.10557857575087436,3,0.26468403280270136,0,None,i7186,665,4.2779782781934803e-05 1727599899,1727600551,652,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 234 confidence 0.25 feature_proportion 0.08812699790705236 n_clusters 2,234,0.25,0.08812699790705236,2,0.2858514230583695,0,None,i7186,645,9.453687900869612e-05 1727599895,1727600569,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10537827537460616 n_clusters 3,100,0.25,0.10537827537460616,3,0.26591172214182346,0,None,i7186,666,4.2620793810423185e-05 1727599919,1727600573,654,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 243 confidence 0.25 feature_proportion 0.11053404376319426 n_clusters 2,243,0.25,0.11053404376319426,2,0.2880885190545104,0,None,i7186,646,9.699234341705633e-05 1727600117,1727600791,674,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10679223701656906 n_clusters 3,100,0.25,0.10679223701656906,3,0.2661227689339122,0,None,i7186,667,4.261588058163842e-05 1727600177,1727600876,699,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 231 confidence 0.25 feature_proportion 0.07612451633078764 n_clusters 1,231,0.25,0.07612451633078764,1,0.28495658465991314,0,None,i7181,691,9.377721609574932e-05 1727600195,1727600899,704,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.057947177819312745 n_clusters 1,100,0.25,0.057947177819312745,1,0.26567776169802215,0,None,i7180,697,4.416771781819168e-05 1727600157,1727600925,768,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 919 confidence 0.001 feature_proportion 0.08847251067189033 n_clusters 1,919,0.001,0.08847251067189033,1,0.3221369995176073,0,None,i7186,760,0.00034884726355932975 1727600436,1727601074,638,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 189 confidence 0.25 feature_proportion 0.10888066272007074 n_clusters 4,189,0.25,0.10888066272007074,4,0.28063555233960447,0,None,i7186,631,7.699701590420327e-05 1727600401,1727601094,693,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 673 confidence 0.01 feature_proportion 0.1235156553743453 n_clusters 1,673,0.01,0.1235156553743453,1,0.31123251326579837,0,None,i7186,685,0.0002530750542492744 1727600481,1727601129,648,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 648 confidence 0.25 feature_proportion 0.18814819529652596 n_clusters 1,648,0.25,0.18814819529652596,1,0.31242522913651716,0,None,i7186,641,0.00023925939692983697 1727600461,1727601139,678,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.057787828021531165 n_clusters 1,100,0.25,0.057787828021531165,1,0.26567776169802215,0,None,i7186,670,4.416771781819168e-05 1727600717,1727601402,685,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 164 confidence 0.1 feature_proportion 0.02807947006267828 n_clusters 2,164,0.1,0.02807947006267828,2,0.2785841775205017,0,None,i7186,678,7.038061001564026e-05 1727600757,1727601465,708,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03376415856467752 n_clusters 3,100,0.25,0.03376415856467752,3,0.2672768933912204,0,None,i7186,700,4.410546920194926e-05 1727600777,1727601473,696,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03439425172627791 n_clusters 3,100,0.25,0.03439425172627791,3,0.2644319826338639,0,None,i7186,689,4.4390695176049105e-05 1727600796,1727601527,731,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.1 feature_proportion 0.10555721401048009 n_clusters 1,101,0.1,0.10555721401048009,1,0.2675976845151954,0,None,i7180,724,4.440017672040598e-05 1727601073,1727601743,670,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10877168409788776 n_clusters 3,100,0.25,0.10877168409788776,3,0.2655318379160637,0,None,i7186,663,4.2697799987867123e-05 1727601113,1727601807,694,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05675743729970295 n_clusters 1,100,0.25,0.05675743729970295,1,0.26567776169802215,0,None,i7186,686,4.416771781819168e-05 1727601133,1727601835,702,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03364897887454467 n_clusters 3,100,0.25,0.03364897887454467,3,0.2676109503135552,0,None,i7186,695,4.398048314114534e-05 1727601156,1727601867,711,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 123 confidence 0.025 feature_proportion 0.12441290992584848 n_clusters 1,123,0.025,0.12441290992584848,1,0.275092860588519,0,None,i7186,704,5.492016953826685e-05 1727601186,1727601963,777,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 117 confidence 0.1 feature_proportion 0.06947060728648527 n_clusters 3,117,0.1,0.06947060728648527,3,0.27472262421611193,0,None,i7186,770,5.44474706217968e-05 1727601576,1727602239,663,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10847998422854391 n_clusters 3,100,0.25,0.10847998422854391,3,0.26465750120598164,0,None,i7186,656,4.2824482158780284e-05 1727601546,1727602247,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.03448932067150464 n_clusters 3,100,0.25,0.03448932067150464,3,0.26682706222865415,0,None,i7186,694,4.4290163570542986e-05 1727601591,1727602272,681,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.10868245795369175 n_clusters 3,100,0.25,0.10868245795369175,3,0.2645972021225277,0,None,i7186,674,4.2805848200289676e-05 1727601631,1727602287,656,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 814 confidence 0.1 feature_proportion 0.11751237940043212 n_clusters 2,814,0.1,0.11751237940043212,2,0.3186131210805596,0,None,i7186,648,0.0002952133707449303 1727601611,1727602312,701,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 296 confidence 0.025 feature_proportion 0.056134262867271904 n_clusters 2,296,0.025,0.056134262867271904,2,0.2910721177038109,0,None,i7186,694,0.00012284380357463663 1727601666,1727602385,719,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 confidence 0.25 feature_proportion 0.07802188923557073 n_clusters 3,118,0.25,0.07802188923557073,3,0.2704401833092137,0,None,i7180,712,5.103822818427159e-05 1727601665,1727602402,737,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0572687222671086 n_clusters 1,100,0.25,0.0572687222671086,1,0.26567776169802215,0,None,i7181,726,4.416771781819168e-05
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2289994/2289994_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2289995/2289995_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290000/2290000_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290009/2290009_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290010/2290010_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290011/2290011_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290012/2290012_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290013/2290013_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290015/2290015_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290088/2290088_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290099/2290099_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(230, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290101/2290101_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 229 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(229, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290102/2290102_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 227 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(227, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290124/2290124_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.25 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(151, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290168/2290168_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290174/2290174_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290178/2290178_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290188/2290188_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 569 confidence 0.001 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(569, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290190/2290190_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290195/2290195_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(250, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290197/2290197_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290199/2290199_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 106 confidence 0.005 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(106, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290204/2290204_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(250, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290207/2290207_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 260 confidence 0.05 feature_proportion 0 n_clusters 2
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(260, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290209/2290209_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 212 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(212, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290212/2290212_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 249 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(249, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290214/2290214_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 268 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(268, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290217/2290217_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 261 confidence 0.01 feature_proportion 0 n_clusters 2
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(261, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290218/2290218_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 255 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(255, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290220/2290220_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 255 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(255, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290223/2290223_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(258, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290225/2290225_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290226/2290226_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(258, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290249/2290249_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 545 confidence 0.001 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(545, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290252/2290252_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 562 confidence 0.001 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(562, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290413/2290413_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 165 confidence 0.05 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(165, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290590/2290590_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 124 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(124, 0)) while a minimum of 1
timestamp,ram_usage_mb,cpu_usage_percent 1727577855,474.1484375,49.7 1727577855,474.1875,50.8 1727577855,474.23828125,49.8 1727577855,474.23828125,56.5 1727577855,474.23828125,39.4 1727577855,474.23828125,50.1 1727577855,474.23828125,39.4 1727577901,482.65234375,49.8 1727577901,482.65234375,36.4 1727577901,482.65234375,49.9 1727577901,482.65234375,40.6 1727577902,482.65234375,49.8 1727577902,482.65234375,55.3 1727577902,482.65234375,47.7 1727577902,482.65234375,57.8 1727577904,482.65234375,49.7 1727577904,482.65234375,51.0 1727577904,482.65234375,52.1 1727577904,482.65234375,44.1 1727577905,482.65234375,49.8 1727577905,482.65234375,39.4 1727577905,482.65234375,49.0 1727577905,482.65234375,57.8 1727577906,482.65234375,49.7 1727577906,482.65234375,47.6 1727577906,482.65234375,52.4 1727577906,482.65234375,38.7 1727577908,482.65234375,49.8 1727577908,482.65234375,37.1 1727577908,482.65234375,51.4 1727577908,482.65234375,51.2 1727577909,482.65234375,49.8 1727577909,482.65234375,37.1 1727577909,482.65234375,53.4 1727577909,482.65234375,37.5 1727577911,482.65234375,49.8 1727577911,482.65234375,54.3 1727577911,482.65234375,47.6 1727577911,482.65234375,59.6 1727577912,482.65234375,49.7 1727577912,482.65234375,52.4 1727577912,482.65234375,48.1 1727577912,482.65234375,55.6 1727577913,482.65234375,49.7 1727577913,482.65234375,54.3 1727577913,482.65234375,50.0 1727577913,482.65234375,56.5 1727577915,482.65234375,49.7 1727577915,482.65234375,55.6 1727577915,482.65234375,48.6 1727577915,482.65234375,57.8 1727577916,482.65234375,49.7 1727577916,482.65234375,54.3 1727577916,482.65234375,49.1 1727577916,482.65234375,58.1 1727577918,482.65234375,49.7 1727577918,482.65234375,54.3 1727577918,482.65234375,47.7 1727577918,482.65234375,40.0 1727577919,482.65234375,49.8 1727577919,482.65234375,47.5 1727577919,482.65234375,52.2 1727577919,482.65234375,38.7 1727577921,482.65234375,49.7 1727577921,482.65234375,55.3 1727577921,482.65234375,49.5 1727577921,482.65234375,55.6 1727577922,482.65234375,49.7 1727577922,482.65234375,53.1 1727577922,482.65234375,46.8 1727577922,482.65234375,57.8 1727577923,482.65234375,49.7 1727577923,482.65234375,54.2 1727577923,482.65234375,49.0 1727577923,482.65234375,56.5 1727577925,482.65234375,49.7 1727577925,482.65234375,53.2 1727577925,482.65234375,50.8 1727577925,482.65234375,42.4 1727577926,482.65234375,49.8 1727577926,482.65234375,55.3 1727577926,482.65234375,49.0 1727577926,482.65234375,39.4 1727577928,482.65234375,49.8 1727577928,482.65234375,36.4 1727577928,482.65234375,52.0 1727577928,482.65234375,56.8 1727577929,482.65234375,49.8 1727577929,482.65234375,53.2 1727577929,482.65234375,52.9 1727577929,482.65234375,38.2 1727577930,482.65234375,49.8 1727577930,482.65234375,54.3 1727577930,482.65234375,52.1 1727577930,482.65234375,42.4 1727577932,482.65234375,49.8 1727577932,482.65234375,55.3 1727577932,482.65234375,48.1 1727577932,482.65234375,57.4 1727577934,482.65234375,49.7 1727577934,482.65234375,55.3 1727577934,482.65234375,52.0 1727577934,482.65234375,38.7 1727577935,482.65234375,49.7 1727577935,482.65234375,54.2 1727577935,482.65234375,47.3 1727577935,482.65234375,53.2 1727577936,482.65234375,49.7 1727577936,482.65234375,53.2 1727577936,482.65234375,52.5 1727577936,482.65234375,40.6 1727577938,482.65234375,49.8 1727577938,482.65234375,38.2 1727577938,482.65234375,49.0 1727577938,482.65234375,57.8 1727577939,482.65234375,49.7 1727577939,482.65234375,38.2 1727577939,482.65234375,54.3 1727577939,482.65234375,37.1 1727577941,482.65234375,49.8 1727577941,482.65234375,35.3 1727577941,482.65234375,49.5 1727577941,482.65234375,56.8 1727577942,482.65234375,49.7 1727577942,482.65234375,55.3 1727577942,482.65234375,51.2 1727577942,482.65234375,40.6 1727577943,482.65234375,49.8 1727577943,482.65234375,54.3 1727577943,482.65234375,49.5 1727577943,482.65234375,55.6 1727577945,482.65234375,49.7 1727577945,482.65234375,53.2 1727577945,482.65234375,47.6 1727577945,482.65234375,56.5 1727577946,482.65234375,49.7 1727577946,482.65234375,44.7 1727577946,482.65234375,50.9 1727577946,482.65234375,51.3 1727577948,482.65234375,49.7 1727577948,482.65234375,56.5 1727577948,482.65234375,48.1 1727577948,482.65234375,55.6 1727577949,482.65234375,49.8 1727577949,482.65234375,36.4 1727577949,482.65234375,52.8 1727577949,482.65234375,46.2 1727577950,482.65234375,49.8 1727577950,482.65234375,37.1 1727577950,482.65234375,53.3 1727577950,482.65234375,44.7 1727577952,482.65234375,49.7 1727577952,482.65234375,53.2 1727577952,482.65234375,48.6 1727577952,482.65234375,55.6 1727577953,482.65234375,49.7 1727577953,482.65234375,55.3 1727577953,482.65234375,49.6 1727577953,482.65234375,51.3 1727577955,482.65234375,49.8 1727577955,482.65234375,51.0 1727577955,482.65234375,49.1 1727577955,482.65234375,54.5 1727577956,482.65234375,49.7 1727577956,482.65234375,52.1 1727577956,482.65234375,49.5 1727577956,482.65234375,53.7 1727577957,482.65234375,49.7 1727577957,482.65234375,54.3 1727577957,482.65234375,48.6 1727577957,482.65234375,56.5 1727577959,482.65234375,49.7 1727577959,482.65234375,54.3 1727577959,482.65234375,49.5 1727577959,482.65234375,40.6 1727577960,482.65234375,49.8 1727577960,482.65234375,56.3 1727577960,482.65234375,48.1 1727577960,482.65234375,56.8 1727577962,482.65234375,49.8 1727577962,482.65234375,56.2 1727577962,482.65234375,46.7 1727577962,482.65234375,54.2 1727577963,482.65234375,49.7 1727577963,482.65234375,53.2 1727577963,482.65234375,47.6 1727577963,482.65234375,57.8 1727577965,482.65234375,49.8 1727577965,482.65234375,56.5 1727577965,482.65234375,46.3 1727577965,482.65234375,56.5 1727577966,482.65234375,49.7 1727577966,482.65234375,54.2 1727577966,482.65234375,48.6 1727577966,482.65234375,55.6 1727577967,482.65234375,49.7 1727577967,482.65234375,53.2 1727577967,482.65234375,52.5 1727577967,482.65234375,39.4 1727577969,482.65234375,49.8 1727577969,482.65234375,37.1 1727577969,482.65234375,52.9 1727577969,482.65234375,45.7 1727577970,482.65234375,49.7 1727577970,482.65234375,54.2 1727577970,482.65234375,48.1 1727577970,482.65234375,55.6 1727577972,482.65234375,49.7 1727577972,482.65234375,40.0 1727577972,482.65234375,50.8 1727577972,482.65234375,54.3 1727577973,482.65234375,49.7 1727577973,482.65234375,41.7 1727577973,482.65234375,52.9 1727577973,482.65234375,39.4 1727577975,482.65234375,49.8 1727577975,482.65234375,36.4 1727577975,482.65234375,52.9 1727577975,482.65234375,40.0 1727577976,482.65234375,49.7 1727577976,482.65234375,50.0 1727577976,482.65234375,48.6 1727577976,482.65234375,54.3 1727577977,482.65234375,49.7 1727577977,482.65234375,54.2 1727577977,482.65234375,46.7 1727577977,482.65234375,57.8 1727577979,482.65234375,49.7 1727577979,482.65234375,47.5 1727577979,482.65234375,50.5 1727577979,482.65234375,51.2 1727577980,482.65234375,49.8 1727577980,482.65234375,54.3 1727577980,482.65234375,49.7 1727577980,482.65234375,36.4 1727577982,482.65234375,49.7 1727577982,482.65234375,47.5 1727577982,482.65234375,51.0 1727577982,482.65234375,55.6 1727577983,482.65234375,49.7 1727577983,482.65234375,52.1 1727577983,482.65234375,50.4 1727577983,482.65234375,57.4 1727577984,482.65234375,49.8 1727577984,482.65234375,38.2 1727577984,482.65234375,53.3 1727577984,482.65234375,37.5 1727577986,482.66015625,49.8 1727577986,482.66015625,38.2 1727577986,482.66015625,50.0 1727577986,482.66015625,56.5 1727577987,482.66015625,49.7 1727577987,482.66015625,50.0 1727577987,482.66015625,51.9 1727577987,482.66015625,37.5 1727577989,482.66015625,49.8 1727577989,482.66015625,48.8 1727577989,482.66015625,51.4 1727577989,482.66015625,39.4 1727577990,482.66015625,49.8 1727577990,482.66015625,54.9 1727577990,482.66015625,47.6 1727577990,482.66015625,55.6 1727577991,482.66015625,49.8 1727577991,482.66015625,54.3 1727577991,482.66015625,49.0 1727577991,482.66015625,53.5 1727577993,482.66015625,49.7 1727577993,482.66015625,54.3 1727577993,482.66015625,47.7 1727577993,482.66015625,52.5 1727577994,482.6640625,49.8 1727577994,482.6640625,38.2 1727577994,482.6640625,54.2 1727577994,482.6640625,38.7 1727577996,482.6640625,49.8 1727577996,482.6640625,38.2 1727577996,482.6640625,55.1 1727577996,482.6640625,38.7 1727577997,482.6640625,49.8 1727577997,482.6640625,54.2 1727577997,482.6640625,47.2 1727577997,482.6640625,59.1 1727577998,482.6640625,49.7 1727577998,482.6640625,55.6 1727577998,482.6640625,48.5 1727577998,482.6640625,45.9 1727578000,482.6640625,49.7 1727578000,482.6640625,55.6 1727578000,482.6640625,48.1 1727578000,482.6640625,56.8 1727578001,482.6640625,49.7 1727578001,482.6640625,53.2 1727578001,482.6640625,49.5 1727578001,482.6640625,55.6 1727578003,482.6640625,49.7 1727578003,482.6640625,55.6 1727578003,482.6640625,48.6 1727578003,482.6640625,58.7 1727578004,482.6640625,49.7 1727578004,482.6640625,38.2 1727578004,482.6640625,53.3 1727578004,482.6640625,39.4 1727578005,482.6640625,49.8 1727578005,482.6640625,53.2 1727578005,482.6640625,49.5 1727578005,482.6640625,37.5 1727578007,482.6640625,49.7 1727578007,482.6640625,56.3 1727578007,482.6640625,48.1 1727578007,482.6640625,54.3 1727578008,482.6640625,49.7 1727578008,482.6640625,38.2 1727578008,482.6640625,51.4 1727578008,482.6640625,51.3 1727578010,482.6640625,49.8 1727578010,482.6640625,37.1 1727578010,482.6640625,54.2 1727578010,482.6640625,39.4 1727578011,482.6640625,49.7 1727578011,482.6640625,54.2 1727578011,482.6640625,47.6 1727578011,482.6640625,55.3 1727578012,482.6640625,49.7 1727578012,482.6640625,36.4 1727578012,482.6640625,50.5 1727578012,482.6640625,56.8 1727578014,482.6640625,49.7 1727578014,482.6640625,57.4 1727578014,482.6640625,52.5 1727578014,482.6640625,33.3 1727578015,482.6640625,49.8 1727578015,482.6640625,48.8 1727578015,482.6640625,50.9 1727578015,482.6640625,39.4 1727578017,482.6640625,49.8 1727578017,482.6640625,55.6 1727578017,482.6640625,49.0 1727578017,482.6640625,37.5 1727578018,482.6640625,49.8 1727578018,482.6640625,54.3 1727578018,482.6640625,44.6 1727578018,482.6640625,58.7 1727578019,482.6640625,49.7 1727578019,482.6640625,38.2 1727578020,482.6640625,51.2 1727578020,482.6640625,54.2 1727578021,482.6640625,49.8 1727578021,482.6640625,36.4 1727578021,482.6640625,53.3 1727578021,482.6640625,40.6 1727578022,482.6640625,49.8 1727578022,482.6640625,38.2 1727578022,482.6640625,50.0 1727578022,482.6640625,56.5 1727578024,482.6640625,49.7 1727578024,482.6640625,54.3 1727578024,482.6640625,48.6 1727578024,482.6640625,56.8 1727578025,482.6640625,49.7 1727578025,482.6640625,55.3 1727578025,482.6640625,47.7 1727578025,482.6640625,56.8 1727578027,482.6640625,49.8 1727578027,482.6640625,54.2 1727578027,482.6640625,47.2 1727578027,482.6640625,56.5 1727578028,482.6640625,49.7 1727578028,482.6640625,36.4 1727578028,482.6640625,52.5 1727578028,482.6640625,50.0 1727578029,482.6640625,49.7 1727578029,482.6640625,55.1 1727578029,482.6640625,47.2 1727578029,482.6640625,56.8 1727578031,482.6640625,49.7 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This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
submitit INFO (2024-09-29 04:44:26,202) - Starting with JobEnvironment(job_id=2289970, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-29 04:44:26,203) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2289970/2289970_submitted.pkl
Parameters: {"n_samples": 392, "confidence": 0.25, "feature_proportion": 0.1259731888771057, "n_clusters": 1}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 392 confidence 0.25 feature_proportion 0.1259731888771057 n_clusters 1
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: 1727577772.0
Last modification: 1724759185.0
Hostname: i7186
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: main_omniopt.py
UID: 2063769
GID: 200270
Status-Change-Time: 1727277242.0
Size: 1729 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727577772.0
Last modification: 1727277242.0
Hostname: i7186
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 392 confidence 0.25 feature_proportion 0.1259731888771057 n_clusters 1
stdout:
RESULT: 0.2993644476603956
OO-Info: runtime: 658
OO-Info: lpd: 0.00015099290585569828
Result: 0.2993644476603956
EXIT_CODE: 0
submitit INFO (2024-09-29 04:55:33,829) - Job completed successfully
submitit INFO (2024-09-29 04:55:33,831) - Exiting after successful completion
Job-Infos
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select {
border: 1px solid #7f9db9;
background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 -0.5 15 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e6eefc' d='M0 0h1'/%3E%3Cpath stroke='%23d1e0fd' d='M1 0h1M0 1h1m3 0h2M2 3h1M2 4h1'/%3E%3Cpath stroke='%23cad8f9' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23c4d3f7' d='M3 0h1M0 3h1M0 4h1'/%3E%3Cpath stroke='%23bfd0f8' d='M4 0h2M0 5h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 0h1M0 6h1'/%3E%3Cpath stroke='%23baccf4' d='M7 0h1m6 2h1m-1 5h1m-1 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M8 0h1M0 7h1M0 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M9 0h2M0 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 0h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 0h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 0h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 0h1'/%3E%3Cpath stroke='%23e1eafe' d='M1 1h1'/%3E%3Cpath stroke='%23dae6fe' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23d4e1fc' d='M3 1h1M1 3h1M1 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M6 1h1M1 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M7 1h1M4 2h2'/%3E%3Cpath stroke='%23cad9fd' d='M8 1h1M6 2h1M3 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M9 1h2'/%3E%3Cpath stroke='%23c5d6fc' d='M11 1h1M2 11h4'/%3E%3Cpath stroke='%23c2d3fc' d='M12 1h1m-2 1h1M1 11h1m0 1h2m-2 1h2'/%3E%3Cpath stroke='%23bccefa' d='M13 1h1m-1 1h1m-1 1h1m-1 1h1M3 15h4'/%3E%3Cpath stroke='%23b9c9f3' d='M14 1h1M3 16h4'/%3E%3Cpath stroke='%23d8e3fc' d='M2 2h1'/%3E%3Cpath stroke='%23d1defd' d='M3 2h1'/%3E%3Cpath stroke='%23c9d8fc' d='M7 2h1M4 3h3M4 4h3M3 6h1m1 0h2M1 7h1M1 8h1'/%3E%3Cpath stroke='%23c5d5fc' d='M8 2h1m-8 8h5'/%3E%3Cpath stroke='%23c5d3fc' d='M9 2h2'/%3E%3Cpath stroke='%23bed0fc' d='M12 2h1M8 3h1M8 4h1m-8 8h1m-1 1h1m0 1h1m1 0h3'/%3E%3Cpath stroke='%23cddbfc' d='M3 3h1M3 4h1M1 6h2'/%3E%3Cpath stroke='%23c8d5fb' d='M7 3h1M7 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M9 3h4M9 4h4M8 5h1M7 6h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 3h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23ceddfd' d='M2 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M4 5h4M1 9h3'/%3E%3Cpath stroke='%23bacdfc' d='M9 5h2m1 0h2M1 14h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1M8 6h2m2 0h2m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%234d6185' d='M4 6h1m5 0h1M3 7h3m3 0h3M4 8h3m1 0h3M5 9h5m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1m-1 1h1'/%3E%3Cpath stroke='%23cad8fd' d='M2 7h1M2 8h2'/%3E%3Cpath stroke='%23c1d3fb' d='M6 7h2M7 8h1M4 9h1'/%3E%3Cpath stroke='%23b6cefb' d='M8 7h1m2 1h1m-2 1h3m-2 1h2'/%3E%3Cpath stroke='%23b6cdfb' d='M13 9h1m-6 6h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 9h1'/%3E%3Cpath stroke='%23b4c8f6' d='M0 10h1'/%3E%3Cpath stroke='%23bdd3fb' d='M9 10h2m-4 4h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 10h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 10h1'/%3E%3Cpath stroke='%23b1c7f6' d='M0 11h1'/%3E%3Cpath stroke='%23c3d5fd' d='M6 11h1'/%3E%3Cpath stroke='%23bad4fc' d='M8 11h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M9 11h4m-2 3h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 11h1m-3 4h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 11h1m-7 5h3'/%3E%3Cpath stroke='%23adc3f6' d='M0 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c2d5fc' d='M4 12h4m-4 1h4'/%3E%3Cpath stroke='%23b7d3fc' d='M9 12h2m-2 1h2m-3 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 12h1m-1 1h1'/%3E%3Cpath stroke='%23afcdfb' d='M12 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afcbfa' d='M13 12h1m-1 1h1'/%3E%3Cpath stroke='%23b2c8f4' d='M14 12h1m-1 1h1m-4 3h1'/%3E%3Cpath stroke='%23c1d2fb' d='M3 14h1'/%3E%3Cpath stroke='%23b6d1fb' d='M9 14h2'/%3E%3Cpath stroke='%23adc9f9' d='M13 14h1m-2 1h1'/%3E%3Cpath stroke='%23b1c6f3' d='M14 14h1m-3 2h1'/%3E%3Cpath stroke='%23abc1f4' d='M0 15h1'/%3E%3Cpath stroke='%23b7cbf9' d='M1 15h1'/%3E%3Cpath stroke='%23b9cefb' d='M2 15h1'/%3E%3Cpath stroke='%23b9cffb' d='M7 15h1'/%3E%3Cpath stroke='%23b2cdfb' d='M9 15h2'/%3E%3Cpath stroke='%23aec8f7' d='M13 15h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 15h1m-2 1h1'/%3E%3Cpath stroke='%23dbe3f8' d='M0 16h1'/%3E%3Cpath stroke='%23b7c6f1' d='M1 16h1'/%3E%3Cpath stroke='%23b8c9f2' d='M2 16h1m4 0h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 16h1'/%3E%3C/svg%3E");
background-size: 15px;
font-size: 11px;
border: none;
background-color: #fff;
box-sizing: border-box;
height: 21px;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
position: relative;
padding: 5px 32px 32px 5px;
background-position: top 50% right 2px;
background-repeat: no-repeat;
border-radius: 0;
border: 1px solid black;
}
body {
font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
background-color: #fafafa;
font-variant: oldstyle-nums;
text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
scroll-behavior: smooth;
text-wrap: balance;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-feature-settings: "ss02", "liga", "onum";
}
.marked_text {
background-color: yellow;
}
.time_picker_container {
font-variant: small-caps;
width: 100%;
}
.time_picker_container > input {
width: 50px;
}
#loader {
display: grid;
justify-content: center;
align-items: center;
height: 100%;
}
.no_linebreak {
line-break: auto;
}
.dark_code_bg {
background-color: #363636;
color: white;
}
.code_bg {
background-color: #C0C0C0;
}
#commands {
line-break: anywhere;
}
.color_red {
color: red;
}
.color_orange {
color: orange;
}
table > tbody > tr:nth-child(odd) {
background-color: #fafafa;
}
table > tbody > tr:nth-child(even) {
background-color: #ddd;
}
table {
border-collapse: collapse;
margin: 25px 0;
min-width: 200px;
}
th {
background-color: #4eae46;
color: #ffffff;
text-align: left;
border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
min-height: 80px;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 60px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 7h1'/%3E%3Cpath stroke='%23c5d5fc' d='M9 3h1M3 9h1m3 0h1'/%3E%3Cpath stroke='%23c5d3fc' d='M10 3h1'/%3E%3Cpath stroke='%23bed0fc' d='M12 3h1M9 4h1m-7 7h1m0 1h1'/%3E%3Cpath stroke='%23bccdfa' d='M13 3h1'/%3E%3Cpath stroke='%23baccf4' d='M14 3h1'/%3E%3Cpath stroke='%23bdcbda' d='M16 3h1'/%3E%3Cpath stroke='%23c4d4f7' d='M2 4h1'/%3E%3Cpath stroke='%23cddbfc' d='M5 4h1M3 6h1'/%3E%3Cpath stroke='%23c8d5fb' d='M8 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M10 4h3M9 5h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 4h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c2d5' d='M16 4h1'/%3E%3Cpath stroke='%23bed0f8' d='M2 5h1'/%3E%3Cpath stroke='%23ceddfd' d='M4 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M6 5h2M3 8h2'/%3E%3Cpath stroke='%234d6185' d='M8 5h1M7 6h3M6 7h5M5 8h3m1 0h3M4 9h3m3 0h3m-8 1h1m5 0h1'/%3E%3Cpath stroke='%23bacdfc' d='M10 5h1m1 0h2M3 12h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1m-2 1h1m1 0h2m-1 1h1'/%3E%3Cpath stroke='%23a8bbd4' d='M16 5h1'/%3E%3Cpath stroke='%23cddafc' d='M4 6h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1'/%3E%3Cpath stroke='%23a4b8d3' d='M16 6h1'/%3E%3Cpath stroke='%23cad8fd' d='M4 7h2'/%3E%3Cpath stroke='%23b6cefb' d='M11 7h1m0 1h1'/%3E%3Cpath stroke='%23bacbf4' d='M14 7h1'/%3E%3Cpath stroke='%23a0b5d3' d='M16 7h1m-1 1h1m-1 5h1'/%3E%3Cpath stroke='%23c1d3fb' d='M8 8h1'/%3E%3Cpath stroke='%23b6cdfb' d='M13 8h1m-5 5h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 8h1'/%3E%3Cpath stroke='%23b4c8f6' d='M2 9h1'/%3E%3Cpath stroke='%23c2d5fc' d='M8 9h1m-1 1h1m-3 1h2'/%3E%3Cpath stroke='%23bdd3fb' d='M9 9h1m-2 3h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 9h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 9h1'/%3E%3Cpath stroke='%239fb5d2' d='M16 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b1c7f6' d='M2 10h1'/%3E%3Cpath stroke='%23c3d5fd' d='M7 10h1'/%3E%3Cpath stroke='%23bad4fc' d='M9 10h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M10 10h1m1 0h1m-2 2h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 10h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 10h1m-6 4h2'/%3E%3Cpath stroke='%23adc3f6' d='M2 11h1'/%3E%3Cpath stroke='%23c3d3fd' d='M5 11h1'/%3E%3Cpath stroke='%23c1d5fb' d='M8 11h1'/%3E%3Cpath stroke='%23b7d3fc' d='M10 11h1m-2 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 11h1'/%3E%3Cpath stroke='%23afcefb' d='M12 11h1'/%3E%3Cpath stroke='%23aecafa' d='M13 11h1'/%3E%3Cpath stroke='%23b1c8f3' d='M14 11h1'/%3E%3Cpath stroke='%23acc2f5' d='M2 12h1'/%3E%3Cpath stroke='%23c1d2fb' d='M5 12h1'/%3E%3Cpath stroke='%23bed1fc' d='M6 12h2'/%3E%3Cpath stroke='%23b6d1fb' d='M10 12h1'/%3E%3Cpath stroke='%23afccfb' d='M12 12h1'/%3E%3Cpath stroke='%23adc9f9' d='M13 12h1m-2 1h1'/%3E%3Cpath stroke='%23b1c5f3' d='M14 12h1'/%3E%3Cpath stroke='%23aac0f3' d='M2 13h1'/%3E%3Cpath stroke='%23b7cbf9' d='M3 13h1'/%3E%3Cpath stroke='%23b9cefb' d='M4 13h1'/%3E%3Cpath stroke='%23bbcef9' d='M7 13h1'/%3E%3Cpath stroke='%23b9cffb' d='M8 13h1'/%3E%3Cpath stroke='%23b2cdfb' d='M10 13h1'/%3E%3Cpath stroke='%23b0cbf9' d='M11 13h1'/%3E%3Cpath stroke='%23aec8f7' d='M13 13h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 13h1'/%3E%3Cpath stroke='%23dbe3f8' d='M2 14h1'/%3E%3Cpath stroke='%23b7c6f1' d='M3 14h1'/%3E%3Cpath stroke='%23b8c9f2' d='M4 14h1m3 0h1'/%3E%3Cpath stroke='%23b2c8f4' d='M11 14h1'/%3E%3Cpath stroke='%23b1c6f3' d='M12 14h1'/%3E%3Cpath stroke='%23b0c4f2' d='M13 14h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 14h1'/%3E%3Cpath stroke='%23aec0d6' d='M16 14h1'/%3E%3Cpath stroke='%23c3d4e7' d='M1 15h1'/%3E%3Cpath stroke='%23aec4e5' d='M15 15h1'/%3E%3Cpath stroke='%23edf1f3' d='M1 16h1'/%3E%3Cpath stroke='%23aac0e1' d='M2 16h1'/%3E%3Cpath stroke='%2394b1d9' d='M3 16h1'/%3E%3Cpath stroke='%2388a7d8' d='M4 16h1'/%3E%3Cpath stroke='%2383a4d3' d='M5 16h1'/%3E%3Cpath stroke='%237da0d4' d='M6 16h1m3 0h3'/%3E%3Cpath stroke='%237e9fd2' d='M7 16h1'/%3E%3Cpath stroke='%237c9fd3' d='M8 16h2'/%3E%3Cpath stroke='%2382a4d6' d='M13 16h1'/%3E%3Cpath stroke='%2394b0dd' d='M14 16h1'/%3E%3Cpath stroke='%23ecf2f7' d='M15 16h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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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],
[
1727592428,
447.06640625,
50
],
[
1727592428,
447.06640625,
56.5
],
[
1727592428,
447.06640625,
49
],
[
1727592428,
447.06640625,
56.5
],
[
1727592542,
452.515625,
50.2
],
[
1727592542,
452.515625,
37.1
],
[
1727592542,
452.515625,
51
],
[
1727592543,
452.515625,
38.7
],
[
1727592825,
453.2109375,
50.2
],
[
1727592825,
453.2109375,
56.5
],
[
1727592825,
453.2109375,
49.6
],
[
1727592825,
453.2109375,
53.5
],
[
1727593068,
456.5625,
50.2
],
[
1727593068,
456.5625,
54.3
],
[
1727593068,
456.5625,
49.8
],
[
1727593068,
456.5625,
37.5
],
[
1727593263,
460.00390625,
50.2
],
[
1727593263,
460.00390625,
55.3
],
[
1727593263,
460.00390625,
50.5
],
[
1727593263,
460.00390625,
44.4
],
[
1727593364,
452.0546875,
50.2
],
[
1727593364,
452.0546875,
54.3
],
[
1727593364,
452.0546875,
49.8
],
[
1727593364,
452.0546875,
56.5
],
[
1727593424,
450.52734375,
50.2
],
[
1727593424,
450.52734375,
55.6
],
[
1727593424,
450.52734375,
50.3
],
[
1727593424,
450.52734375,
39.4
],
[
1727593439,
450.296875,
50
],
[
1727593439,
450.296875,
55.1
],
[
1727593439,
450.296875,
48.9
],
[
1727593439,
450.296875,
55.6
],
[
1727593455,
449.80859375,
50
],
[
1727593455,
449.80859375,
53.2
],
[
1727593455,
449.80859375,
49.5
],
[
1727593455,
449.80859375,
55.3
],
[
1727593474,
449.90234375,
50
],
[
1727593474,
449.90234375,
54.2
],
[
1727593474,
449.90234375,
50.2
],
[
1727593474,
449.90234375,
48.7
],
[
1727593556,
450.89453125,
50.2
],
[
1727593556,
450.89453125,
51.1
],
[
1727593556,
450.89453125,
49.9
],
[
1727593556,
450.89453125,
56.5
],
[
1727593780,
456.87109375,
50.2
],
[
1727593780,
456.87109375,
38.2
],
[
1727593780,
456.87109375,
50.4
],
[
1727593780,
456.87109375,
45.9
],
[
1727594052,
465.28125,
50.2
],
[
1727594052,
465.28125,
56
],
[
1727594052,
465.28125,
49
],
[
1727594052,
465.28125,
55.8
],
[
1727594400,
461.546875,
50.2
],
[
1727594400,
461.546875,
50
],
[
1727594400,
461.546875,
49.7
],
[
1727594400,
461.546875,
56.5
],
[
1727594562,
466.62890625,
50.2
],
[
1727594562,
466.62890625,
54.3
],
[
1727594562,
466.62890625,
49.6
],
[
1727594562,
466.62890625,
55.6
],
[
1727594678,
465.796875,
50.2
],
[
1727594678,
465.796875,
53.2
],
[
1727594678,
465.796875,
50
],
[
1727594678,
465.796875,
39.4
],
[
1727594797,
464.32421875,
50.2
],
[
1727594797,
464.32421875,
54.3
],
[
1727594797,
464.32421875,
50.3
],
[
1727594797,
464.32421875,
40
],
[
1727594954,
467.140625,
50.2
],
[
1727594954,
467.140625,
54.3
],
[
1727594954,
467.140625,
50.5
],
[
1727594954,
467.140625,
42.4
],
[
1727595035,
458.01171875,
50.1
],
[
1727595035,
458.01171875,
38.2
],
[
1727595035,
458.01171875,
51
],
[
1727595035,
458.01171875,
37.5
],
[
1727595294,
466.8125,
50.2
],
[
1727595294,
466.8125,
51
],
[
1727595294,
466.8125,
50.7
],
[
1727595294,
466.8125,
41.9
],
[
1727595462,
461.19921875,
50.2
],
[
1727595462,
461.19921875,
55.3
],
[
1727595462,
461.19921875,
49.6
],
[
1727595462,
461.19921875,
55.6
],
[
1727595613,
462.7578125,
50.2
],
[
1727595613,
462.7578125,
44.7
],
[
1727595613,
462.7578125,
50.7
],
[
1727595613,
462.7578125,
39.4
],
[
1727595740,
459.08984375,
50.2
],
[
1727595740,
459.08984375,
55.6
],
[
1727595740,
459.08984375,
49.7
],
[
1727595740,
459.08984375,
57.8
],
[
1727595957,
463.29296875,
50.2
],
[
1727595957,
463.29296875,
55.3
],
[
1727595957,
463.29296875,
50.3
],
[
1727595957,
463.29296875,
41.2
],
[
1727596077,
471.34375,
50.2
],
[
1727596077,
471.34375,
52.1
],
[
1727596077,
471.34375,
49.7
],
[
1727596077,
471.34375,
55.6
],
[
1727596205,
471.265625,
50.2
],
[
1727596205,
471.265625,
39.4
],
[
1727596205,
471.265625,
49.8
],
[
1727596205,
471.265625,
58.7
],
[
1727596323,
463.76953125,
50.2
],
[
1727596323,
463.76953125,
53.2
],
[
1727596323,
463.76953125,
49.5
],
[
1727596323,
463.76953125,
57.8
],
[
1727596462,
479.45703125,
50.2
],
[
1727596462,
479.45703125,
54.3
],
[
1727596462,
479.45703125,
50.3
],
[
1727596462,
479.45703125,
38.7
],
[
1727596652,
467.875,
50.2
],
[
1727596652,
467.875,
56.3
],
[
1727596652,
467.875,
49.4
],
[
1727596652,
467.875,
55.6
],
[
1727596958,
471.84765625,
50.2
],
[
1727596958,
471.84765625,
38.2
],
[
1727596958,
471.84765625,
50.9
],
[
1727596958,
471.84765625,
39.4
],
[
1727597235,
470.67578125,
50.2
],
[
1727597235,
470.67578125,
38.2
],
[
1727597235,
470.67578125,
51
],
[
1727597235,
470.67578125,
38.2
],
[
1727597522,
471.1640625,
50.2
],
[
1727597522,
471.1640625,
52.1
],
[
1727597522,
471.1640625,
50.5
],
[
1727597522,
471.1640625,
38.7
],
[
1727597696,
469.875,
50.2
],
[
1727597696,
469.875,
54.3
],
[
1727597696,
469.875,
49.5
],
[
1727597696,
469.875,
56.8
],
[
1727598011,
472.375,
50.2
],
[
1727598011,
472.375,
36.4
],
[
1727598011,
472.375,
50.4
],
[
1727598011,
472.375,
55.6
],
[
1727598416,
478.5859375,
50.2
],
[
1727598416,
478.5859375,
54.3
],
[
1727598416,
478.5859375,
50.1
],
[
1727598416,
478.5859375,
39.4
],
[
1727598749,
482.46484375,
50.2
],
[
1727598749,
482.46484375,
35.3
],
[
1727598749,
482.46484375,
50
],
[
1727598749,
482.46484375,
57.8
],
[
1727599088,
475.58984375,
50.2
],
[
1727599088,
475.58984375,
55.1
],
[
1727599088,
475.58984375,
48.9
],
[
1727599088,
475.58984375,
57.8
],
[
1727599350,
487.2578125,
50.2
],
[
1727599350,
487.2578125,
40
],
[
1727599350,
487.2578125,
50.6
],
[
1727599350,
487.2578125,
39.4
],
[
1727599629,
481.5234375,
50.2
],
[
1727599629,
481.5234375,
53.2
],
[
1727599629,
481.5234375,
50
],
[
1727599629,
481.5234375,
41.9
],
[
1727599917,
489.4140625,
50.2
],
[
1727599917,
489.4140625,
55.3
],
[
1727599917,
489.4140625,
49.4
],
[
1727599917,
489.4140625,
57.8
],
[
1727600188,
493.88671875,
50.2
],
[
1727600188,
493.88671875,
56.2
],
[
1727600188,
493.88671875,
50
],
[
1727600188,
493.88671875,
55.6
],
[
1727600472,
490.71484375,
50.2
],
[
1727600472,
490.71484375,
55.3
],
[
1727600472,
490.71484375,
49.6
],
[
1727600472,
490.71484375,
47.4
],
[
1727600788,
496.60546875,
50.2
],
[
1727600788,
496.60546875,
55.6
],
[
1727600788,
496.60546875,
50.4
],
[
1727600788,
496.60546875,
40.6
],
[
1727601180,
493.1953125,
50.2
],
[
1727601180,
493.1953125,
54
],
[
1727601180,
493.1953125,
50.1
],
[
1727601180,
493.1953125,
56.5
],
[
1727601666,
476.515625,
50.2
],
[
1727601666,
476.515625,
48.8
],
[
1727602408,
476.60546875,
49.7
],
[
1727602408,
476.60546875,
52.1
]
];
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);
}
}
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var pres = document.getElementsByTagName('pre');
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pres[i].style.width = width + 'px';
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window.addEventListener('load', updatePreWidths);
window.addEventListener('resize', updatePreWidths);
$(document).ready(function() {
colorize_table_entries();
add_up_down_arrows_for_scrolling();
add_colorize_to_gridjs_table();
});
$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();;
plotExitCodesPieChart();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0, failed: 37): </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> n_samples</th><th>confidence</th><th>feature_proportion</th><th>n_clusters</th><th>result </th></tr></thead><tbody><tr><td> 100</td><td>0.25</td><td>0.113997</td><td>3</td><td>0.263726 </td></tr></tbody></table><h2>Experiment parameters: </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type </th></tr></thead><tbody><tr><td> n_samples</td><td>range</td><td>100</td><td>1000</td><td></td><td>int </td></tr><tr><td> confidence</td><td>choice</td><td></td><td></td><td>0.001, 0.005,</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>0.01, 0.025,</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>0.05, 0.1,</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>0.25</td><td></td></tr><tr><td> feature_propo…</td><td>range</td><td>0</td><td>0.2</td><td></td><td>float </td></tr><tr><td> n_clusters</td><td>range</td><td>1</td><td>4</td><td></td><td>int </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>37</td>
<td>460</td>
<td>4</td>
<td>501</td>
</tr>
</tbody>
</table>
<h1> Results</h1>
<div id='tab_results_csv_table'></div>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,result,n_samples,confidence,feature_proportion,n_clusters
0,0_0,COMPLETED,Sobol,0.299364447660395605055327905575,392,0.250000000000000000000000000000,0.125973188877105707339509876874,1
1,1_0,COMPLETED,Sobol,0.310570429329474206880945530429,661,0.010000000000000000208166817117,0.129536976665258402041658314374,1
2,2_0,COMPLETED,Sobol,0.272050168837433714585927191365,124,0.250000000000000000000000000000,0.004527391307055950511739617070,2
3,3_0,COMPLETED,Sobol,0.272345634346357945432259839436,127,0.025000000000000001387778780781,0.188410873524844646453857421875,2
4,4_0,COMPLETED,Sobol,0.316195127834056899374104432354,721,0.100000000000000005551115123126,0.025189650245010852813720703125,1
5,5_0,COMPLETED,Sobol,0.317855764592378187138876910467,861,0.001000000000000000020816681712,0.095915075019001969081067215939,1
6,6_0,COMPLETED,Sobol,0.322983598649300485128321724915,974,0.100000000000000005551115123126,0.002316499501466751011896638701,1
7,7_0,COMPLETED,Sobol,0.308909792571152919116173052316,610,0.100000000000000005551115123126,0.103769115358591082487471624063,2
8,8_0,COMPLETED,Sobol,0.291261456825856246410921812640,335,0.250000000000000000000000000000,0.094662074930965900421142578125,2
9,9_0,COMPLETED,Sobol,0.305904486251808993557688154397,521,0.250000000000000000000000000000,0.175773870013654243127376730627,3
10,10_0,COMPLETED,Sobol,0.289911963338157274705508825718,268,0.025000000000000001387778780781,0.088243950903415691033870871252,2
11,11_0,COMPLETED,Sobol,0.314307766521948916960127462517,703,0.025000000000000001387778780781,0.151831477321684366055265513751,4
12,12_0,COMPLETED,Sobol,0.299522431259044874529706703470,466,0.001000000000000000020816681712,0.134880634583532804660066517499,1
13,13_0,COMPLETED,Sobol,0.320090448625180878927665162337,880,0.100000000000000005551115123126,0.054677539132535458998862765156,2
14,14_0,COMPLETED,Sobol,0.284035214664737134526717454719,218,0.050000000000000002775557561563,0.188544376567006122247249777502,1
15,15_0,COMPLETED,Sobol,0.318912204534491094953807532875,766,0.010000000000000000208166817117,0.025302996300160886938845905547,4
16,16_0,COMPLETED,Sobol,0.314120839363241644015545261936,182,0.001000000000000000020816681712,0.011049367859959602702901726445,4
17,17_0,COMPLETED,Sobol,0.313270622286541233769696646050,717,0.025000000000000001387778780781,0.115910582058131705895931418127,4
18,18_0,COMPLETED,Sobol,0.318783164495899606905027212633,843,0.250000000000000000000000000000,0.087754449248313909359708873126,3
19,19_0,COMPLETED,Sobol,0.279965026531596716807825941942,159,0.100000000000000005551115123126,0.071889520995318895169035045001,4
20,20_0,COMPLETED,BoTorch,0.267884708152436035177856865630,100,0.050000000000000002775557561563,0.177883868649023973995682013083,3
21,21_0,COMPLETED,BoTorch,0.264487457790641533605935364903,100,0.250000000000000000000000000000,0.027738805364601382130729234632,3
22,22_0,COMPLETED,BoTorch,0.265705499276410961329020210542,100,0.250000000000000000000000000000,0.062866928559237936213754949222,2
23,23_0,COMPLETED,BoTorch,0.273271828268210281720484999823,100,0.010000000000000000208166817117,0.200000000000000011102230246252,2
24,24_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
25,25_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
26,26_0,COMPLETED,BoTorch,0.271438736131210833768534484989,100,0.005000000000000000104083408559,0.200000000000000011102230246252,1
27,27_0,COMPLETED,BoTorch,0.270335262904003892359128258249,100,0.100000000000000005551115123126,0.096077322497792383426684637016,2
28,28_0,COMPLETED,BoTorch,0.269007477086348312234065360826,100,0.025000000000000001387778780781,0.200000000000000011102230246252,3
29,29_0,COMPLETED,BoTorch,0.265430535455861038585112510191,100,0.100000000000000005551115123126,0.200000000000000011102230246252,3
30,30_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
31,31_0,COMPLETED,BoTorch,0.268408104196816155173621609720,100,0.050000000000000002775557561563,0.156571684842136849002969256617,2
32,32_0,COMPLETED,BoTorch,0.285082006753497374518246942898,100,0.001000000000000000020816681712,0.200000000000000011102230246252,3
33,33_0,COMPLETED,BoTorch,0.270660877954655099131286988268,100,0.100000000000000005551115123126,0.045550926298050381513249362797,1
34,34_0,COMPLETED,BoTorch,0.267842498794018335495081828412,100,0.050000000000000002775557561563,0.200000000000000011102230246252,4
35,35_0,COMPLETED,BoTorch,0.266488181379643029877968274377,100,0.250000000000000000000000000000,0.057876130083879789856027286987,3
36,36_0,COMPLETED,BoTorch,0.265090448625180941100154541346,100,0.250000000000000000000000000000,0.147416184455075177650584805633,3
37,37_0,COMPLETED,BoTorch,0.270454655089242601562204981747,100,0.100000000000000005551115123126,0.088106778025058174463879367977,2
38,38_0,COMPLETED,BoTorch,0.269813072841292855841288655938,100,0.025000000000000001387778780781,0.171349745133378444927885198013,1
39,39_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,3
40,24_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
41,25_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
42,42_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
43,24_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
44,44_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.017034812548118678787734125990,1
45,25_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
46,46_0,COMPLETED,BoTorch,0.270665701881331433042987555382,100,0.010000000000000000208166817117,0.200000000000000011102230246252,1
47,47_0,COMPLETED,BoTorch,0.265104920405209831812953780172,100,0.250000000000000000000000000000,0.111624761330627708666440867091,2
48,48_0,COMPLETED,BoTorch,0.270619874577906371904134630313,100,0.100000000000000005551115123126,0.034287894684050565508037777818,2
49,49_0,COMPLETED,BoTorch,0.265680173661360319314894695708,100,0.250000000000000000000000000000,0.106217880490681981786593723882,3
50,50_0,COMPLETED,BoTorch,0.265209840810419650303231264843,100,0.250000000000000000000000000000,0.159470336260858297494635849034,2
51,51_0,COMPLETED,BoTorch,0.265921369995176126721503351291,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4
52,52_0,COMPLETED,BoTorch,0.294149782923299518699877808103,308,0.025000000000000001387778780781,0.076951969676878395620889250495,2
53,53_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.068499618636857578790611000841,1
54,54_0,COMPLETED,BoTorch,0.264509165460685036208587916917,100,0.250000000000000000000000000000,0.200000000000000011102230246252,2
55,55_0,COMPLETED,BoTorch,0.279602026049204033242290279304,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1
56,56_0,COMPLETED,BoTorch,0.270036179450072411079020184843,100,0.025000000000000001387778780781,0.199535571062655570084842793221,4
57,57_0,COMPLETED,BoTorch,0.266441148094548996283492670045,100,0.050000000000000002775557561563,0.179968288355066341477339619814,4
58,58_0,COMPLETED,BoTorch,0.292829232995658439442365761352,274,0.025000000000000001387778780781,0.063819841783673739166182770077,3
59,59_0,COMPLETED,BoTorch,0.287747226242161091569471409457,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4
60,60_0,COMPLETED,BoTorch,0.265389532079112422380262614752,100,0.250000000000000000000000000000,0.200000000000000011102230246252,3
61,51_0,COMPLETED,BoTorch,0.264904727448142751633497482544,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4
62,62_0,COMPLETED,BoTorch,0.264904727448142751633497482544,100,0.250000000000000000000000000000,0.157875109277411301844651347892,4
63,63_0,COMPLETED,BoTorch,0.267074288470815268681235465920,100,0.250000000000000000000000000000,0.127902252698476631698198957565,2
64,64_0,COMPLETED,BoTorch,0.279779305354558638363471345656,118,0.001000000000000000020816681712,0.195173412651064803746692177810,2
65,65_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.145585477484363984279625015006,1
66,66_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.147851125674286931754863871902,1
67,67_0,COMPLETED,BoTorch,0.265311143270622329382035786693,100,0.100000000000000005551115123126,0.178017096733771307270899342257,4
68,68_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063038119545012802347194735830,1
69,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
70,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
71,71_0,COMPLETED,BoTorch,0.282743608297153836694803885621,100,0.001000000000000000020816681712,0.095557102229740842336269679436,1
72,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
73,73_0,COMPLETED,BoTorch,0.265877954655089232538500709779,100,0.100000000000000005551115123126,0.200000000000000011102230246252,2
74,74_0,COMPLETED,BoTorch,0.266189097925711548597860200971,100,0.250000000000000000000000000000,0.022821504645745865902384252877,3
75,75_0,COMPLETED,BoTorch,0.277569946936806588588808608620,173,0.250000000000000000000000000000,0.200000000000000011102230246252,4
76,76_0,COMPLETED,BoTorch,0.284845634346357901023338854429,220,0.025000000000000001387778780781,0.171978087245400146354512571634,1
77,77_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.200000000000000011102230246252,1
78,77_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.200000000000000011102230246252,1
79,79_0,COMPLETED,BoTorch,0.284362035697057424776801326516,228,0.025000000000000001387778780781,0.192083004366270820861828383386,1
80,80_0,COMPLETED,BoTorch,0.274657501205981646741349777585,135,0.100000000000000005551115123126,0.200000000000000011102230246252,2
81,81_0,COMPLETED,BoTorch,0.273724071394114809585573766526,105,0.005000000000000000104083408559,0.119407873090501992496825778289,1
82,82_0,COMPLETED,BoTorch,0.266254220935841723338910469465,100,0.250000000000000000000000000000,0.042146187575146314141516512564,2
83,83_0,COMPLETED,BoTorch,0.292783405692233489325815298798,346,0.250000000000000000000000000000,0.071503605591182545220263477859,2
84,75_0,COMPLETED,BoTorch,0.277454172696575018797204847942,173,0.250000000000000000000000000000,0.200000000000000011102230246252,4
85,85_0,COMPLETED,BoTorch,0.282850940665701933163234116364,157,0.050000000000000002775557561563,0.053702779364252509197719120948,3
86,86_0,COMPLETED,BoTorch,0.279430776652194845510734921845,152,0.100000000000000005551115123126,0.023917583569774580887212778180,4
87,87_0,COMPLETED,BoTorch,0.266654606849975883697823064722,100,0.250000000000000000000000000000,0.037505726348380785672986803547,2
88,88_0,COMPLETED,BoTorch,0.279657501205981651182241876086,150,0.050000000000000002775557561563,0.079821518535756086354382432546,2
89,89_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.174884221516155097253530925627,1
90,90_0,COMPLETED,BoTorch,0.269813072841292855841288655938,100,0.025000000000000001387778780781,0.148195435547283954624830926150,1
91,77_0,COMPLETED,BoTorch,0.266737819585142310607750459894,100,0.100000000000000005551115123126,0.200000000000000011102230246252,1
92,92_0,COMPLETED,BoTorch,0.265694645441389321049996397051,100,0.250000000000000000000000000000,0.033707657150358803777034921723,2
93,93_0,COMPLETED,BoTorch,0.278112638687891933209073158650,153,0.050000000000000002775557561563,0.071329545044852046831174163799,2
94,94_0,COMPLETED,BoTorch,0.266637723106608826029173542338,100,0.250000000000000000000000000000,0.032172239915852975100651889306,2
95,95_0,COMPLETED,BoTorch,0.295928605885190543212104330451,319,0.001000000000000000020816681712,0.200000000000000011102230246252,4
96,96_0,COMPLETED,BoTorch,0.266509889049686421458318363875,100,0.250000000000000000000000000000,0.017924730352540090699831054621,2
97,97_0,COMPLETED,BoTorch,0.277861794500723569001365831355,166,0.100000000000000005551115123126,0.111611382580355036897579168453,4
98,98_0,COMPLETED,BoTorch,0.267038109020742875365783675079,100,0.250000000000000000000000000000,0.050489515786813543574140794590,4
99,99_0,COMPLETED,BoTorch,0.294187168355040995493254740722,301,0.005000000000000000104083408559,0.080799212261498470910581204407,3
100,100_0,COMPLETED,BoTorch,0.266648576941630466308197355829,100,0.250000000000000000000000000000,0.044482555475510422715590408416,4
101,101_0,COMPLETED,BoTorch,0.287492764109985476927988656826,273,0.025000000000000001387778780781,0.085753487422381785565406175920,1
102,102_0,COMPLETED,BoTorch,0.266125180897250346312432611739,100,0.250000000000000000000000000000,0.033543377052116289671612747725,4
103,103_0,COMPLETED,BoTorch,0.267068258562469851291609757027,100,0.250000000000000000000000000000,0.033440639794442034160582011282,4
104,104_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.036505117536770277653079830316,1
105,105_0,COMPLETED,BoTorch,0.267492764109985570186722725339,100,0.250000000000000000000000000000,0.028830864552846571791100416249,4
106,106_0,COMPLETED,BoTorch,0.292256391702846118896275129373,288,0.010000000000000000208166817117,0.096754763556448367123330456252,2
107,107_0,COMPLETED,BoTorch,0.266766763145200203055651400064,100,0.250000000000000000000000000000,0.023362646599296647831334183820,4
108,108_0,COMPLETED,BoTorch,0.266691992281717360491199997341,100,0.250000000000000000000000000000,0.020726584857856129168185432832,4
109,109_0,COMPLETED,BoTorch,0.278664978292329945652738842909,159,0.100000000000000005551115123126,0.044211268138367058877502557834,3
110,110_0,COMPLETED,BoTorch,0.274739507959478990173352030979,126,0.100000000000000005551115123126,0.059878997487557289458592890696,4
111,111_0,COMPLETED,BoTorch,0.267440906898215091658244091377,100,0.250000000000000000000000000000,0.025656768214582188758488001668,4
112,112_0,COMPLETED,BoTorch,0.265606608779546560228368434764,100,0.250000000000000000000000000000,0.024419600336531684103835715405,4
113,113_0,COMPLETED,BoTorch,0.278607091172214160756936962571,156,0.100000000000000005551115123126,0.027141222999015869599137573687,3
114,114_0,COMPLETED,BoTorch,0.278310419681620846432679172722,152,0.100000000000000005551115123126,0.047694359628426323538530340329,3
115,115_0,COMPLETED,BoTorch,0.265387120115774255424412331195,100,0.250000000000000000000000000000,0.038988677223391096648974496475,4
116,116_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049738440660780827706766160645,1
117,117_0,COMPLETED,BoTorch,0.302309454896285578762160639599,411,0.001000000000000000020816681712,0.140145107403291890868501923251,4
118,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
119,119_0,COMPLETED,BoTorch,0.278637240713941136682763044519,172,0.050000000000000002775557561563,0.200000000000000011102230246252,3
120,120_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.048593689470893541848539598504,1
121,121_0,COMPLETED,BoTorch,0.311116739025566801934985505795,183,0.001000000000000000020816681712,0.035801898679152968640870824402,4
122,122_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.048296241396162453707319173191,1
123,123_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.047665086285069331595298791626,1
124,124_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.047316575317859009552901028428,1
125,125_0,COMPLETED,BoTorch,0.286870477568740955831572136958,211,0.005000000000000000104083408559,0.160722948797047154867456697502,3
126,126_0,COMPLETED,BoTorch,0.292190062711046749654997256584,337,0.005000000000000000104083408559,0.102292789507217193878396699347,3
127,127_0,COMPLETED,BoTorch,0.310909310178485331910280820011,678,0.050000000000000002775557561563,0.128759511771469686847169100474,2
128,128_0,COMPLETED,BoTorch,0.310771828268210370538326969836,182,0.001000000000000000020816681712,0.035995133853017412250974160770,4
129,129_0,FAILED,BoTorch,,230,0.250000000000000000000000000000,0.000000000000000000000000000000,1
130,130_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.045359913357807417344513822854,1
131,131_0,FAILED,BoTorch,,229,0.250000000000000000000000000000,0.000000000000000000000000000000,1
132,132_0,FAILED,BoTorch,,227,0.250000000000000000000000000000,0.000000000000000000000000000000,1
133,133_0,COMPLETED,BoTorch,0.294034008683068059930576509942,352,0.250000000000000000000000000000,0.098168705538623141082510414890,2
134,134_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050315271550182422144459337687,1
135,135_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050766865902727165149599386496,1
136,136_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050504912170116725289581438574,1
137,137_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050970185064206509351070195635,1
138,138_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050614450151675149935925901445,1
139,139_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050138878747595573559525661267,1
140,140_0,COMPLETED,BoTorch,0.289524843222383032603772790026,254,0.010000000000000000208166817117,0.009544117887174069850275692772,3
141,141_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.027417080626232416540322134324,1
142,142_0,COMPLETED,BoTorch,0.284444042450554768208803579910,173,0.001000000000000000020816681712,0.123181387492010754880489287189,3
143,143_0,COMPLETED,BoTorch,0.272988422575976885653403769538,121,0.250000000000000000000000000000,0.032939386489557666293226390053,1
144,144_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.050397608925995256157204238434,1
145,145_0,COMPLETED,BoTorch,0.265619874577906367463242531812,100,0.250000000000000000000000000000,0.163236779356049632738745458482,4
146,146_0,COMPLETED,BoTorch,0.280151953690303878730105680006,171,0.100000000000000005551115123126,0.020056208405210197709411801270,2
147,147_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049805387446812503238202651801,1
148,148_0,COMPLETED,BoTorch,0.292800289435600546994464821182,293,0.010000000000000000208166817117,0.075862777938960734847029243610,4
149,149_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.051917039133237842529755567966,1
150,150_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.052258665421560851482851717265,1
151,151_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.052211428481386795708019121776,1
152,152_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.051899044103794193805168077915,1
153,153_0,COMPLETED,BoTorch,0.278017366136034782542196808208,170,0.100000000000000005551115123126,0.017157494960939496941865201052,2
154,154_0,FAILED,BoTorch,,151,0.250000000000000000000000000000,0.000000000000000000000000000000,4
155,155_0,COMPLETED,BoTorch,0.278950795947901619697972819267,171,0.100000000000000005551115123126,0.016893846161754374340935314081,2
156,156_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049652065133151290776680752970,1
157,157_0,COMPLETED,BoTorch,0.264856488181379634561096736434,100,0.250000000000000000000000000000,0.183010858806825327249612200831,3
158,158_0,COMPLETED,BoTorch,0.280827303424987961832925975614,178,0.050000000000000002775557561563,0.070791305551041960875124914310,2
159,159_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.052944212052922060007809079707,1
160,160_0,COMPLETED,BoTorch,0.264006271104679224315248120547,100,0.250000000000000000000000000000,0.189759541032041040287126065778,4
161,161_0,COMPLETED,BoTorch,0.287022431259044807916325225960,235,0.005000000000000000104083408559,0.105382706189895716808457848401,3
162,162_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.049088870410681550993103883229,1
163,163_0,COMPLETED,BoTorch,0.277050168837433719026819289866,148,0.050000000000000002775557561563,0.005102395700533231270856759920,2
164,164_0,COMPLETED,BoTorch,0.264944524843222395382724698720,100,0.100000000000000005551115123126,0.198080013733558657484934428794,4
165,165_0,COMPLETED,BoTorch,0.265680173661360319314894695708,100,0.250000000000000000000000000000,0.191124439388942857220854421030,3
166,166_0,COMPLETED,BoTorch,0.278221177038109002133126068657,158,0.250000000000000000000000000000,0.200000000000000011102230246252,1
167,167_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.048862258501669669774614135349,1
168,168_0,COMPLETED,BoTorch,0.287219006271104637662006098253,234,0.050000000000000002775557561563,0.176981218309804899657677879077,1
169,169_0,COMPLETED,BoTorch,0.283358658948383967945972017333,198,0.001000000000000000020816681712,0.200000000000000011102230246252,1
170,170_0,COMPLETED,BoTorch,0.288865171249397034713979337539,139,0.010000000000000000208166817117,0.093497685206801461887948789808,4
171,171_0,COMPLETED,BoTorch,0.280168837433671047421057664906,153,0.005000000000000000104083408559,0.167414708244667392866844579657,1
172,172_0,COMPLETED,BoTorch,0.287132175590931071340605740261,202,0.001000000000000000020816681712,0.145594283020721193544488869520,4
173,173_0,COMPLETED,BoTorch,0.277379401833092176232753445220,162,0.005000000000000000104083408559,0.176090543290137402365758134692,1
174,174_0,COMPLETED,BoTorch,0.304261939218523846939490340446,419,0.001000000000000000020816681712,0.123217977003118275836079931196,4
175,175_0,COMPLETED,BoTorch,0.279623733719247424822640368802,147,0.005000000000000000104083408559,0.161372700373134586593337758131,1
176,176_0,COMPLETED,BoTorch,0.287715870718765032165720185731,204,0.001000000000000000020816681712,0.168326320223342013049361298727,3
177,177_0,COMPLETED,BoTorch,0.284813072841292758141662488924,183,0.001000000000000000020816681712,0.101967054482906424595078931361,3
178,178_0,COMPLETED,BoTorch,0.282455378678244106716022088222,185,0.005000000000000000104083408559,0.174652985088769391319729606948,3
179,179_0,COMPLETED,BoTorch,0.280799565846599152862950177223,144,0.005000000000000000104083408559,0.153040437513289662163629145653,2
180,180_0,COMPLETED,BoTorch,0.287922093584177529734802192252,220,0.001000000000000000020816681712,0.173229805605066261575686326069,2
181,181_0,COMPLETED,BoTorch,0.285477568740955089943156508525,192,0.001000000000000000020816681712,0.186435694528571727079935271831,2
182,182_0,COMPLETED,BoTorch,0.280915340086830722654553937900,177,0.001000000000000000020816681712,0.139001928384399953131378424587,2
183,183_0,COMPLETED,BoTorch,0.285547515677761709618209806649,192,0.001000000000000000020816681712,0.184572013116698935286663640909,2
184,184_0,COMPLETED,BoTorch,0.271438736131210833768534484989,100,0.005000000000000000104083408559,0.124884889729805784175376004441,1
185,185_0,COMPLETED,BoTorch,0.284359623733719257820951042959,194,0.005000000000000000104083408559,0.200000000000000011102230246252,1
186,186_0,COMPLETED,BoTorch,0.279052098408104187754474878602,155,0.005000000000000000104083408559,0.169816536097741915822822988957,1
187,187_0,COMPLETED,BoTorch,0.265887602508441900361901844008,100,0.100000000000000005551115123126,0.183367946131215348026799460968,4
188,188_0,COMPLETED,BoTorch,0.281319343945972022424939495977,184,0.005000000000000000104083408559,0.198297852464462798227629036774,1
189,189_0,COMPLETED,BoTorch,0.324157018813314046212781249778,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
190,190_0,COMPLETED,BoTorch,0.321632899179932429944983596215,857,0.001000000000000000020816681712,0.078690445868493677505917105464,1
191,191_0,COMPLETED,BoTorch,0.268461167390255717180025385460,111,0.250000000000000000000000000000,0.199369732630930496242172011989,4
192,192_0,COMPLETED,BoTorch,0.286938012542209408550775151525,125,0.010000000000000000208166817117,0.013685528305224049783195283680,3
193,193_0,COMPLETED,BoTorch,0.266368789194404209652589088364,100,0.100000000000000005551115123126,0.182745895072306097084435805300,4
194,194_0,COMPLETED,BoTorch,0.322161119150988883852448907419,976,0.001000000000000000020816681712,0.200000000000000011102230246252,3
195,195_0,COMPLETED,BoTorch,0.323787988422575945257619878248,965,0.001000000000000000020816681712,0.200000000000000011102230246252,4
196,195_0,COMPLETED,BoTorch,0.323238060781476099769804477546,965,0.001000000000000000020816681712,0.200000000000000011102230246252,4
197,197_0,COMPLETED,BoTorch,0.322635069946936803297887763620,965,0.001000000000000000020816681712,0.199020702640858915266619533213,2
198,198_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,4
199,199_0,COMPLETED,BoTorch,0.278070429329474233526298121433,177,0.050000000000000002775557561563,0.026468955384571127287252068072,2
200,189_0,COMPLETED,BoTorch,0.325141099855282167396808290505,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
201,201_0,COMPLETED,BoTorch,0.305247226242161162623744985467,544,0.050000000000000002775557561563,0.196824549372222490362105418171,2
202,202_0,COMPLETED,BoTorch,0.314370477568740924745327447454,188,0.001000000000000000020816681712,0.022440683374965957053159826273,4
203,203_0,COMPLETED,BoTorch,0.312692957067052579311905446957,187,0.001000000000000000020816681712,0.023391348905394158258497228076,4
204,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
205,205_0,COMPLETED,BoTorch,0.264770863482875040695319057704,100,0.250000000000000000000000000000,0.183832211683872565055608561124,4
206,206_0,COMPLETED,BoTorch,0.319965026531596752334962729947,919,0.001000000000000000020816681712,0.198852693699973381846746178780,1
207,207_0,COMPLETED,BoTorch,0.279013506994693627483172804205,155,0.050000000000000002775557561563,0.012644201833267138326832856876,2
208,42_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
209,209_0,COMPLETED,BoTorch,0.323362276893391253906884230673,920,0.001000000000000000020816681712,0.198834649936369234346500434185,1
210,189_0,COMPLETED,BoTorch,0.324854076218041520895951634884,1000,0.001000000000000000020816681712,0.200000000000000011102230246252,4
211,211_0,COMPLETED,BoTorch,0.278528702363724067758710134513,111,0.001000000000000000020816681712,0.117070640942127046635334863822,1
212,212_0,COMPLETED,BoTorch,0.323101784852870221875775769149,976,0.010000000000000000208166817117,0.200000000000000011102230246252,4
213,213_0,COMPLETED,BoTorch,0.281054027978774767504432929854,159,0.025000000000000001387778780781,0.180340861569562305177782945975,2
214,214_0,COMPLETED,BoTorch,0.277169561022672428229896013363,174,0.250000000000000000000000000000,0.188441310756199270626254360650,4
215,215_0,COMPLETED,BoTorch,0.269611673902556692183907216531,107,0.050000000000000002775557561563,0.111728123906690679500997021023,1
216,216_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060642060464390273866186475971,1
217,217_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060225730870675557582227810371,1
218,218_0,FAILED,BoTorch,,569,0.001000000000000000020816681712,0.000000000000000000000000000000,4
219,219_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062580185499531260084893347084,1
220,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
221,221_0,RUNNING,BoTorch,,187,0.250000000000000000000000000000,0.052850737238033990150753993476,1
222,59_0,COMPLETED,BoTorch,0.288771104679208856502725666360,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4
223,223_0,COMPLETED,BoTorch,0.298052339604437999120989388757,375,0.100000000000000005551115123126,0.153831505030393611566097433752,1
224,224_0,COMPLETED,BoTorch,0.301693198263386364033067366108,420,0.250000000000000000000000000000,0.012946567745694659637423207244,2
225,225_0,FAILED,BoTorch,,250,0.250000000000000000000000000000,0.000000000000000000000000000000,1
226,226_0,COMPLETED,BoTorch,0.295326821032320330218112758303,348,0.250000000000000000000000000000,0.090501819572133074265352092880,2
227,118_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
228,228_0,COMPLETED,BoTorch,0.283795224312590410598033940914,140,0.010000000000000000208166817117,0.041977368993984839895539096233,3
229,229_0,FAILED,BoTorch,,106,0.005000000000000000104083408559,0.000000000000000000000000000000,1
230,230_0,COMPLETED,BoTorch,0.273742161119150950732148430689,100,0.010000000000000000208166817117,0.070078831681142525478200866473,1
231,231_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063828190775983215110578328222,1
232,232_0,COMPLETED,BoTorch,0.289294500723588976498490410449,145,0.010000000000000000208166817117,0.090575529823945233331983217795,4
233,233_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063783024454271863845633561141,1
234,225_0,FAILED,BoTorch,,250,0.250000000000000000000000000000,0.000000000000000000000000000000,1
235,235_0,COMPLETED,BoTorch,0.287575976845151903837916051998,259,0.050000000000000002775557561563,0.011784025878387003813396738394,2
236,236_0,COMPLETED,BoTorch,0.294482633863965226339587388793,350,0.250000000000000000000000000000,0.089586741383260046212022587042,2
237,237_0,FAILED,BoTorch,,260,0.050000000000000002775557561563,0.000000000000000000000000000000,2
238,238_0,COMPLETED,BoTorch,0.292444524843222364296480009216,298,0.005000000000000000104083408559,0.103926612404703050396292951518,3
239,239_0,FAILED,BoTorch,,212,0.025000000000000001387778780781,0.000000000000000000000000000000,1
240,240_0,COMPLETED,BoTorch,0.284140135069946953016994939389,187,0.010000000000000000208166817117,0.018465700310964838770644291799,2
241,241_0,COMPLETED,BoTorch,0.305875542691751101109787214227,296,0.001000000000000000020816681712,0.050740103637683255100032653218,4
242,242_0,FAILED,BoTorch,,249,0.250000000000000000000000000000,0.000000000000000000000000000000,1
243,243_0,COMPLETED,BoTorch,0.278481669078630034164234530181,105,0.001000000000000000020816681712,0.112032745622363760773509966384,1
244,244_0,FAILED,BoTorch,,268,0.250000000000000000000000000000,0.000000000000000000000000000000,1
245,245_0,COMPLETED,BoTorch,0.284956584659913136903242047993,231,0.250000000000000000000000000000,0.025574268507030123442902436182,1
246,246_0,COMPLETED,BoTorch,0.279073806078147579334824968100,118,0.025000000000000001387778780781,0.045409233378626540078570883452,2
247,247_0,FAILED,BoTorch,,261,0.010000000000000000208166817117,0.000000000000000000000000000000,2
248,248_0,FAILED,BoTorch,,255,0.250000000000000000000000000000,0.000000000000000000000000000000,1
249,249_0,COMPLETED,BoTorch,0.290010853835021675806160601496,260,0.010000000000000000208166817117,0.024920782002454670844837636423,3
250,248_0,FAILED,BoTorch,,255,0.250000000000000000000000000000,0.000000000000000000000000000000,1
251,251_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062382576859791719070535975789,1
252,252_0,COMPLETED,BoTorch,0.293769898697539777465692623082,316,0.250000000000000000000000000000,0.115827191472457152743302799536,1
253,253_0,FAILED,BoTorch,,258,0.250000000000000000000000000000,0.000000000000000000000000000000,1
254,254_0,COMPLETED,BoTorch,0.283778340569223352929384418530,236,0.250000000000000000000000000000,0.029641443862270928227831134905,1
255,255_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
256,256_0,FAILED,BoTorch,,258,0.250000000000000000000000000000,0.000000000000000000000000000000,4
257,257_0,COMPLETED,BoTorch,0.280135069946936821061456157622,185,0.250000000000000000000000000000,0.024488023150332334509915455101,2
258,258_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062191181517896244879128886396,1
259,259_0,COMPLETED,BoTorch,0.291406174626145708650426513486,275,0.250000000000000000000000000000,0.200000000000000011102230246252,4
260,260_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.061789307872297527524096949492,1
261,261_0,COMPLETED,BoTorch,0.284400627110467874025800938398,232,0.050000000000000002775557561563,0.194390432577510707812251666837,1
262,262_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064297448962912340242326081352,1
263,263_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062809465903159777955266918070,1
264,264_0,COMPLETED,BoTorch,0.280564399421128762845967230533,181,0.010000000000000000208166817117,0.031742202679727507530493824106,1
265,265_0,COMPLETED,BoTorch,0.285085624698504624952022368234,226,0.050000000000000002775557561563,0.175189593122712150563557997884,2
266,266_0,COMPLETED,BoTorch,0.293098166907863055818950215325,316,0.250000000000000000000000000000,0.086508836994181007029602881175,1
267,267_0,COMPLETED,BoTorch,0.285050651230101315114495719172,222,0.100000000000000005551115123126,0.152520216165787036999290648964,2
268,268_0,COMPLETED,BoTorch,0.295255668113844627065134318400,349,0.250000000000000000000000000000,0.086369363620642902734658719055,2
269,269_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.071254519137674068862686738157,1
270,270_0,COMPLETED,BoTorch,0.275168837433671042980165566405,129,0.050000000000000002775557561563,0.017891866121939138667418234263,2
271,271_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.069851468866725219264424140420,1
272,272_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.070360318576455901928845548809,1
273,273_0,COMPLETED,BoTorch,0.286810178485287003979919973062,239,0.050000000000000002775557561563,0.184812060794586452416865540727,3
274,274_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.069608187028502729876144883292,1
275,275_0,COMPLETED,BoTorch,0.267978774722624213389110536809,102,0.100000000000000005551115123126,0.147233929973952792913394205243,2
276,276_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.066981210791419901195986597031,1
277,277_0,COMPLETED,BoTorch,0.287477086348287502737264276220,272,0.250000000000000000000000000000,0.060280831158161164717856905781,4
278,278_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.066774721653305099677844225425,1
279,279_0,FAILED,BoTorch,,545,0.001000000000000000020816681712,0.000000000000000000000000000000,1
280,280_0,COMPLETED,BoTorch,0.290629522431259057491104158544,327,0.050000000000000002775557561563,0.031841886265239142950189688008,4
281,281_0,COMPLETED,BoTorch,0.275183309213699933692964805232,100,0.050000000000000002775557561563,0.073456958530075056046371173579,2
282,282_0,FAILED,BoTorch,,562,0.001000000000000000020816681712,0.000000000000000000000000000000,1
283,283_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.067291773271187099725310076792,1
284,284_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.068106368089074095784063445080,1
285,285_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.067713032974790740570192326686,1
286,286_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.067970476920417952082509316369,1
287,287_0,COMPLETED,BoTorch,0.276031114327062176982963137561,147,0.050000000000000002775557561563,0.029684903280067117398166587350,1
288,288_0,COMPLETED,BoTorch,0.296496623251326529846494395315,337,0.250000000000000000000000000000,0.198653219666456104519625114335,3
289,289_0,COMPLETED,BoTorch,0.290800771828268245222659516003,290,0.100000000000000005551115123126,0.186132790415169169229159251699,4
290,290_0,COMPLETED,BoTorch,0.303004100337674886489480741147,448,0.050000000000000002775557561563,0.133432193286716932467683704999,3
291,291_0,COMPLETED,BoTorch,0.300605402797877507836687982490,461,0.250000000000000000000000000000,0.119272310341075660211629383411,2
292,292_0,COMPLETED,BoTorch,0.288464785335262874355066742282,279,0.050000000000000002775557561563,0.027026415231706832353886227338,2
293,293_0,COMPLETED,BoTorch,0.287159913169319880310581538652,242,0.100000000000000005551115123126,0.156560805597633245778155242078,3
294,294_0,COMPLETED,BoTorch,0.274887843704775702846632157161,100,0.050000000000000002775557561563,0.066374546291804933928482057581,2
295,295_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065952769341442632478056395939,1
296,296_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065957181510519144462811880203,1
297,297_0,COMPLETED,BoTorch,0.266480945489628529010417423706,100,0.250000000000000000000000000000,0.090103966775734373184647552080,3
298,298_0,COMPLETED,BoTorch,0.283187409551374780214416659874,186,0.010000000000000000208166817117,0.070589791280776165161015001104,1
299,299_0,COMPLETED,BoTorch,0.287364930053063183379435940878,272,0.025000000000000001387778780781,0.005747990976141181471148478010,3
300,300_0,COMPLETED,BoTorch,0.301029908345393115709498488286,477,0.025000000000000001387778780781,0.038225660453959808826596145082,1
301,301_0,COMPLETED,BoTorch,0.288591413410516195447996778967,171,0.005000000000000000104083408559,0.099360480747154478198268634515,3
302,302_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064052036270882531376891222408,1
303,303_0,COMPLETED,BoTorch,0.313490110950313538573652749619,755,0.010000000000000000208166817117,0.104958781868517328494938567474,4
304,304_0,COMPLETED,BoTorch,0.308582971538832628866089180519,612,0.005000000000000000104083408559,0.064217537837185381666138539458,2
305,305_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064231553822249640162134198818,1
306,306_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064451514504983584141761809860,1
307,307_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064668571264303489898317423012,1
308,308_0,COMPLETED,BoTorch,0.308116256632899210288201174990,538,0.010000000000000000208166817117,0.107040308120906327293830884173,3
309,309_0,COMPLETED,BoTorch,0.299108779546550906935920011165,386,0.025000000000000001387778780781,0.037533670135556418401279188402,1
310,310_0,COMPLETED,BoTorch,0.267342016401350690557592315599,100,0.250000000000000000000000000000,0.064316705885661087926941092974,2
311,311_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064204083499126984868610179547,1
312,312_0,COMPLETED,BoTorch,0.265508924264351131583339338249,100,0.250000000000000000000000000000,0.161606480231661742141824333885,3
313,313_0,COMPLETED,BoTorch,0.264781717317896791996645333711,100,0.250000000000000000000000000000,0.121070516694132851442944343034,4
314,314_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063337250252014129503486117301,1
315,315_0,COMPLETED,BoTorch,0.313241678726483341321795705881,100,0.001000000000000000020816681712,0.000000025935531194014214255653,4
316,316_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063543069480409913873941718521,1
317,317_0,COMPLETED,BoTorch,0.280318379160636732549960470351,141,0.010000000000000000208166817117,0.071473442489243865782810871679,2
318,318_0,COMPLETED,BoTorch,0.276101061263868796658016435686,145,0.100000000000000005551115123126,0.151033775508403800280632367503,3
319,319_0,COMPLETED,BoTorch,0.265844187168355006178899202496,100,0.250000000000000000000000000000,0.119595762080195497367718360238,4
320,320_0,COMPLETED,BoTorch,0.265735648818137937254846292490,100,0.250000000000000000000000000000,0.122461282514456565317928493641,4
321,321_0,COMPLETED,BoTorch,0.309163048721659450279730663169,665,0.250000000000000000000000000000,0.042090272349207133451010065528,1
322,322_0,COMPLETED,BoTorch,0.279647853352629094381143204373,169,0.100000000000000005551115123126,0.111696985327760761608217876528,3
323,323_0,COMPLETED,BoTorch,0.264963820549927620007224504661,100,0.250000000000000000000000000000,0.121516274124574025217171424629,4
324,324_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063133473748510374190523464222,1
325,325_0,COMPLETED,BoTorch,0.267262421611191514081440345763,100,0.050000000000000002775557561563,0.200000000000000011102230246252,1
326,326_0,COMPLETED,BoTorch,0.271025084418716866174747792684,109,0.025000000000000001387778780781,0.169987450161800446313264956189,2
327,327_0,COMPLETED,BoTorch,0.278762662807525374297767939424,169,0.100000000000000005551115123126,0.113450208912514630132939430496,3
328,328_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063237848533221438507290201869,1
329,329_0,RUNNING,BoTorch,,100,0.005000000000000000104083408559,0.026050041163762610585408197039,4
330,330_0,COMPLETED,BoTorch,0.264857694163048718039021878212,100,0.250000000000000000000000000000,0.125139381197802213030456641718,4
331,331_0,COMPLETED,BoTorch,0.268098166907863033614489722822,104,0.050000000000000002775557561563,0.156014692581839115037567466970,3
332,332_0,COMPLETED,BoTorch,0.283845875542691805648587433097,102,0.001000000000000000020816681712,0.191857993924353847914332504843,3
333,333_0,COMPLETED,BoTorch,0.264757597684515233460444960656,100,0.250000000000000000000000000000,0.191960427848695630581588034147,3
334,334_0,COMPLETED,BoTorch,0.283388808490110943871798099281,196,0.025000000000000001387778780781,0.198088906571731282468817880726,2
335,335_0,COMPLETED,BoTorch,0.287231066087795472441257516039,252,0.050000000000000002775557561563,0.164869137460948889106759907008,3
336,336_0,COMPLETED,BoTorch,0.273684273999035165836346550350,135,0.025000000000000001387778780781,0.157174280875351735087264160029,3
337,337_0,COMPLETED,BoTorch,0.278933912204534451007020834368,153,0.005000000000000000104083408559,0.149081084025102755807878907035,2
338,338_0,COMPLETED,BoTorch,0.274193198263386395119312055613,102,0.010000000000000000208166817117,0.166512542619868475490818582330,2
339,339_0,COMPLETED,BoTorch,0.272694163048721627262693800731,100,0.010000000000000000208166817117,0.161063041324750355620665231982,2
340,340_0,COMPLETED,BoTorch,0.271070911722141816291298255237,116,0.025000000000000001387778780781,0.177934761474252189916711586193,2
341,341_0,COMPLETED,BoTorch,0.282802701398938705068530907738,196,0.010000000000000000208166817117,0.068712668026650033925584182271,2
342,342_0,COMPLETED,BoTorch,0.266037144235407585490804649453,100,0.250000000000000000000000000000,0.120500228667561784523520884704,4
343,343_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063584740001787667940114090470,1
344,344_0,COMPLETED,BoTorch,0.289739507959479003496028326481,283,0.100000000000000005551115123126,0.091085626567366517747359466739,2
345,345_0,COMPLETED,BoTorch,0.265634346357935369198344233155,100,0.250000000000000000000000000000,0.120432566045628186568450246341,4
346,69_0,COMPLETED,BoTorch,0.266140858658948431525459454861,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
347,347_0,COMPLETED,BoTorch,0.265773034249879414048223225109,100,0.250000000000000000000000000000,0.123632744325315749311933188892,4
348,348_0,COMPLETED,BoTorch,0.265250844187168377530383622798,100,0.250000000000000000000000000000,0.124883035504576564433953933531,4
349,349_0,COMPLETED,BoTorch,0.319889049686444715270283722930,771,0.010000000000000000208166817117,0.021548360621025351713253570551,4
350,350_0,COMPLETED,BoTorch,0.281723347805113322195325054054,155,0.050000000000000002775557561563,0.058398526128525141132286080392,4
351,351_0,COMPLETED,BoTorch,0.265920164013507043243578209513,100,0.250000000000000000000000000000,0.127489676322350559134477521184,4
352,352_0,COMPLETED,BoTorch,0.302875060299083398440700420906,453,0.250000000000000000000000000000,0.119561869651079177856445312500,1
353,353_0,COMPLETED,BoTorch,0.265806801736613640407824732392,100,0.250000000000000000000000000000,0.122363521231052277404849348841,4
354,354_0,COMPLETED,BoTorch,0.265477568740955183201890577038,100,0.250000000000000000000000000000,0.123845343575086905274496018592,4
355,355_0,COMPLETED,BoTorch,0.266332609744331927359439760039,100,0.250000000000000000000000000000,0.122858949936550027048909328187,4
356,356_0,COMPLETED,BoTorch,0.319152194886637707860188584164,812,0.010000000000000000208166817117,0.024053327552928480126404053863,2
357,357_0,COMPLETED,BoTorch,0.313627592860588499945606599795,835,0.025000000000000001387778780781,0.126259841211140172445581697502,4
358,358_0,COMPLETED,BoTorch,0.288736131210805546665199017298,258,0.005000000000000000104083408559,0.035321528960216314052544817059,2
359,359_0,COMPLETED,BoTorch,0.265417269657501231350238413143,100,0.250000000000000000000000000000,0.121736300193025004379165920909,3
360,360_0,COMPLETED,BoTorch,0.274540520984080993471820875129,118,0.050000000000000002775557561563,0.096498756906635271834993261564,2
361,361_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063527941358953191053338116490,1
362,362_0,COMPLETED,BoTorch,0.265144717800289475562180996349,100,0.250000000000000000000000000000,0.121546971136156922721482942507,3
363,363_0,COMPLETED,BoTorch,0.264557404727448153280988663028,100,0.250000000000000000000000000000,0.121805488174018150537847304804,3
364,364_0,COMPLETED,BoTorch,0.288467197298601041310917025839,100,0.010000000000000000208166817117,0.052955123851128972634061398139,4
365,365_0,COMPLETED,BoTorch,0.285312349252291319601226859959,275,0.025000000000000001387778780781,0.014338094393113143612539239768,2
366,366_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.062952578658279648160522867784,1
367,367_0,COMPLETED,BoTorch,0.266718523878437085983250653953,100,0.250000000000000000000000000000,0.036179828689350747306097133560,3
368,368_0,COMPLETED,BoTorch,0.290048239266763152599537534115,278,0.025000000000000001387778780781,0.014138993314002668358342340582,2
369,369_0,COMPLETED,BoTorch,0.289283646888567336219466596958,279,0.025000000000000001387778780781,0.015026445603824982455432035522,2
370,370_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064318717147673828971043974434,1
371,371_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063984665438246476898420667112,1
372,372_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064131729785597960113996407472,1
373,373_0,COMPLETED,BoTorch,0.275817655571635289568632742885,124,0.010000000000000000208166817117,0.190468934062753447733484790660,3
374,374_0,COMPLETED,BoTorch,0.265461890979257097988863733917,100,0.250000000000000000000000000000,0.036387464631917450230691457591,3
375,375_0,COMPLETED,BoTorch,0.290227930535455813654266421509,101,0.010000000000000000208166817117,0.063215206101426585361124921292,4
376,376_0,COMPLETED,BoTorch,0.303468403280270138111518463120,472,0.025000000000000001387778780781,0.039686041896183771315875787877,4
377,377_0,COMPLETED,BoTorch,0.265675349734683985403194128594,100,0.250000000000000000000000000000,0.115513971526920958265982619650,3
378,378_0,COMPLETED,BoTorch,0.288247708634828736506960922270,277,0.025000000000000001387778780781,0.014054262298149836074578900025,2
379,379_0,COMPLETED,BoTorch,0.264731066087795507968394304044,100,0.250000000000000000000000000000,0.118432409826433507338272477227,3
380,380_0,COMPLETED,BoTorch,0.265693439459720237572071255272,100,0.250000000000000000000000000000,0.189627252782111455076119455043,4
381,381_0,COMPLETED,BoTorch,0.267191268692715921950764368376,100,0.250000000000000000000000000000,0.035301236167060649762117208184,3
382,382_0,COMPLETED,BoTorch,0.264576700434153377905488468969,100,0.250000000000000000000000000000,0.119108095259933766119075926326,3
383,383_0,COMPLETED,BoTorch,0.301599131693198296844116157445,415,0.010000000000000000208166817117,0.045053543032174375726484072402,3
384,384_0,COMPLETED,BoTorch,0.298589001447178037373930692411,405,0.250000000000000000000000000000,0.035485977434126139695713675337,2
385,385_0,COMPLETED,BoTorch,0.266368789194404209652589088364,100,0.250000000000000000000000000000,0.038428593979787852019658345171,3
386,386_0,COMPLETED,BoTorch,0.290342498794018299967945040407,177,0.010000000000000000208166817117,0.071972628610205910404395979185,4
387,387_0,COMPLETED,BoTorch,0.263876025084418763810845121043,100,0.250000000000000000000000000000,0.117734571342938298954550191411,3
388,388_0,COMPLETED,BoTorch,0.300711529184756409804890608939,428,0.250000000000000000000000000000,0.026728490803702098510541418364,2
389,389_0,COMPLETED,BoTorch,0.265786300048239221283097322157,100,0.250000000000000000000000000000,0.117076918030664695047349255219,3
390,390_0,RUNNING,BoTorch,,100,0.100000000000000005551115123126,0.106548122824840746369901012258,1
391,391_0,COMPLETED,BoTorch,0.279409068982151453930384832347,188,0.100000000000000005551115123126,0.132531769013446737437433853302,3
392,392_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065176816424789493775726612057,1
393,393_0,COMPLETED,BoTorch,0.276203569705740448192443636799,111,0.050000000000000002775557561563,0.085906512677011118150893764778,3
394,394_0,COMPLETED,BoTorch,0.264792571152918432275669147202,101,0.100000000000000005551115123126,0.181562152950617172741942795255,2
395,395_0,COMPLETED,BoTorch,0.271261456825856228647353418637,123,0.050000000000000002775557561563,0.116164860618951620852001838102,3
396,396_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064754373903214978991371708616,1
397,397_0,COMPLETED,BoTorch,0.319154606849975874816038867721,780,0.005000000000000000104083408559,0.139552144817559814482521574064,2
398,398_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065257522971363973729452823136,1
399,399_0,COMPLETED,BoTorch,0.311398938736131225546444056818,664,0.100000000000000005551115123126,0.095541476830840077472117854995,4
400,400_0,COMPLETED,BoTorch,0.299359623733719271143627338461,421,0.250000000000000000000000000000,0.158319316611443605680165092053,1
401,401_0,COMPLETED,BoTorch,0.264793777134587515753594288981,100,0.250000000000000000000000000000,0.116635184058910629234517841724,3
402,402_0,COMPLETED,BoTorch,0.289557404727448175485449155531,101,0.010000000000000000208166817117,0.063273788309726394785137415511,4
403,403_0,COMPLETED,BoTorch,0.321859623733719235616490550456,946,0.001000000000000000020816681712,0.159737053241990223373392154826,2
404,404_0,COMPLETED,BoTorch,0.272731548480463104056070733350,107,0.100000000000000005551115123126,0.000371037982404232025146484375,2
405,405_0,COMPLETED,BoTorch,0.294842016401350659471347626095,311,0.001000000000000000020816681712,0.072576894238591191377274469687,2
406,406_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.065408465464321260962599069444,1
407,407_0,COMPLETED,BoTorch,0.315252050168837394394927287067,766,0.005000000000000000104083408559,0.067075824715662230035562174635,1
408,408_0,RUNNING,BoTorch,,135,0.010000000000000000208166817117,0.122820258326828482542403264688,4
409,409_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.064694343375096899362297619973,1
410,410_0,COMPLETED,BoTorch,0.325395561987457782038291043136,960,0.100000000000000005551115123126,0.079440948180854328852795731564,4
411,411_0,COMPLETED,BoTorch,0.266611191509889100537122885726,100,0.250000000000000000000000000000,0.114746300444862212031260639833,3
412,412_0,COMPLETED,BoTorch,0.323230824891461598902253626875,963,0.025000000000000001387778780781,0.193529451265931129455566406250,3
413,413_0,COMPLETED,BoTorch,0.265595754944524808927042158757,100,0.250000000000000000000000000000,0.113786599035390290080016484353,3
414,414_0,COMPLETED,BoTorch,0.263726483357452967659639853082,100,0.250000000000000000000000000000,0.113997034237553529489872516933,3
415,415_0,COMPLETED,BoTorch,0.299312590448625126526849271613,379,0.050000000000000002775557561563,0.105226681753993037138350530313,2
416,416_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.066460569297477553596387167545,1
417,417_0,COMPLETED,BoTorch,0.266555716353111482597171288944,100,0.250000000000000000000000000000,0.114489330101097144809507710761,3
418,418_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063406270569913533718953146945,1
419,419_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063486220831572071077175678511,1
420,420_0,COMPLETED,BoTorch,0.283662566328991783137780657853,211,0.250000000000000000000000000000,0.111541977947689163741529227991,3
421,421_0,COMPLETED,BoTorch,0.265344910757356444719334831461,100,0.250000000000000000000000000000,0.113331590999864784130579664634,3
422,422_0,COMPLETED,BoTorch,0.265336468885672971396161301527,100,0.250000000000000000000000000000,0.113123227633923822965655858752,3
423,423_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063552364705884781259648264040,1
424,424_0,COMPLETED,BoTorch,0.283125904486251855907141816715,224,0.100000000000000005551115123126,0.101804282702726303644347183308,3
425,425_0,COMPLETED,BoTorch,0.267311866859623714631766233651,100,0.250000000000000000000000000000,0.115437169271624895383610009958,3
426,426_0,COMPLETED,BoTorch,0.281548480463096995052296733775,181,0.025000000000000001387778780781,0.068657192774116998501554576251,2
427,427_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063930818803618991164050555653,1
428,428_0,COMPLETED,BoTorch,0.298344187168355090555849074008,359,0.005000000000000000104083408559,0.048345424234867095947265625000,3
429,429_0,COMPLETED,BoTorch,0.317013989387361361238504287030,750,0.010000000000000000208166817117,0.076163237852733808863625597496,3
430,430_0,COMPLETED,BoTorch,0.266016642547033277388379701733,100,0.250000000000000000000000000000,0.075936220406138399408035866145,4
431,431_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063663841948895905087368873865,1
432,432_0,COMPLETED,BoTorch,0.280370236372407100056136641797,161,0.100000000000000005551115123126,0.014815445886267744959874370636,2
433,433_0,COMPLETED,BoTorch,0.278306801736613595998903747386,182,0.100000000000000005551115123126,0.137126265003239544704172203637,2
434,434_0,COMPLETED,BoTorch,0.266726965750120559306424183887,100,0.250000000000000000000000000000,0.041485150633278511378421171685,3
435,435_0,COMPLETED,BoTorch,0.265208634828750566825306123064,100,0.250000000000000000000000000000,0.114582156814305002878739969674,3
436,436_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063167257120466668851577196619,1
437,437_0,COMPLETED,BoTorch,0.264710564399421088843666893808,100,0.250000000000000000000000000000,0.114575279144877292814008740152,3
438,438_0,COMPLETED,BoTorch,0.308264592378195811939178838657,622,0.025000000000000001387778780781,0.170440341760796060244231853176,2
439,439_0,COMPLETED,BoTorch,0.298102990834539283149240418425,360,0.050000000000000002775557561563,0.029526027726847932364240278957,2
440,440_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.063119629126338130942208692886,1
441,441_0,COMPLETED,BoTorch,0.280414857694163077717064425087,161,0.100000000000000005551115123126,0.015398910526911450263876091071,2
442,442_0,COMPLETED,BoTorch,0.293525084418716830647611004679,307,0.250000000000000000000000000000,0.057535290872564773256847558969,4
443,443_0,FAILED,BoTorch,,165,0.050000000000000002775557561563,0.000000000000000000000000000000,1
444,444_0,COMPLETED,BoTorch,0.317969126869271589974630387587,900,0.250000000000000000000000000000,0.057907013836237267168094433600,1
445,445_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060891879156301190190170302685,1
446,446_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.061063330620698567929860445247,1
447,447_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060871664129112879049188222780,1
448,448_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.061194731653367834267598368569,1
449,449_0,COMPLETED,BoTorch,0.264134105161601517863800836494,100,0.250000000000000000000000000000,0.111357322529521560183773942754,3
450,450_0,COMPLETED,BoTorch,0.273310419681620841991787074221,134,0.250000000000000000000000000000,0.021199076569781705181583220110,2
451,451_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.060905206873586449634050410396,1
452,452_0,COMPLETED,BoTorch,0.306477327544621314103778786375,532,0.010000000000000000208166817117,0.187354802712664553210331064292,3
453,453_0,COMPLETED,BoTorch,0.323941148094548991842600571545,932,0.100000000000000005551115123126,0.092217177311222808810953210923,3
454,454_0,COMPLETED,BoTorch,0.266509889049686421458318363875,100,0.250000000000000000000000000000,0.044655957057352008154893496794,3
455,455_0,COMPLETED,BoTorch,0.264499517607332368385186782689,100,0.250000000000000000000000000000,0.110785076226966164303355810716,3
456,456_0,COMPLETED,BoTorch,0.264700916546068532042568222096,100,0.250000000000000000000000000000,0.110925927995745882137512694499,3
457,457_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058798372025838624432836354572,1
458,458_0,COMPLETED,BoTorch,0.266275928605885225941563021479,100,0.250000000000000000000000000000,0.040396779155443776621048357356,2
459,459_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058708394592289027402731704797,1
460,460_0,COMPLETED,BoTorch,0.265180897250361757855330324674,100,0.250000000000000000000000000000,0.109856149947308398551193420190,3
461,461_0,FAILED,BoTorch,,124,0.250000000000000000000000000000,0.000000000000000000000000000000,1
462,462_0,COMPLETED,BoTorch,0.265676555716353068881119270372,100,0.250000000000000000000000000000,0.037829374633336125544058603509,3
463,463_0,COMPLETED,BoTorch,0.290453449107573535847848233971,313,0.250000000000000000000000000000,0.028041718799812966056439478280,3
464,464_0,COMPLETED,BoTorch,0.269008683068017395711990502605,114,0.250000000000000000000000000000,0.032206390015410833738140894411,3
465,465_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058679409940176022186797410995,1
466,466_0,COMPLETED,BoTorch,0.280218282682103247971383552795,170,0.100000000000000005551115123126,0.029042969085276128249351046406,2
467,467_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.058572691251139154255955787676,1
468,468_0,COMPLETED,BoTorch,0.265722383019778130019972195441,100,0.250000000000000000000000000000,0.036293903523610726580272967112,3
469,469_0,COMPLETED,BoTorch,0.264505547515677785774812491582,100,0.250000000000000000000000000000,0.156324936855616380970701584374,4
470,470_0,COMPLETED,BoTorch,0.297466232513265760317722197215,381,0.050000000000000002775557561563,0.044967321678996086120605468750,3
471,471_0,COMPLETED,BoTorch,0.282545827303425034493500334065,208,0.250000000000000000000000000000,0.161883437819778930322200949377,4
472,472_0,COMPLETED,BoTorch,0.295653642064640620468196630100,355,0.025000000000000001387778780781,0.145991105391367531618485031686,4
473,473_0,COMPLETED,BoTorch,0.264684032802701363351616237196,100,0.250000000000000000000000000000,0.105578575750874356642405871298,3
474,474_0,COMPLETED,BoTorch,0.265911722141823458898102217063,100,0.250000000000000000000000000000,0.105378275374606159009438499652,3
475,475_0,COMPLETED,BoTorch,0.285851423058369524810018447170,234,0.250000000000000000000000000000,0.088126997907052362357660513226,2
476,476_0,COMPLETED,BoTorch,0.288088519054510383554656982596,243,0.250000000000000000000000000000,0.110534043763194256282389460466,2
477,477_0,COMPLETED,BoTorch,0.266122768933912179356582328182,100,0.250000000000000000000000000000,0.106792237016569063756676882804,3
478,478_0,COMPLETED,BoTorch,0.322136999517607325316248534364,919,0.001000000000000000020816681712,0.088472510671890325362198836956,1
479,479_0,COMPLETED,BoTorch,0.284956584659913136903242047993,231,0.250000000000000000000000000000,0.076124516330787636575472276945,1
480,480_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.057947177819312745139512799142,1
481,481_0,COMPLETED,BoTorch,0.311232513265798371726589266473,673,0.010000000000000000208166817117,0.123515655374345306727335014330,1
482,482_0,COMPLETED,BoTorch,0.280635552339604465998945670435,189,0.250000000000000000000000000000,0.108880662720070742199851565601,4
483,483_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.057787828021531165156865483823,1
484,484_0,COMPLETED,BoTorch,0.312425229136517157435548597277,648,0.250000000000000000000000000000,0.188148195296525960751310435626,1
485,485_0,COMPLETED,BoTorch,0.278584177520501685698661731294,164,0.100000000000000005551115123126,0.028079470062678281438994787322,2
486,486_0,COMPLETED,BoTorch,0.267276893391220404794239584589,100,0.250000000000000000000000000000,0.033764158564677522467167847253,3
487,487_0,COMPLETED,BoTorch,0.264431982633863915665983768122,100,0.250000000000000000000000000000,0.034394251726277906555662156052,3
488,488_0,COMPLETED,BoTorch,0.267597684515195388677000210009,101,0.100000000000000005551115123126,0.105557214010480085453203002999,1
489,489_0,COMPLETED,BoTorch,0.265531837916063717663917032041,100,0.250000000000000000000000000000,0.108771684097887760822587210896,3
490,490_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.056757437299702950816993052285,1
491,491_0,COMPLETED,BoTorch,0.267610950313555195911874307058,100,0.250000000000000000000000000000,0.033648978874544668848933781646,3
492,492_0,COMPLETED,BoTorch,0.275092860588519005915486559388,123,0.025000000000000001387778780781,0.124412909925848483294608115557,1
493,493_0,COMPLETED,BoTorch,0.274722624216111932504702508595,117,0.100000000000000005551115123126,0.069470607286485269726838964743,3
494,494_0,COMPLETED,BoTorch,0.266827062228654154907303563959,100,0.250000000000000000000000000000,0.034489320671504640480886649812,3
495,495_0,COMPLETED,BoTorch,0.264657501205981637859565580584,100,0.250000000000000000000000000000,0.108479984228543907542885449402,3
496,496_0,COMPLETED,BoTorch,0.264597202122527686007913416688,100,0.250000000000000000000000000000,0.108682457953691746777025173287,3
497,497_0,COMPLETED,BoTorch,0.291072117703810917532791791018,296,0.025000000000000001387778780781,0.056134262867271904340338295469,2
498,498_0,COMPLETED,BoTorch,0.318613121080559613673699459468,814,0.100000000000000005551115123126,0.117512379400432118159436356564,2
499,499_0,COMPLETED,BoTorch,0.265677761698022152359044412151,100,0.250000000000000000000000000000,0.057268722267108600199048140666,1
500,500_0,COMPLETED,BoTorch,0.270440183309213710849405742920,118,0.250000000000000000000000000000,0.078021889235570726639501515365,3
</pre>
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<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download »results.csv« as file</button>
<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> Errors</h1>
<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download »oo_errors.txt« as file</button>
<pre id='simple_pre_tab_tab_errors'><span style="background-color: black; color: white">
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2289994/2289994_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2289995/2289995_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290000/2290000_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290009/2290009_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290010/2290010_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290011/2290011_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290012/2290012_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290013/2290013_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290015/2290015_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290088/2290088_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290099/2290099_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(230, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290101/2290101_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 229 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(229, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290102/2290102_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 227 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(227, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290124/2290124_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.25 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(151, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290168/2290168_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290174/2290174_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290178/2290178_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290188/2290188_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 569 confidence 0.001 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(569, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290190/2290190_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290195/2290195_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(250, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290197/2290197_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290199/2290199_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 106 confidence 0.005 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(106, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290204/2290204_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(250, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290207/2290207_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 260 confidence 0.05 feature_proportion 0 n_clusters 2
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(260, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290209/2290209_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 212 confidence 0.025 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(212, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290212/2290212_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 249 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(249, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290214/2290214_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 268 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(268, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290217/2290217_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 261 confidence 0.01 feature_proportion 0 n_clusters 2
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(261, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290218/2290218_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 255 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(255, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290220/2290220_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 255 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(255, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290223/2290223_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(258, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290225/2290225_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290226/2290226_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 4
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(258, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290249/2290249_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 545 confidence 0.001 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(545, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290252/2290252_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 562 confidence 0.001 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(562, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290413/2290413_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 165 confidence 0.05 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(165, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_PokerHand/0/single_runs/2290590/2290590_0_log.out contains potential errors:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py PokerHand 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 124 confidence 0.25 feature_proportion 0 n_clusters 1
- Got no result.
- Non-zero exit-code detected: 1
- raise ValueError(ValueError: Found array with 0 feature(s) (shape=(124, 0)) while a minimum of 1
</span></pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download »oo_errors.txt« as file</button>
<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
1727577855,474.1484375,49.7
1727577855,474.1875,50.8
1727577855,474.23828125,49.8
1727577855,474.23828125,56.5
1727577855,474.23828125,39.4
1727577855,474.23828125,50.1
1727577855,474.23828125,39.4
1727577901,482.65234375,49.8
1727577901,482.65234375,36.4
1727577901,482.65234375,49.9
1727577901,482.65234375,40.6
1727577902,482.65234375,49.8
1727577902,482.65234375,55.3
1727577902,482.65234375,47.7
1727577902,482.65234375,57.8
1727577904,482.65234375,49.7
1727577904,482.65234375,51.0
1727577904,482.65234375,52.1
1727577904,482.65234375,44.1
1727577905,482.65234375,49.8
1727577905,482.65234375,39.4
1727577905,482.65234375,49.0
1727577905,482.65234375,57.8
1727577906,482.65234375,49.7
1727577906,482.65234375,47.6
1727577906,482.65234375,52.4
1727577906,482.65234375,38.7
1727577908,482.65234375,49.8
1727577908,482.65234375,37.1
1727577908,482.65234375,51.4
1727577908,482.65234375,51.2
1727577909,482.65234375,49.8
1727577909,482.65234375,37.1
1727577909,482.65234375,53.4
1727577909,482.65234375,37.5
1727577911,482.65234375,49.8
1727577911,482.65234375,54.3
1727577911,482.65234375,47.6
1727577911,482.65234375,59.6
1727577912,482.65234375,49.7
1727577912,482.65234375,52.4
1727577912,482.65234375,48.1
1727577912,482.65234375,55.6
1727577913,482.65234375,49.7
1727577913,482.65234375,54.3
1727577913,482.65234375,50.0
1727577913,482.65234375,56.5
1727577915,482.65234375,49.7
1727577915,482.65234375,55.6
1727577915,482.65234375,48.6
1727577915,482.65234375,57.8
1727577916,482.65234375,49.7
1727577916,482.65234375,54.3
1727577916,482.65234375,49.1
1727577916,482.65234375,58.1
1727577918,482.65234375,49.7
1727577918,482.65234375,54.3
1727577918,482.65234375,47.7
1727577918,482.65234375,40.0
1727577919,482.65234375,49.8
1727577919,482.65234375,47.5
1727577919,482.65234375,52.2
1727577919,482.65234375,38.7
1727577921,482.65234375,49.7
1727577921,482.65234375,55.3
1727577921,482.65234375,49.5
1727577921,482.65234375,55.6
1727577922,482.65234375,49.7
1727577922,482.65234375,53.1
1727577922,482.65234375,46.8
1727577922,482.65234375,57.8
1727577923,482.65234375,49.7
1727577923,482.65234375,54.2
1727577923,482.65234375,49.0
1727577923,482.65234375,56.5
1727577925,482.65234375,49.7
1727577925,482.65234375,53.2
1727577925,482.65234375,50.8
1727577925,482.65234375,42.4
1727577926,482.65234375,49.8
1727577926,482.65234375,55.3
1727577926,482.65234375,49.0
1727577926,482.65234375,39.4
1727577928,482.65234375,49.8
1727577928,482.65234375,36.4
1727577928,482.65234375,52.0
1727577928,482.65234375,56.8
1727577929,482.65234375,49.8
1727577929,482.65234375,53.2
1727577929,482.65234375,52.9
1727577929,482.65234375,38.2
1727577930,482.65234375,49.8
1727577930,482.65234375,54.3
1727577930,482.65234375,52.1
1727577930,482.65234375,42.4
1727577932,482.65234375,49.8
1727577932,482.65234375,55.3
1727577932,482.65234375,48.1
1727577932,482.65234375,57.4
1727577934,482.65234375,49.7
1727577934,482.65234375,55.3
1727577934,482.65234375,52.0
1727577934,482.65234375,38.7
1727577935,482.65234375,49.7
1727577935,482.65234375,54.2
1727577935,482.65234375,47.3
1727577935,482.65234375,53.2
1727577936,482.65234375,49.7
1727577936,482.65234375,53.2
1727577936,482.65234375,52.5
1727577936,482.65234375,40.6
1727577938,482.65234375,49.8
1727577938,482.65234375,38.2
1727577938,482.65234375,49.0
1727577938,482.65234375,57.8
1727577939,482.65234375,49.7
1727577939,482.65234375,38.2
1727577939,482.65234375,54.3
1727577939,482.65234375,37.1
1727577941,482.65234375,49.8
1727577941,482.65234375,35.3
1727577941,482.65234375,49.5
1727577941,482.65234375,56.8
1727577942,482.65234375,49.7
1727577942,482.65234375,55.3
1727577942,482.65234375,51.2
1727577942,482.65234375,40.6
1727577943,482.65234375,49.8
1727577943,482.65234375,54.3
1727577943,482.65234375,49.5
1727577943,482.65234375,55.6
1727577945,482.65234375,49.7
1727577945,482.65234375,53.2
1727577945,482.65234375,47.6
1727577945,482.65234375,56.5
1727577946,482.65234375,49.7
1727577946,482.65234375,44.7
1727577946,482.65234375,50.9
1727577946,482.65234375,51.3
1727577948,482.65234375,49.7
1727577948,482.65234375,56.5
1727577948,482.65234375,48.1
1727577948,482.65234375,55.6
1727577949,482.65234375,49.8
1727577949,482.65234375,36.4
1727577949,482.65234375,52.8
1727577949,482.65234375,46.2
1727577950,482.65234375,49.8
1727577950,482.65234375,37.1
1727577950,482.65234375,53.3
1727577950,482.65234375,44.7
1727577952,482.65234375,49.7
1727577952,482.65234375,53.2
1727577952,482.65234375,48.6
1727577952,482.65234375,55.6
1727577953,482.65234375,49.7
1727577953,482.65234375,55.3
1727577953,482.65234375,49.6
1727577953,482.65234375,51.3
1727577955,482.65234375,49.8
1727577955,482.65234375,51.0
1727577955,482.65234375,49.1
1727577955,482.65234375,54.5
1727577956,482.65234375,49.7
1727577956,482.65234375,52.1
1727577956,482.65234375,49.5
1727577956,482.65234375,53.7
1727577957,482.65234375,49.7
1727577957,482.65234375,54.3
1727577957,482.65234375,48.6
1727577957,482.65234375,56.5
1727577959,482.65234375,49.7
1727577959,482.65234375,54.3
1727577959,482.65234375,49.5
1727577959,482.65234375,40.6
1727577960,482.65234375,49.8
1727577960,482.65234375,56.3
1727577960,482.65234375,48.1
1727577960,482.65234375,56.8
1727577962,482.65234375,49.8
1727577962,482.65234375,56.2
1727577962,482.65234375,46.7
1727577962,482.65234375,54.2
1727577963,482.65234375,49.7
1727577963,482.65234375,53.2
1727577963,482.65234375,47.6
1727577963,482.65234375,57.8
1727577965,482.65234375,49.8
1727577965,482.65234375,56.5
1727577965,482.65234375,46.3
1727577965,482.65234375,56.5
1727577966,482.65234375,49.7
1727577966,482.65234375,54.2
1727577966,482.65234375,48.6
1727577966,482.65234375,55.6
1727577967,482.65234375,49.7
1727577967,482.65234375,53.2
1727577967,482.65234375,52.5
1727577967,482.65234375,39.4
1727577969,482.65234375,49.8
1727577969,482.65234375,37.1
1727577969,482.65234375,52.9
1727577969,482.65234375,45.7
1727577970,482.65234375,49.7
1727577970,482.65234375,54.2
1727577970,482.65234375,48.1
1727577970,482.65234375,55.6
1727577972,482.65234375,49.7
1727577972,482.65234375,40.0
1727577972,482.65234375,50.8
1727577972,482.65234375,54.3
1727577973,482.65234375,49.7
1727577973,482.65234375,41.7
1727577973,482.65234375,52.9
1727577973,482.65234375,39.4
1727577975,482.65234375,49.8
1727577975,482.65234375,36.4
1727577975,482.65234375,52.9
1727577975,482.65234375,40.0
1727577976,482.65234375,49.7
1727577976,482.65234375,50.0
1727577976,482.65234375,48.6
1727577976,482.65234375,54.3
1727577977,482.65234375,49.7
1727577977,482.65234375,54.2
1727577977,482.65234375,46.7
1727577977,482.65234375,57.8
1727577979,482.65234375,49.7
1727577979,482.65234375,47.5
1727577979,482.65234375,50.5
1727577979,482.65234375,51.2
1727577980,482.65234375,49.8
1727577980,482.65234375,54.3
1727577980,482.65234375,49.7
1727577980,482.65234375,36.4
1727577982,482.65234375,49.7
1727577982,482.65234375,47.5
1727577982,482.65234375,51.0
1727577982,482.65234375,55.6
1727577983,482.65234375,49.7
1727577983,482.65234375,52.1
1727577983,482.65234375,50.4
1727577983,482.65234375,57.4
1727577984,482.65234375,49.8
1727577984,482.65234375,38.2
1727577984,482.65234375,53.3
1727577984,482.65234375,37.5
1727577986,482.66015625,49.8
1727577986,482.66015625,38.2
1727577986,482.66015625,50.0
1727577986,482.66015625,56.5
1727577987,482.66015625,49.7
1727577987,482.66015625,50.0
1727577987,482.66015625,51.9
1727577987,482.66015625,37.5
1727577989,482.66015625,49.8
1727577989,482.66015625,48.8
1727577989,482.66015625,51.4
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1727578137,482.6640625,52.2
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1727578139,482.6640625,53.2
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1727578139,482.6640625,55.8
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1727578140,482.6640625,54.3
1727578140,482.6640625,49.1
1727578140,482.6640625,38.7
1727578142,482.6640625,49.8
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1727578142,482.6640625,48.6
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1727578143,482.6640625,49.7
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1727578146,482.6640625,44.4
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1727578149,482.6640625,49.7
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1727578149,482.6640625,48.1
1727578149,482.6640625,54.3
1727578150,482.6640625,49.7
1727578150,482.6640625,55.3
1727578150,482.6640625,48.1
1727578150,482.6640625,57.4
1727578151,482.6640625,49.7
1727578151,482.6640625,51.2
1727578151,482.6640625,52.9
1727578151,482.6640625,39.4
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1727578153,482.6640625,39.4
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1727578154,482.6640625,49.8
1727578154,482.6640625,54.3
1727578154,482.6640625,48.6
1727578154,482.6640625,56.5
1727578156,482.6640625,49.7
1727578156,482.6640625,53.2
1727578156,482.6640625,48.1
1727578156,482.6640625,55.6
1727578157,482.6640625,49.7
1727578157,482.6640625,41.7
1727578157,482.6640625,54.3
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1727578159,482.6640625,49.7
1727578159,482.6640625,53.2
1727578159,482.6640625,49.9
1727578159,482.6640625,53.2
1727578160,482.6640625,49.7
1727578160,482.6640625,55.1
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1727578160,482.6640625,40.6
1727578161,482.6640625,49.8
1727578161,482.6640625,54.3
1727578161,482.6640625,48.6
1727578161,482.6640625,53.2
1727578163,482.6640625,49.8
1727578163,482.6640625,43.6
1727578163,482.6640625,50.9
1727578163,482.6640625,48.6
1727578164,482.6640625,49.8
1727578164,482.6640625,36.4
1727578164,482.6640625,51.4
1727578164,482.6640625,46.3
1727578166,482.6640625,49.7
1727578166,482.6640625,53.2
1727578166,482.6640625,47.2
1727578166,482.6640625,57.4
1727578167,482.6640625,49.7
1727578167,482.6640625,56.3
1727578167,482.6640625,47.2
1727578167,482.6640625,56.8
1727578168,482.6640625,49.7
1727578168,482.6640625,56.2
1727578168,482.6640625,48.1
1727578168,482.6640625,56.8
1727578170,482.6640625,49.8
1727578170,482.6640625,38.2
1727578170,482.6640625,54.2
1727578170,482.6640625,37.5
1727578171,482.6640625,49.8
1727578171,482.6640625,38.2
1727578171,482.6640625,53.3
1727578171,482.6640625,39.4
1727578173,482.6640625,49.8
1727578173,482.6640625,36.4
1727578173,482.6640625,53.7
1727578173,482.6640625,39.4
1727578174,482.6640625,49.8
1727578174,482.6640625,36.4
1727578174,482.6640625,51.8
1727578174,482.6640625,48.7
1727578175,482.6640625,49.7
1727578175,482.6640625,54.3
1727578175,482.6640625,49.5
1727578175,482.6640625,45.9
1727578177,482.6640625,49.8
1727578177,482.6640625,36.4
1727578177,482.6640625,53.7
1727578177,482.6640625,40.6
1727578178,482.6640625,49.7
1727578178,482.6640625,53.2
1727578178,482.6640625,49.5
1727578178,482.6640625,55.6
1727578180,482.6640625,49.7
1727578180,482.6640625,38.2
1727578180,482.6640625,54.2
1727578180,482.6640625,39.4
1727578181,482.6640625,49.7
1727578181,482.6640625,54.3
1727578181,482.6640625,48.1
1727578181,482.6640625,57.4
1727578182,482.6640625,49.7
1727578182,482.6640625,54.3
1727578182,482.6640625,49.1
1727578182,482.6640625,56.8
1727578184,482.6875,49.8
1727578184,482.6875,38.2
1727578184,482.6875,54.2
1727578184,482.6875,40.6
1727578185,482.6875,49.8
1727578185,482.6875,55.3
1727578185,482.6875,48.6
1727578185,482.6875,38.7
1727578187,482.6875,49.8
1727578187,482.6875,36.4
1727578187,482.6875,53.2
1727578187,482.6875,47.4
1727578188,482.6875,49.7
1727578188,482.6875,46.2
1727578188,482.6875,49.5
1727578188,482.6875,56.5
1727578190,482.6875,49.7
1727578190,482.6875,47.4
1727578190,482.6875,51.7
1727578190,482.6875,38.7
1727578191,482.6875,49.7
1727578191,482.6875,54.3
1727578191,482.6875,48.5
1727578191,482.6875,57.8
1727578192,482.6875,49.7
1727578192,482.6875,36.4
1727578192,482.6875,52.7
1727578192,482.6875,47.4
1727578194,482.6875,49.8
1727578194,482.6875,38.2
1727578194,482.6875,54.3
1727578194,482.6875,42.4
1727578195,482.6875,49.7
1727578195,482.6875,55.6
1727578195,482.6875,48.1
1727578195,482.6875,56.8
1727578196,482.6875,49.7
1727578196,482.6875,52.0
1727578196,482.6875,48.1
1727578196,482.6875,40.6
1727578198,482.6875,49.8
1727578198,482.6875,54.3
1727578198,482.6875,49.0
1727578198,482.6875,57.4
1727578199,482.6875,49.7
1727578199,482.6875,55.3
1727578199,482.6875,48.1
1727578199,482.6875,56.5
1727578201,482.6875,49.8
1727578201,482.6875,37.1
1727578201,482.6875,53.8
1727578201,482.6875,37.5
1727578202,482.6875,49.8
1727578202,482.6875,53.2
1727578202,482.6875,46.8
1727578202,482.6875,56.5
1727578203,482.6875,49.8
1727578203,482.6875,40.0
1727578203,482.6875,54.2
1727578203,482.6875,40.6
1727578205,482.6875,49.7
1727578205,482.6875,56.5
1727578205,482.6875,49.5
1727578205,482.6875,39.4
1727578206,482.6875,49.7
1727578206,482.6875,55.3
1727578206,482.6875,49.5
1727578206,482.6875,56.5
1727578208,482.6875,49.7
1727578208,482.6875,52.1
1727578208,482.6875,49.5
1727578208,482.6875,57.8
1727578209,482.6875,49.8
1727578209,482.6875,36.4
1727578209,482.6875,53.4
1727578209,482.6875,42.4
1727578210,482.6875,49.7
1727578210,482.6875,55.6
1727578210,482.6875,47.2
1727578210,482.6875,57.8
1727578212,482.6875,49.7
1727578212,482.6875,54.3
1727578212,482.6875,50.5
1727578212,482.6875,51.3
1727578213,482.6875,49.7
1727578213,482.6875,50.0
1727578213,482.6875,50.5
1727578213,482.6875,41.9
1727578215,482.6875,49.8
1727578215,482.6875,54.3
1727578215,482.6875,49.0
1727578215,482.6875,57.8
1727578216,482.6875,49.7
1727578216,482.6875,54.3
1727578216,482.6875,48.6
1727578216,482.6875,53.2
1727578218,482.6875,49.8
1727578218,482.6875,35.3
1727578218,482.6875,54.2
1727578218,482.6875,42.4
1727578219,482.6875,49.8
1727578219,482.6875,41.2
1727578219,482.6875,49.0
1727578219,482.6875,56.8
1727578220,482.6875,49.7
1727578220,482.6875,55.3
1727578220,482.6875,50.0
1727578220,482.6875,40.6
1727578222,482.6875,49.8
1727578222,482.6875,54.3
1727578222,482.6875,48.6
1727578222,482.6875,37.5
1727578223,482.6875,49.8
1727578223,482.6875,50.0
1727578223,482.6875,49.5
1727578223,482.6875,57.8
1727578224,482.6875,49.8
1727578224,482.6875,39.4
1727578224,482.6875,53.8
1727578224,482.6875,39.4
1727578226,482.6875,49.7
1727578226,482.6875,54.3
1727578226,482.6875,50.0
1727578226,482.6875,56.8
1727578227,482.6875,49.7
1727578227,482.6875,47.6
1727578227,482.6875,48.0
1727578227,482.6875,58.1
1727578229,482.6875,49.7
1727578229,482.6875,38.2
1727578229,482.6875,54.2
1727578229,482.6875,38.7
1727578230,482.6875,49.8
1727578230,482.6875,54.2
1727578230,482.6875,50.8
1727578230,482.6875,39.4
1727578231,482.6875,49.8
1727578231,482.6875,35.3
1727578231,482.6875,53.8
1727578231,482.6875,42.4
1727578233,482.6875,49.7
1727578233,482.6875,53.1
1727578233,482.6875,49.5
1727578233,482.6875,56.8
1727578234,482.6875,49.7
1727578234,482.6875,54.3
1727578234,482.6875,48.1
1727578234,482.6875,56.5
1727578236,482.6875,49.7
1727578236,482.6875,55.3
1727578236,482.6875,49.5
1727578236,482.6875,55.6
1727578237,482.6875,49.7
1727578237,482.6875,38.2
1727578237,482.6875,50.5
1727578237,482.6875,53.5
1727578238,482.6875,49.8
1727578238,482.6875,38.2
1727578238,482.6875,53.4
1727578238,482.6875,42.4
1727578240,482.6875,49.7
1727578240,482.6875,51.0
1727578240,482.6875,50.0
1727578240,482.6875,56.8
1727578241,482.6875,49.7
1727578241,482.6875,55.6
1727578241,482.6875,49.0
1727578241,482.6875,41.9
1727578243,482.6875,49.7
1727578243,482.6875,54.3
1727578243,482.6875,49.0
1727578243,482.6875,56.5
1727578244,482.6875,49.7
1727578244,482.6875,52.1
1727578244,482.6875,50.0
1727578244,482.6875,40.6
1727578245,482.6875,49.8
1727578245,482.6875,54.2
1727578245,482.6875,49.5
1727578245,482.6875,38.7
1727578247,482.6875,49.7
1727578247,482.6875,54.3
1727578247,482.6875,48.6
1727578247,482.6875,56.5
1727578248,482.6875,49.7
1727578248,482.6875,39.4
1727578248,482.6875,52.9
1727578248,482.6875,39.4
1727578250,482.6875,49.8
1727578250,482.6875,51.0
1727578250,482.6875,47.2
1727578250,482.6875,56.5
1727578251,482.6875,49.7
1727578251,482.6875,40.5
1727578251,482.6875,52.1
1727578251,482.6875,37.5
1727578253,482.6875,49.8
1727578253,482.6875,52.1
1727578253,482.6875,49.5
1727578253,482.6875,45.7
1727578254,482.6875,49.8
1727578254,482.6875,55.3
1727578254,482.6875,49.1
1727578254,482.6875,55.6
1727578255,482.6875,49.7
1727578255,482.6875,54.3
1727578255,482.6875,48.6
1727578255,482.6875,51.3
1727578257,482.6875,49.8
1727578257,482.6875,53.2
1727578257,482.6875,48.6
1727578257,482.6875,40.6
1727578258,482.6875,49.8
1727578258,482.6875,53.2
1727578258,482.6875,49.1
1727578258,482.6875,39.4
1727578260,482.6875,49.8
1727578260,482.6875,37.5
1727578260,482.6875,53.8
1727578260,482.6875,40.6
1727578261,482.6875,49.8
1727578261,482.6875,53.5
1727578261,482.6875,49.0
1727578261,482.6875,55.6
1727578262,482.6875,49.8
1727578262,482.6875,56.5
1727578262,482.6875,48.6
1727578262,482.6875,55.6
1727578264,482.6875,49.8
1727578264,482.6875,38.2
1727578264,482.6875,49.5
1727578264,482.6875,56.8
1727578265,482.6875,49.7
1727578265,482.6875,53.2
1727578265,482.6875,50.5
1727578265,482.6875,38.7
1727578267,482.6875,49.8
1727578267,482.6875,36.4
1727578267,482.6875,49.5
1727578267,482.6875,53.5
1727578268,482.6875,49.7
1727578268,482.6875,54.3
1727578268,482.6875,45.9
1727578268,482.6875,56.5
1727578269,482.6875,49.7
1727578269,482.6875,47.4
1727578269,482.6875,52.3
1727578269,482.6875,41.9
1727578271,482.6875,49.7
1727578271,482.6875,55.3
1727578271,482.6875,47.6
1727578271,482.6875,40.6
1727578272,482.6875,49.8
1727578272,482.6875,56.5
1727578272,482.6875,48.5
1727578272,482.6875,40.6
1727578273,482.6875,49.8
1727578273,482.6875,55.6
1727578273,482.6875,47.3
1727578273,482.6875,57.1
1727578275,482.6875,49.7
1727578275,482.6875,54.3
1727578275,482.6875,49.5
1727578275,482.6875,39.4
1727578276,482.6875,49.8
1727578276,482.6875,36.4
1727578276,482.6875,54.7
1727578276,482.6875,39.4
1727578278,482.6875,49.7
1727578278,482.6875,55.3
1727578278,482.6875,48.6
1727578278,482.6875,40.6
1727578279,482.6875,49.8
1727578279,482.6875,36.4
1727578279,482.6875,53.9
1727578279,482.6875,44.1
1727578280,482.6875,49.7
1727578280,482.6875,52.1
1727578280,482.6875,49.5
1727578280,482.6875,56.8
1727578282,482.6875,49.8
1727578282,482.6875,55.3
1727578282,482.6875,49.3
1727578282,482.6875,42.4
1727578283,482.6875,49.7
1727578283,482.6875,54.3
1727578283,482.6875,48.6
1727578283,482.6875,55.6
1727578285,482.6875,49.8
1727578285,482.6875,39.4
1727578285,482.6875,52.6
1727578285,482.6875,44.4
1727578286,482.6875,49.7
1727578286,482.6875,53.3
1727578286,482.6875,49.1
1727578286,482.6875,41.2
1727578288,482.6875,49.8
1727578288,482.6875,48.8
1727578288,482.6875,51.4
1727578288,482.6875,39.4
1727578289,482.6875,49.8
1727578289,482.6875,38.2
1727578289,482.6875,52.2
1727578289,482.6875,47.2
1727578290,482.6875,49.7
1727578290,482.6875,56.5
1727578290,482.6875,47.7
1727578290,482.6875,57.8
1727578292,482.6875,49.8
1727578292,482.6875,54.3
1727578292,482.6875,50.0
1727578292,482.6875,56.8
1727578293,482.6875,49.7
1727578293,482.6875,54.3
1727578293,482.6875,49.1
1727578293,482.6875,47.4
1727578294,482.6875,49.8
1727578294,482.6875,41.2
1727578294,482.6875,54.2
1727578294,482.6875,40.6
1727578296,482.6875,49.7
1727578296,482.6875,54.3
1727578296,482.6875,49.1
1727578296,482.6875,58.7
1727578297,482.6875,49.7
1727578297,482.6875,53.3
1727578297,482.6875,49.5
1727578297,482.6875,38.7
1727578299,482.6875,49.7
1727578299,482.6875,55.3
1727578299,482.6875,47.2
1727578299,482.6875,56.5
1727578300,482.6875,49.7
1727578300,482.6875,54.3
1727578300,482.6875,49.1
1727578300,482.6875,56.8
1727578301,482.6875,49.7
1727578301,482.6875,54.3
1727578301,482.6875,48.6
1727578301,482.6875,57.4
1727578303,482.6875,49.7
1727578303,482.6875,35.3
1727578303,482.6875,52.9
1727578303,482.6875,41.2
1727578304,482.6875,49.8
1727578304,482.6875,50.0
1727578304,482.6875,50.0
1727578304,482.6875,40.6
1727578306,482.6875,49.8
1727578306,482.6875,38.9
1727578306,482.6875,54.2
1727578306,482.6875,39.4
1727578307,482.6875,49.8
1727578307,482.6875,38.2
1727578307,482.6875,54.7
1727578307,482.6875,42.4
1727578308,482.6875,49.7
1727578308,482.6875,55.6
1727578308,482.6875,48.1
1727578308,482.6875,57.8
1727578310,482.6875,49.8
1727578310,482.6875,39.4
1727578310,482.6875,48.1
1727578310,482.6875,55.6
1727578311,482.703125,49.7
1727578311,482.703125,56.3
1727578311,482.703125,48.1
1727578311,482.703125,55.6
1727578313,482.703125,49.8
1727578313,482.703125,55.3
1727578313,482.703125,49.5
1727578313,482.703125,55.6
1727578314,482.703125,49.7
1727578314,482.703125,54.3
1727578314,482.703125,50.5
1727578314,482.703125,54.8
1727578316,482.703125,49.7
1727578316,482.703125,54.3
1727578316,482.703125,47.7
1727578316,482.703125,56.8
1727578317,482.703125,49.7
1727578317,482.703125,41.7
1727578317,482.703125,52.5
1727578317,482.703125,39.4
1727578318,482.703125,49.7
1727578318,482.703125,57.4
1727578318,482.703125,47.7
1727578318,482.703125,55.6
1727578320,482.703125,49.8
1727578320,482.703125,38.2
1727578320,482.703125,54.2
1727578320,482.703125,39.4
1727578321,482.703125,49.8
1727578321,482.703125,36.4
1727578321,482.703125,50.5
1727578321,482.703125,55.6
1727578323,482.703125,49.8
1727578323,482.703125,56.5
1727578323,482.703125,48.6
1727578323,482.703125,57.8
1727578324,482.703125,49.8
1727578324,482.703125,38.2
1727578324,482.703125,49.0
1727578324,482.703125,56.8
1727578325,482.703125,49.7
1727578325,482.703125,46.2
1727578325,482.703125,51.7
1727578325,482.703125,39.4
1727578327,482.703125,49.7
1727578327,482.703125,54.2
1727578327,482.703125,50.0
1727578327,482.703125,55.6
1727578328,482.703125,49.7
1727578328,482.703125,38.2
1727578328,482.703125,53.8
1727578328,482.703125,40.6
1727578329,482.703125,49.7
1727578330,482.703125,42.5
1727578330,482.703125,50.5
1727578330,482.703125,56.8
1727578331,482.703125,49.7
1727578331,482.703125,56.5
1727578331,482.703125,49.0
1727578331,482.703125,55.3
1727578332,482.703125,49.7
1727578332,482.703125,38.9
1727578332,482.703125,52.9
1727578332,482.703125,39.4
1727578334,482.703125,49.8
1727578334,482.703125,55.6
1727578334,482.703125,48.6
1727578334,482.703125,56.5
1727578335,482.703125,49.7
1727578335,482.703125,52.9
1727578335,482.703125,50.0
1727578335,482.703125,51.3
1727578336,482.703125,49.8
1727578336,482.703125,44.4
1727578336,482.703125,49.5
1727578336,482.703125,55.6
1727578338,482.703125,49.7
1727578338,482.703125,55.6
1727578338,482.703125,49.5
1727578338,482.703125,56.5
1727578339,482.703125,49.7
1727578339,482.703125,53.2
1727578339,482.703125,47.3
1727578339,482.703125,54.8
1727578341,482.703125,49.7
1727578341,482.703125,43.2
1727578341,482.703125,50.0
1727578341,482.703125,51.3
1727578342,482.703125,49.7
1727578342,482.703125,55.3
1727578342,482.703125,48.1
1727578342,482.703125,57.4
1727578343,482.703125,49.7
1727578343,482.703125,55.6
1727578343,482.703125,48.6
1727578343,482.703125,56.8
1727578345,482.703125,49.8
1727578345,482.703125,38.2
1727578345,482.703125,49.5
1727578345,482.703125,56.8
1727578346,482.703125,49.7
1727578346,482.703125,55.3
1727578346,482.703125,48.5
1727578346,482.703125,57.8
1727578348,482.703125,49.7
1727578348,482.703125,55.6
1727578348,482.703125,48.1
1727578348,482.703125,47.2
1727578349,482.703125,49.7
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1727578510,483.53515625,59.1
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1727578512,483.53515625,40.6
1727578515,483.53515625,49.9
1727578515,483.53515625,53.2
1727578515,483.53515625,47.3
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1727578517,483.53515625,49.9
1727578517,483.53515625,51.2
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1727578519,483.68359375,49.9
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1727578521,483.68359375,42.9
1727578521,483.68359375,52.8
1727578521,483.68359375,34.3
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1727578526,483.68359375,49.9
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1727578526,483.68359375,47.2
1727578526,483.68359375,55.1
1727578528,483.68359375,49.9
1727578528,483.68359375,56.5
1727578528,483.68359375,51.3
1727578528,483.68359375,42.9
1727578530,483.6875,49.9
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1727578530,483.6875,45.9
1727578530,483.6875,57.8
1727578533,483.6875,49.9
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1727578535,483.6875,49.8
1727578535,483.6875,52.1
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1727578538,483.6875,49.9
1727578538,483.6875,55.3
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1727578540,483.6875,49.9
1727578540,483.6875,54.3
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1727578540,483.6875,48.6
1727578542,483.6875,49.9
1727578542,483.6875,55.3
1727578542,483.6875,46.8
1727578542,483.6875,55.6
1727578545,483.76953125,49.9
1727578545,483.76953125,56.5
1727578545,483.76953125,46.3
1727578545,483.76953125,57.8
1727578547,483.76953125,49.9
1727578547,483.76953125,37.5
1727578547,483.76953125,51.2
1727578547,483.76953125,57.8
1727578549,483.76953125,49.9
1727578549,483.76953125,47.5
1727578549,483.76953125,50.0
1727578549,483.76953125,54.8
1727578551,483.76953125,49.9
1727578551,483.76953125,56.2
1727578551,483.76953125,45.9
1727578551,483.76953125,57.8
1727578554,483.76953125,49.9
1727578554,483.76953125,47.6
1727578554,483.76953125,51.6
1727578554,483.76953125,55.6
1727578557,483.76953125,49.9
1727578557,483.76953125,39.4
1727578557,483.76953125,51.6
1727578557,483.76953125,40.6
1727578560,483.76953125,49.9
1727578560,483.76953125,56.5
1727578560,483.76953125,46.9
1727578560,483.76953125,54.5
1727578562,483.76953125,49.9
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1727578634,483.96875,49.9
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1727578634,483.96875,56.8
1727578636,483.96875,49.9
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1727578636,483.96875,49.6
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1727578638,483.96875,49.9
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1727578796,484.0546875,41.7
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1727578796,484.0546875,56.8
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1727578808,484.0546875,38.2
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1727578813,484.0546875,50.9
1727578813,484.0546875,38.7
1727578815,484.0546875,49.9
1727578815,484.0546875,35.3
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1727578815,484.0546875,57.8
1727578817,484.0546875,49.9
1727578817,484.0546875,54.3
1727578817,484.0546875,49.7
1727578817,484.0546875,42.4
1727578819,484.0546875,49.9
1727578819,484.0546875,53.2
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1727578819,484.0546875,55.6
1727578821,484.0546875,49.9
1727578821,484.0546875,36.4
1727578821,484.0546875,52.6
1727578821,484.0546875,42.4
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1727578823,484.0546875,48.8
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1727578826,484.0546875,49.9
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1727581438,541.4609375,37.5
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1727581459,541.4609375,56.5
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1727581459,541.4609375,44.4
1727581461,541.4609375,49.9
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1727581463,541.4609375,45.9
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1727581463,541.4609375,48.6
1727581465,541.4609375,49.9
1727581465,541.4609375,55.3
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1727581465,541.4609375,40.6
1727581468,541.4609375,49.9
1727581468,541.4609375,55.3
1727581468,541.4609375,50.4
1727581468,541.4609375,55.3
1727581470,541.4609375,49.9
1727581470,541.4609375,54.3
1727581470,541.4609375,49.8
1727581470,541.4609375,55.6
1727581472,541.4609375,49.9
1727581472,541.4609375,43.2
1727581472,541.4609375,50.0
1727581472,541.4609375,55.6
1727581474,541.4609375,49.9
1727581474,541.4609375,55.3
1727581474,541.4609375,49.1
1727581474,541.4609375,55.6
1727581477,541.4609375,49.9
1727581477,541.4609375,53.2
1727581477,541.4609375,49.3
1727581477,541.4609375,57.8
1727581479,541.4609375,49.8
1727581479,541.4609375,54.2
1727581479,541.4609375,49.3
1727581479,541.4609375,55.3
1727581483,541.50390625,49.9
1727581483,541.50390625,45.9
1727581483,541.50390625,51.1
1727581483,541.50390625,38.7
1727581502,542.1640625,50.2
1727581502,542.1640625,53.1
1727581502,542.1640625,49.1
1727581502,542.1640625,48.7
1727581524,542.078125,50.2
1727581524,542.078125,47.5
1727581524,542.078125,49.5
1727581524,542.078125,55.6
1727581526,542.078125,49.9
1727581526,542.078125,46.2
1727581526,542.078125,49.1
1727581526,542.078125,56.5
1727581528,542.078125,49.9
1727581528,542.078125,55.3
1727581528,542.078125,50.0
1727581528,542.078125,37.5
1727581530,542.078125,49.9
1727581530,542.078125,36.4
1727581531,542.078125,50.0
1727581531,542.078125,55.6
1727581533,542.078125,49.9
1727581533,542.078125,53.3
1727581533,542.078125,49.8
1727581533,542.078125,40.6
1727581535,542.078125,49.9
1727581535,542.078125,38.2
1727581535,542.078125,51.7
1727581535,542.078125,38.7
1727581537,542.078125,49.9
1727581537,542.078125,37.5
1727581537,542.078125,50.8
1727581537,542.078125,39.4
1727581540,542.078125,49.9
1727581540,542.078125,36.4
1727581540,542.078125,51.0
1727581540,542.078125,47.4
1727581542,542.078125,49.9
1727581542,542.078125,55.3
1727581542,542.078125,49.8
1727581542,542.078125,41.2
1727581544,542.078125,49.9
1727581544,542.078125,38.2
1727581544,542.078125,51.3
1727581544,542.078125,39.4
1727581547,542.09765625,49.9
1727581547,542.09765625,39.4
1727581547,542.09765625,50.4
1727581547,542.09765625,55.6
1727581549,542.09765625,49.9
1727581549,542.09765625,53.2
1727581549,542.09765625,50.0
1727581549,542.09765625,53.5
1727581551,542.09765625,49.9
1727581551,542.09765625,55.6
1727581551,542.09765625,49.5
1727581551,542.09765625,56.5
1727581554,542.09765625,49.9
1727581554,542.09765625,55.3
1727581554,542.09765625,49.8
1727581554,542.09765625,52.4
1727581556,542.09765625,49.9
1727581556,542.09765625,40.5
1727581556,542.09765625,51.3
1727581556,542.09765625,38.2
1727581558,542.09765625,49.9
1727581558,542.09765625,39.4
1727581558,542.09765625,49.7
1727581558,542.09765625,55.8
1727581561,542.09765625,49.9
1727581561,542.09765625,54.3
1727581561,542.09765625,49.6
1727581561,542.09765625,56.5
1727581563,541.88671875,49.9
1727581563,541.88671875,51.2
1727581563,541.88671875,49.8
1727581563,541.88671875,57.8
1727581565,541.1171875,49.9
1727581565,541.1171875,38.2
1727581565,541.1171875,51.3
1727581565,541.1171875,38.7
1727581567,541.88671875,49.9
1727581567,541.88671875,54.3
1727581567,541.88671875,49.6
1727581567,541.88671875,52.3
1727581570,541.88671875,49.9
1727581570,541.88671875,37.1
1727581570,541.88671875,51.5
1727581570,541.88671875,37.5
1727581572,541.12109375,49.9
1727581572,541.12109375,35.3
1727581572,541.12109375,50.7
1727581572,541.12109375,56.8
1727581574,541.1171875,49.9
1727581574,541.1171875,56.5
1727581574,541.1171875,49.5
1727581574,541.1171875,57.8
1727581578,541.12109375,49.9
1727581578,541.12109375,57.4
1727581578,541.12109375,48.7
1727581578,541.12109375,51.2
1727581600,534.94921875,50.2
1727581600,534.94921875,54.2
1727581600,534.94921875,50.0
1727581600,534.94921875,39.4
1727581624,541.65625,50.2
1727581624,541.65625,55.3
1727581624,541.65625,49.0
1727581624,541.65625,57.8
1727581626,541.6484375,49.8
1727581626,541.6484375,55.3
1727581626,541.6484375,49.6
1727581626,541.6484375,39.4
1727581628,541.66015625,49.9
1727581628,541.66015625,38.2
1727581628,541.66015625,49.8
1727581628,541.66015625,54.3
1727581631,541.59765625,49.8
1727581631,541.59765625,55.3
1727581631,541.59765625,49.8
1727581631,541.59765625,50.0
1727581634,541.671875,49.9
1727581634,541.671875,50.0
1727581634,541.671875,49.8
1727581634,541.671875,51.2
1727581636,541.66796875,49.9
1727581636,541.66796875,44.7
1727581636,541.66796875,51.0
1727581636,541.66796875,37.5
1727581638,541.65234375,49.9
1727581638,541.65234375,54.3
1727581638,541.65234375,49.4
1727581638,541.65234375,55.6
1727581641,541.65234375,49.9
1727581641,541.65234375,53.2
1727581641,541.65234375,49.1
1727581641,541.65234375,56.5
1727581643,541.65625,49.9
1727581643,541.65625,56.5
1727581643,541.65625,49.8
1727581643,541.65625,56.8
1727581645,541.65625,49.9
1727581645,541.65625,38.2
1727581645,541.65625,51.3
1727581645,541.65625,40.6
1727581648,541.55078125,49.9
1727581648,541.55078125,50.0
1727581648,541.55078125,49.0
1727581648,541.55078125,58.7
1727581650,541.67578125,49.9
1727581650,541.67578125,54.3
1727581650,541.67578125,49.1
1727581650,541.67578125,56.5
1727581652,541.671875,49.9
1727581652,541.671875,53.2
1727581652,541.671875,49.7
1727581652,541.671875,53.5
1727581655,541.65625,49.9
1727581655,541.65625,53.1
1727581655,541.65625,49.3
1727581655,541.65625,50.0
1727581657,541.65625,49.9
1727581657,541.65625,54.3
1727581657,541.65625,48.6
1727581657,541.65625,56.8
1727581659,541.6328125,49.9
1727581659,541.6328125,56.5
1727581659,541.6328125,49.7
1727581659,541.6328125,55.6
1727581662,541.65234375,49.9
1727581662,541.65234375,56.5
1727581662,541.65234375,49.5
1727581662,541.65234375,56.5
1727581665,541.66015625,49.9
1727581665,541.66015625,35.3
1727581665,541.66015625,50.8
1727581665,541.66015625,52.5
1727581684,541.69140625,50.2
1727581684,541.69140625,53.2
1727581684,541.69140625,49.3
1727581684,541.69140625,57.4
1727581686,541.6640625,49.9
1727581686,541.6640625,56.5
1727581686,541.6640625,49.5
1727581686,541.6640625,45.9
1727581689,541.671875,49.9
1727581689,541.671875,37.5
1727581689,541.671875,50.4
1727581689,541.671875,56.8
1727581691,542.66796875,49.9
1727581691,542.66796875,54.3
1727581691,542.66796875,50.4
1727581691,542.66796875,38.7
1727581693,542.66796875,49.9
1727581693,542.66796875,55.3
1727581693,542.66796875,49.8
1727581693,542.66796875,40.6
1727581696,542.453125,49.9
1727581696,542.453125,43.2
1727581696,542.453125,49.8
1727581696,542.453125,55.6
1727581698,542.453125,49.9
1727581698,542.453125,55.6
1727581698,542.453125,49.1
1727581698,542.453125,57.8
1727581700,542.453125,49.9
1727581700,542.453125,50.0
1727581700,542.453125,48.7
1727581700,542.453125,55.6
1727581703,542.4609375,49.9
1727581703,542.4609375,55.3
1727581703,542.4609375,49.0
1727581703,542.4609375,57.8
1727581705,542.4609375,49.9
1727581705,542.4609375,46.3
1727581705,542.4609375,50.0
1727581705,542.4609375,53.3
1727581708,541.6796875,49.9
1727581708,541.6796875,38.9
1727581708,541.6796875,50.7
1727581708,541.6796875,50.0
1727581710,541.67578125,49.9
1727581710,541.67578125,36.4
1727581710,541.67578125,50.3
1727581710,541.67578125,53.7
1727581712,541.62890625,49.9
1727581712,541.62890625,57.4
1727581712,541.62890625,50.2
1727581712,541.62890625,37.5
1727581715,541.68359375,49.9
1727581715,541.68359375,38.9
1727581715,541.68359375,49.6
1727581715,541.68359375,52.0
1727581717,541.67578125,49.9
1727581717,541.67578125,55.6
1727581717,541.67578125,49.1
1727581717,541.67578125,55.8
1727581719,542.4453125,49.9
1727581719,542.4453125,47.5
1727581719,542.4453125,49.6
1727581719,542.4453125,56.8
1727581722,542.19921875,49.9
1727581722,542.19921875,36.4
1727581722,542.19921875,50.9
1727581722,542.19921875,48.6
1727581724,491.9375,49.9
1727581724,492.19140625,45.0
1727581724,492.19140625,51.0
1727581724,492.19140625,42.4
1727581726,492.19140625,49.9
1727581726,492.19140625,54.3
1727581726,492.19140625,50.0
1727581726,492.19140625,40.6
1727581729,482.0390625,49.9
1727581729,482.0390625,54.3
1727581729,482.0390625,50.5
1727581729,482.0390625,40.6
1727581731,482.0390625,49.9
1727581731,482.0390625,55.6
1727581731,482.0390625,49.8
1727581731,482.0390625,37.5
1727581733,482.0390625,49.9
1727581733,482.0390625,54.2
1727581733,482.0390625,50.2
1727581733,482.0390625,55.6
1727581758,441.47265625,50.1
1727581758,441.47265625,49.9
1727581758,441.47265625,49.6
1727581758,441.47265625,52.0
1727581781,437.3359375,50.2
1727581781,437.3359375,55.3
1727581781,437.3359375,49.0
1727581781,437.3359375,56.8
1727581783,437.3359375,49.9
1727581783,437.3359375,41.2
1727581783,437.3359375,51.2
1727581783,437.3359375,40.6
1727581787,437.3359375,49.9
1727581787,437.3359375,54.3
1727581787,437.3359375,49.4
1727581787,437.3359375,58.7
1727581810,441.7890625,50.2
1727581810,441.7890625,37.1
1727581810,441.7890625,50.3
1727581810,441.7890625,55.6
1727581844,435.3203125,50.2
1727581844,435.3203125,46.2
1727581844,435.3203125,50.8
1727581844,435.3203125,44.1
1727581880,441.12109375,50.2
1727581880,441.12109375,54.2
1727581880,441.12109375,49.1
1727581880,441.12109375,56.8
1727581911,435.44140625,50.2
1727581911,435.44140625,54.3
1727581911,435.44140625,49.5
1727581911,435.44140625,56.8
1727581914,435.44140625,49.9
1727581914,435.44140625,51.0
1727581914,435.44140625,50.8
1727581914,435.44140625,40.6
1727581917,435.44140625,49.9
1727581917,435.44140625,52.3
1727581917,435.44140625,50.3
1727581917,435.44140625,42.9
1727581938,436.0234375,50.1
1727581938,436.0234375,56.2
1727581938,436.0234375,50.8
1727581938,436.0234375,39.4
1727581957,435.54296875,50.2
1727581957,435.54296875,54.2
1727581957,435.54296875,48.4
1727581957,435.54296875,55.6
1727581960,434.76953125,49.9
1727581960,434.76953125,54.3
1727581960,434.76953125,50.3
1727581960,434.76953125,42.4
1727581962,434.76953125,49.9
1727581962,434.76953125,40.0
1727581962,434.76953125,49.9
1727581962,434.76953125,55.6
1727581965,435.5390625,49.9
1727581965,435.5390625,54.3
1727581965,435.5390625,50.0
1727581965,435.5390625,40.6
1727581967,435.53125,49.9
1727581967,435.53125,36.4
1727581967,435.53125,50.5
1727581967,435.53125,48.7
1727581970,434.7734375,49.9
1727581970,434.7734375,53.1
1727581970,434.7734375,51.0
1727581970,434.7734375,40.6
1727581972,434.77734375,49.9
1727581972,434.77734375,55.3
1727581972,434.77734375,48.1
1727581972,434.77734375,56.8
1727581975,434.78125,49.9
1727581975,434.78125,44.7
1727581975,434.78125,50.8
1727581975,434.78125,40.6
1727581977,434.77734375,49.9
1727581977,434.77734375,55.3
1727581977,434.77734375,49.0
1727581977,434.77734375,55.6
1727581980,434.78125,49.9
1727581980,434.78125,50.0
1727581980,434.78125,49.7
1727581980,434.78125,56.8
1727581982,434.78125,49.9
1727581982,434.78125,38.2
1727581982,434.78125,51.4
1727581982,434.78125,44.4
1727581985,434.78125,49.9
1727581985,434.78125,38.2
1727581985,434.78125,49.5
1727581985,434.78125,56.8
1727581987,434.77734375,49.9
1727581987,434.77734375,48.8
1727581987,434.77734375,50.0
1727581987,434.77734375,55.6
1727581990,434.765625,49.9
1727581990,434.765625,55.3
1727581990,434.765625,50.7
1727581990,434.765625,37.5
1727581992,434.76953125,49.9
1727581992,434.76953125,52.0
1727581992,434.76953125,50.2
1727581992,434.76953125,55.6
1727581995,434.78125,49.9
1727581995,434.78125,54.2
1727581995,434.78125,49.0
1727581995,434.78125,55.6
1727581998,434.78515625,49.9
1727581998,434.78515625,36.4
1727581998,434.78515625,50.9
1727581998,434.78515625,57.8
1727582000,434.78515625,49.9
1727582000,434.78515625,53.2
1727582000,434.78515625,49.1
1727582000,434.78515625,55.6
1727582003,434.7890625,49.9
1727582003,434.7890625,55.6
1727582003,434.7890625,48.3
1727582003,434.7890625,56.8
1727582006,435.90234375,49.9
1727582006,435.90234375,36.4
1727582006,435.90234375,50.7
1727582006,435.90234375,57.4
1727582024,434.26171875,50.1
1727582024,434.26171875,43.6
1727582024,434.26171875,50.3
1727582024,434.26171875,50.0
1727582027,434.2265625,49.9
1727582027,434.2265625,54.3
1727582027,434.2265625,49.7
1727582027,434.2265625,56.8
1727582029,434.2734375,49.9
1727582029,434.2734375,38.2
1727582029,434.2734375,52.0
1727582029,434.2734375,39.4
1727582032,434.26171875,49.9
1727582032,434.26171875,38.2
1727582032,434.26171875,51.3
1727582032,434.26171875,39.4
1727582035,434.26171875,49.9
1727582035,434.26171875,56.2
1727582035,434.26171875,49.7
1727582035,434.26171875,38.7
1727582038,435.40234375,49.9
1727582038,435.40234375,36.4
1727582038,435.40234375,50.5
1727582038,435.40234375,56.5
1727582055,442.98046875,50.1
1727582055,442.98046875,51.2
1727582055,442.98046875,49.0
1727582055,442.98046875,56.5
1727582058,442.86328125,49.9
1727582058,442.86328125,57.4
1727582058,442.86328125,48.8
1727582058,442.86328125,53.7
1727582060,442.9609375,49.9
1727582060,442.9609375,48.8
1727582060,442.9609375,49.7
1727582060,442.9609375,57.4
1727582063,442.91796875,49.9
1727582063,442.91796875,54.3
1727582063,442.91796875,50.6
1727582063,442.91796875,38.7
1727582065,442.95703125,49.9
1727582065,442.95703125,55.3
1727582065,442.95703125,48.8
1727582065,442.95703125,56.5
1727582068,442.95703125,49.9
1727582068,442.95703125,54.3
1727582068,442.95703125,50.7
1727582068,442.95703125,39.4
1727582070,442.96484375,49.9
1727582070,442.96484375,54.3
1727582070,442.96484375,49.3
1727582070,442.96484375,55.6
1727582074,442.96484375,49.9
1727582074,442.96484375,54.3
1727582074,442.96484375,49.1
1727582074,442.96484375,45.7
1727582093,441.640625,50.2
1727582093,441.640625,54.3
1727582093,441.640625,48.6
1727582093,441.640625,55.6
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1727582830,450.6953125,49.9
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1727590864,436.078125,49.9
1727590864,436.078125,51.3
1727590973,440.9296875,50.2
1727590973,440.9296875,55.3
1727590973,440.9296875,49.7
1727590973,440.9296875,55.6
1727591121,442.13671875,50.2
1727591121,442.13671875,54.3
1727591121,442.13671875,49.3
1727591121,442.13671875,55.6
1727591357,448.7734375,50.2
1727591357,448.7734375,56.3
1727591357,448.7734375,49.1
1727591357,448.7734375,55.6
1727591603,452.53125,50.2
1727591603,452.53125,54.2
1727591603,452.53125,50.1
1727591603,452.53125,54.3
1727591696,448.55859375,50.2
1727591696,448.55859375,53.2
1727591696,448.55859375,49.3
1727591696,448.55859375,55.6
1727591776,457.265625,50.1
1727591776,457.265625,53.2
1727591776,457.265625,50.3
1727591776,457.265625,38.7
1727591831,452.57421875,50.1
1727591831,452.57421875,54.2
1727591831,452.57421875,49.6
1727591831,452.57421875,57.4
1727591880,451.30859375,50.1
1727591880,451.30859375,51.1
1727591880,451.30859375,50.0
1727591880,451.30859375,41.2
1727591895,451.234375,50.0
1727591895,451.234375,56.5
1727591895,451.234375,48.9
1727591895,451.234375,56.5
1727591950,443.109375,50.1
1727591950,443.109375,56.5
1727591950,443.109375,49.4
1727591950,443.109375,56.8
1727591965,443.1328125,50.0
1727591965,443.1328125,38.2
1727591965,443.1328125,50.7
1727591965,443.1328125,42.9
1727591979,443.0546875,50.0
1727591979,443.0546875,36.4
1727591979,443.0546875,50.7
1727591979,443.0546875,39.4
1727591993,443.06640625,50.0
1727591993,443.06640625,55.6
1727591993,443.06640625,49.4
1727591993,443.06640625,53.7
1727592007,443.07421875,50.0
1727592007,443.07421875,55.3
1727592007,443.07421875,49.9
1727592007,443.07421875,54.3
1727592058,444.421875,50.1
1727592058,444.421875,36.4
1727592058,444.421875,50.4
1727592058,444.421875,53.5
1727592072,444.36328125,50.0
1727592072,444.36328125,44.7
1727592072,444.36328125,49.8
1727592072,444.36328125,55.6
1727592137,442.28125,50.1
1727592137,442.28125,53.2
1727592137,442.28125,49.9
1727592137,442.28125,39.4
1727592231,451.24609375,50.2
1727592231,451.24609375,55.3
1727592231,451.24609375,49.8
1727592231,451.24609375,47.5
1727592338,452.28515625,50.2
1727592338,452.28515625,53.2
1727592338,452.28515625,49.1
1727592338,452.28515625,56.8
1727592395,447.97265625,50.1
1727592395,447.97265625,38.2
1727592395,447.97265625,50.3
1727592395,447.97265625,57.8
1727592410,446.95703125,50.0
1727592410,446.95703125,54.3
1727592410,446.95703125,49.8
1727592410,446.95703125,52.3
1727592428,447.06640625,50.0
1727592428,447.06640625,56.5
1727592428,447.06640625,49.0
1727592428,447.06640625,56.5
1727592542,452.515625,50.2
1727592542,452.515625,37.1
1727592542,452.515625,51.0
1727592543,452.515625,38.7
1727592825,453.2109375,50.2
1727592825,453.2109375,56.5
1727592825,453.2109375,49.6
1727592825,453.2109375,53.5
1727593068,456.5625,50.2
1727593068,456.5625,54.3
1727593068,456.5625,49.8
1727593068,456.5625,37.5
1727593263,460.00390625,50.2
1727593263,460.00390625,55.3
1727593263,460.00390625,50.5
1727593263,460.00390625,44.4
1727593364,452.0546875,50.2
1727593364,452.0546875,54.3
1727593364,452.0546875,49.8
1727593364,452.0546875,56.5
1727593424,450.52734375,50.2
1727593424,450.52734375,55.6
1727593424,450.52734375,50.3
1727593424,450.52734375,39.4
1727593439,450.296875,50.0
1727593439,450.296875,55.1
1727593439,450.296875,48.9
1727593439,450.296875,55.6
1727593455,449.80859375,50.0
1727593455,449.80859375,53.2
1727593455,449.80859375,49.5
1727593455,449.80859375,55.3
1727593474,449.90234375,50.0
1727593474,449.90234375,54.2
1727593474,449.90234375,50.2
1727593474,449.90234375,48.7
1727593556,450.89453125,50.2
1727593556,450.89453125,51.1
1727593556,450.89453125,49.9
1727593556,450.89453125,56.5
1727593780,456.87109375,50.2
1727593780,456.87109375,38.2
1727593780,456.87109375,50.4
1727593780,456.87109375,45.9
1727594052,465.28125,50.2
1727594052,465.28125,56.0
1727594052,465.28125,49.0
1727594052,465.28125,55.8
1727594400,461.546875,50.2
1727594400,461.546875,50.0
1727594400,461.546875,49.7
1727594400,461.546875,56.5
1727594562,466.62890625,50.2
1727594562,466.62890625,54.3
1727594562,466.62890625,49.6
1727594562,466.62890625,55.6
1727594678,465.796875,50.2
1727594678,465.796875,53.2
1727594678,465.796875,50.0
1727594678,465.796875,39.4
1727594797,464.32421875,50.2
1727594797,464.32421875,54.3
1727594797,464.32421875,50.3
1727594797,464.32421875,40.0
1727594954,467.140625,50.2
1727594954,467.140625,54.3
1727594954,467.140625,50.5
1727594954,467.140625,42.4
1727595035,458.01171875,50.1
1727595035,458.01171875,38.2
1727595035,458.01171875,51.0
1727595035,458.01171875,37.5
1727595294,466.8125,50.2
1727595294,466.8125,51.0
1727595294,466.8125,50.7
1727595294,466.8125,41.9
1727595462,461.19921875,50.2
1727595462,461.19921875,55.3
1727595462,461.19921875,49.6
1727595462,461.19921875,55.6
1727595613,462.7578125,50.2
1727595613,462.7578125,44.7
1727595613,462.7578125,50.7
1727595613,462.7578125,39.4
1727595740,459.08984375,50.2
1727595740,459.08984375,55.6
1727595740,459.08984375,49.7
1727595740,459.08984375,57.8
1727595957,463.29296875,50.2
1727595957,463.29296875,55.3
1727595957,463.29296875,50.3
1727595957,463.29296875,41.2
1727596077,471.34375,50.2
1727596077,471.34375,52.1
1727596077,471.34375,49.7
1727596077,471.34375,55.6
1727596205,471.265625,50.2
1727596205,471.265625,39.4
1727596205,471.265625,49.8
1727596205,471.265625,58.7
1727596323,463.76953125,50.2
1727596323,463.76953125,53.2
1727596323,463.76953125,49.5
1727596323,463.76953125,57.8
1727596462,479.45703125,50.2
1727596462,479.45703125,54.3
1727596462,479.45703125,50.3
1727596462,479.45703125,38.7
1727596652,467.875,50.2
1727596652,467.875,56.3
1727596652,467.875,49.4
1727596652,467.875,55.6
1727596958,471.84765625,50.2
1727596958,471.84765625,38.2
1727596958,471.84765625,50.9
1727596958,471.84765625,39.4
1727597235,470.67578125,50.2
1727597235,470.67578125,38.2
1727597235,470.67578125,51.0
1727597235,470.67578125,38.2
1727597522,471.1640625,50.2
1727597522,471.1640625,52.1
1727597522,471.1640625,50.5
1727597522,471.1640625,38.7
1727597696,469.875,50.2
1727597696,469.875,54.3
1727597696,469.875,49.5
1727597696,469.875,56.8
1727598011,472.375,50.2
1727598011,472.375,36.4
1727598011,472.375,50.4
1727598011,472.375,55.6
1727598416,478.5859375,50.2
1727598416,478.5859375,54.3
1727598416,478.5859375,50.1
1727598416,478.5859375,39.4
1727598749,482.46484375,50.2
1727598749,482.46484375,35.3
1727598749,482.46484375,50.0
1727598749,482.46484375,57.8
1727599088,475.58984375,50.2
1727599088,475.58984375,55.1
1727599088,475.58984375,48.9
1727599088,475.58984375,57.8
1727599350,487.2578125,50.2
1727599350,487.2578125,40.0
1727599350,487.2578125,50.6
1727599350,487.2578125,39.4
1727599629,481.5234375,50.2
1727599629,481.5234375,53.2
1727599629,481.5234375,50.0
1727599629,481.5234375,41.9
1727599917,489.4140625,50.2
1727599917,489.4140625,55.3
1727599917,489.4140625,49.4
1727599917,489.4140625,57.8
1727600188,493.88671875,50.2
1727600188,493.88671875,56.2
1727600188,493.88671875,50.0
1727600188,493.88671875,55.6
1727600472,490.71484375,50.2
1727600472,490.71484375,55.3
1727600472,490.71484375,49.6
1727600472,490.71484375,47.4
1727600788,496.60546875,50.2
1727600788,496.60546875,55.6
1727600788,496.60546875,50.4
1727600788,496.60546875,40.6
1727601180,493.1953125,50.2
1727601180,493.1953125,54.0
1727601180,493.1953125,50.1
1727601180,493.1953125,56.5
1727601666,476.515625,50.2
1727601666,476.515625,48.8
1727602408,476.60546875,49.7
1727602408,476.60546875,52.1
</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>
<h1> Exit-Codes</h1>
<div class="invert_in_dark_mode" id="plotExitCodesPieChart"></div>
</body>
</html>