| n_samples | confidence | feature_proportion | n_clusters | result |
|---|---|---|---|---|
| 100 | 0.025 | 0.084886 | 1 | 0.210303 |
| 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 |
|---|---|---|---|
| 244 | 253 | 8 | 505 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,confidence,feature_proportion,n_clusters 0,0_0,COMPLETED,Sobol,0.368842210552638105625078424055,639,0.010000000000000000208166817117,0.035388970375061036544028780781,4 1,1_0,COMPLETED,Sobol,0.396599149787446814130476013815,769,0.025000000000000001387778780781,0.174874556064605723992855246252,3 2,2_0,COMPLETED,Sobol,0.376094023505876506874301412608,594,0.100000000000000005551115123126,0.088957514055073266812101451251,1 3,3_0,COMPLETED,Sobol,0.398099524881220356853361863614,847,0.001000000000000000020816681712,0.096011908352375038844250809689,3 4,4_0,COMPLETED,Sobol,0.276819204801200258181381741451,180,0.050000000000000002775557561563,0.027782568894326689634688420938,4 5,5_0,COMPLETED,Sobol,0.414853713428357084858077996614,815,0.005000000000000000104083408559,0.168868544511497020721435546875,3 6,6_0,COMPLETED,Sobol,0.380345086271567933700055164081,726,0.005000000000000000104083408559,0.013220926560461521842571031016,4 7,7_0,COMPLETED,Sobol,0.282070517629407380155726059456,237,0.005000000000000000104083408559,0.136544787138700496331722433752,2 8,8_0,COMPLETED,Sobol,0.372343085771442816600540481886,609,0.001000000000000000020816681712,0.142151387408375740051269531250,4 9,9_0,COMPLETED,Sobol,0.403850962740685215379699002369,800,0.010000000000000000208166817117,0.035909005254507068982672279844,2 10,10_0,COMPLETED,Sobol,0.414353588397099237283782713348,804,0.005000000000000000104083408559,0.123794245161116131526135575314,4 11,11_0,COMPLETED,Sobol,0.395098774693673382429892626533,699,0.005000000000000000104083408559,0.198873057216405885183618806877,1 12,12_0,COMPLETED,Sobol,0.399099774943735940979649967630,899,0.001000000000000000020816681712,0.163477747701108455657958984375,1 13,13_0,COMPLETED,Sobol,0.272818204551137810653926862869,224,0.100000000000000005551115123126,0.170623718388378642352165570628,1 14,14_0,COMPLETED,Sobol,0.354588647161790393447233782354,538,0.001000000000000000020816681712,0.079169971495866783839367997189,2 15,15_0,COMPLETED,Sobol,0.351337834458614661770070597413,442,0.001000000000000000020816681712,0.045531598292291169949308482501,1 16,16_0,COMPLETED,Sobol,0.243810952738184538723942296201,138,0.001000000000000000020816681712,0.045034025423228742079917452656,2 17,17_0,COMPLETED,Sobol,0.415103775943985953134074406989,971,0.005000000000000000104083408559,0.032584542781114576859291531719,1 18,18_0,COMPLETED,Sobol,0.371342835708927232474252377870,657,0.025000000000000001387778780781,0.047511684894561770353682561563,4 19,19_0,COMPLETED,Sobol,0.406601650412603099482566904044,876,0.050000000000000002775557561563,0.060447103716433053799406138751,1 20,20_0,COMPLETED,BoTorch,0.266066516629157256978999157582,100,0.005000000000000000104083408559,0.044424322962941846515416699503,2 21,21_0,COMPLETED,BoTorch,0.236309077269317380221025359788,100,0.001000000000000000020816681712,0.037716570669483182043357771818,3 22,22_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.025000000000000001387778780781,0.057179380594893650102683579917,2 23,23_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2 24,24_0,COMPLETED,BoTorch,0.274318579644911242354510250152,100,0.001000000000000000020816681712,0.081359089299967046748918164667,3 25,25_0,COMPLETED,BoTorch,0.237559389847461832623309874180,100,0.001000000000000000020816681712,0.068729863265890103751765138895,1 26,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 27,27_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.006200475445037423143090915545,2 28,28_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.158829322168216713340171963864,3 29,29_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.049678109617522753227447651625,1 30,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 31,31_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.062710458944585670271187893832,3 32,32_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.001000000000000000020816681712,0.092118489706186704180090885075,4 33,33_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.094334945386475285711291860480,2 34,34_0,COMPLETED,BoTorch,0.242810702675668954597654192185,100,0.010000000000000000208166817117,0.056389409209062095473807829649,2 35,35_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,0.050000000000000002775557561563,0.107980795408812613178639594480,1 36,36_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.044856578283365935999604801054,1 37,37_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 38,38_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,0.005000000000000000104083408559,0.003420433302942613336405930369,3 39,39_0,COMPLETED,BoTorch,0.229807451862965694822094064875,100,0.005000000000000000104083408559,0.129426832632290045310696768865,4 40,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 41,41_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.061780352951651906767693844813,1 42,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 43,43_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.059729765498412738800038113141,1 44,44_0,COMPLETED,BoTorch,0.272818204551137810653926862869,224,0.100000000000000005551115123126,0.170626964876806758164562438651,1 45,45_0,FAILED,BoTorch,,190,0.001000000000000000020816681712,0.000000000000000000000000000000,3 46,46_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 47,47_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4 48,48_0,COMPLETED,BoTorch,0.289072268067016802106650175119,249,0.250000000000000000000000000000,0.200000000000000011102230246252,4 49,49_0,COMPLETED,BoTorch,0.294573643410852681334688440984,245,0.250000000000000000000000000000,0.129810919441300659515903248575,2 50,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 51,51_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.250000000000000000000000000000,0.006866049574184560078116135173,2 52,52_0,COMPLETED,BoTorch,0.216804201050262546068836400082,100,0.010000000000000000208166817117,0.108355869800822068871326564476,1 53,53_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.200000000000000011102230246252,4 54,54_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.106322634362150864051344001382,1 55,55_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.105717243358629600646914070694,1 56,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 57,57_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.025000000000000001387778780781,0.093779770968284825727323550382,4 58,58_0,COMPLETED,BoTorch,0.292323080770192533783813360060,215,0.010000000000000000208166817117,0.115086596391147585882741566365,4 59,59_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4 60,60_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.096935564253627565234339158451,4 61,61_0,COMPLETED,BoTorch,0.242560640160039975299355319294,163,0.100000000000000005551115123126,0.014601076067599513819139644966,4 62,62_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.085909866664424711091285757902,1 63,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 64,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 65,65_0,COMPLETED,BoTorch,0.225806451612903247294639186293,100,0.010000000000000000208166817117,0.081216257540240352486016206512,1 66,66_0,FAILED,BoTorch,,266,0.001000000000000000020816681712,0.000000000000000000000000000000,4 67,67_0,COMPLETED,BoTorch,0.256814203550887687477199960995,177,0.005000000000000000104083408559,0.067131592174801385519700147597,4 68,68_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.090734185904579156556337693473,4 69,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 70,70_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.129410088665140005081966023681,4 71,71_0,COMPLETED,BoTorch,0.244811202800700122850230400218,115,0.001000000000000000020816681712,0.043198331606128993753745959339,4 72,72_0,COMPLETED,BoTorch,0.276069017254313542331090047810,180,0.050000000000000002775557561563,0.027794026254146522725285706201,4 73,73_0,COMPLETED,BoTorch,0.232308077019254821671268018690,128,0.250000000000000000000000000000,0.128478886696705901782067371641,3 74,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 75,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 76,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 77,77_0,FAILED,BoTorch,,233,0.250000000000000000000000000000,0.000000000000000000000000000000,3 78,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 79,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 80,80_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.077600927671389985373906483801,1 81,81_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,0.001000000000000000020816681712,0.147831743427241896204904492151,4 82,82_0,FAILED,BoTorch,,257,0.250000000000000000000000000000,0.000000000000000000000000000000,1 83,83_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 84,84_0,FAILED,BoTorch,,183,0.250000000000000000000000000000,0.000000000000000000000000000000,2 85,85_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 86,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 87,87_0,FAILED,BoTorch,,232,0.100000000000000005551115123126,0.000000000000000000000000000000,2 88,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 89,89_0,FAILED,BoTorch,,191,0.250000000000000000000000000000,0.000000000000000000000000000000,3 90,90_0,COMPLETED,BoTorch,0.310327581895473825213116469968,285,0.100000000000000005551115123126,0.087026870679377157924427876878,4 91,91_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.250000000000000000000000000000,0.006872404276689253466159357231,2 92,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 93,93_0,COMPLETED,BoTorch,0.319579894973743394714915666555,170,0.100000000000000005551115123126,0.035728821249001528614908096415,3 94,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 95,95_0,COMPLETED,BoTorch,0.259314828707176814326373914810,183,0.025000000000000001387778780781,0.050710015386007938065215938650,4 96,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 97,97_0,COMPLETED,BoTorch,0.265316329082270541128707463940,196,0.050000000000000002775557561563,0.032618923786967415900939215589,3 98,98_0,COMPLETED,BoTorch,0.249312328082020528974283024581,154,0.005000000000000000104083408559,0.012694664077119818812455775969,4 99,99_0,FAILED,BoTorch,,250,0.250000000000000000000000000000,0.000000000000000000000000000000,4 100,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 101,101_0,COMPLETED,BoTorch,0.262315578894723677727540689375,193,0.050000000000000002775557561563,0.101669647441252716801862732154,4 102,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 103,85_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 104,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 105,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 106,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 107,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 108,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 109,109_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.050000000000000002775557561563,0.098295268134416524663521386174,4 110,110_0,COMPLETED,BoTorch,0.260065016254063530176665608451,178,0.005000000000000000104083408559,0.021807185621937494324207307272,4 111,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 112,112_0,COMPLETED,BoTorch,0.222555638909727404595173538837,116,0.001000000000000000020816681712,0.048331853330789986689097759154,4 113,113_0,COMPLETED,BoTorch,0.267066766691672952127589724114,169,0.010000000000000000208166817117,0.018648485564222161414704714844,3 114,114_0,COMPLETED,BoTorch,0.287321830457614391107767914946,228,0.010000000000000000208166817117,0.039446095795129983152538244440,4 115,115_0,COMPLETED,BoTorch,0.235558889722430553348431203631,142,0.025000000000000001387778780781,0.087632654575256763163082496249,4 116,116_0,COMPLETED,BoTorch,0.249312328082020528974283024581,154,0.100000000000000005551115123126,0.064897217770104737022407448421,4 117,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 118,118_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.056162085787583908291775713906,4 119,119_0,COMPLETED,BoTorch,0.247561890472618117975400764408,154,0.005000000000000000104083408559,0.012700182286472237389030048860,4 120,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 121,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 122,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 123,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 124,124_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.080916029449961845987360220533,1 125,125_0,COMPLETED,BoTorch,0.292823205801450381358108643326,256,0.010000000000000000208166817117,0.081671389557463253128233304778,3 126,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 127,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 128,128_0,COMPLETED,BoTorch,0.233808452113028253371851405973,132,0.050000000000000002775557561563,0.194664398985279291087735487054,4 129,129_0,COMPLETED,BoTorch,0.249312328082020528974283024581,149,0.050000000000000002775557561563,0.115717772861261017358636138397,4 130,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 131,131_0,COMPLETED,BoTorch,0.253313328332082976501737903163,161,0.005000000000000000104083408559,0.072350303959938883080482696641,4 132,132_0,FAILED,BoTorch,,273,0.250000000000000000000000000000,0.000000000000000000000000000000,4 133,133_0,COMPLETED,BoTorch,0.258314578644661119177783348277,115,0.001000000000000000020816681712,0.043232179473651689838309408742,4 134,134_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.081086735412702726222278215573,1 135,135_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.033604323227361482251396296306,4 136,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 137,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 138,138_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,4 139,139_0,COMPLETED,BoTorch,0.280070017504376100880847388908,198,0.010000000000000000208166817117,0.054554901597087306075462009858,4 140,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 141,141_0,COMPLETED,BoTorch,0.223055763940985252169468822103,117,0.025000000000000001387778780781,0.076458015051571945330799451312,1 142,142_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.075794577395037032729874226789,1 143,143_0,COMPLETED,BoTorch,0.218804701175293825343715070630,113,0.010000000000000000208166817117,0.078277427219997086638159089489,1 144,144_0,COMPLETED,BoTorch,0.214553638409602398517961319158,111,0.025000000000000001387778780781,0.076677726639996668378529420806,1 145,145_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.082033249829531917907132765322,1 146,146_0,COMPLETED,BoTorch,0.229307326831707958270101244125,111,0.010000000000000000208166817117,0.077808241936501418289928722061,1 147,147_0,COMPLETED,BoTorch,0.243810952738184538723942296201,112,0.025000000000000001387778780781,0.079934142304802516254547128938,1 148,148_0,COMPLETED,BoTorch,0.229307326831707958270101244125,111,0.010000000000000000208166817117,0.078606658097365778026244242938,1 149,149_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.093976912011139987490615510524,3 150,150_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.073873975434035066278681824770,1 151,151_0,COMPLETED,BoTorch,0.320580145036259089863506233087,368,0.050000000000000002775557561563,0.066990045419658111880423234652,1 152,152_0,COMPLETED,BoTorch,0.219804951237809409470003174647,110,0.025000000000000001387778780781,0.077671858577057412142963244150,1 153,153_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.078286201019343124030136493730,1 154,154_0,COMPLETED,BoTorch,0.214553638409602398517961319158,111,0.025000000000000001387778780781,0.080940034759783882623018769209,1 155,155_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.078931929702148517780102565666,1 156,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 157,157_0,COMPLETED,BoTorch,0.243810952738184538723942296201,112,0.025000000000000001387778780781,0.078488616787303983057810796709,1 158,158_0,COMPLETED,BoTorch,0.273818454613653394780214966886,201,0.025000000000000001387778780781,0.048806280926064607439407438960,3 159,159_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.090561233853479583544299202913,1 160,160_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.091265011881889443468018896510,1 161,161_0,FAILED,BoTorch,,153,0.001000000000000000020816681712,0.000000000000000000000000000000,3 162,162_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.088714849464212777729876791000,1 163,161_0,FAILED,BoTorch,,153,0.001000000000000000020816681712,0.000000000000000000000000000000,3 164,164_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.089125128248580320899918660871,1 165,165_0,COMPLETED,BoTorch,0.230807701925481389970684631407,105,0.005000000000000000104083408559,0.033914710209900660042858788756,3 166,166_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.084885830043585172588471721156,1 167,167_0,COMPLETED,BoTorch,0.258314578644661119177783348277,184,0.001000000000000000020816681712,0.200000000000000011102230246252,4 168,168_0,COMPLETED,BoTorch,0.413353338334583653157494609331,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4 169,169_0,FAILED,BoTorch,,151,0.001000000000000000020816681712,0.000000000000000000000000000000,3 170,170_0,COMPLETED,BoTorch,0.413353338334583653157494609331,1000,0.025000000000000001387778780781,0.069184708041460846184023125716,3 171,171_0,COMPLETED,BoTorch,0.398849712428107072703653557255,967,0.025000000000000001387778780781,0.000648655421756750443819383722,4 172,172_0,COMPLETED,BoTorch,0.272318079519879963079631579603,168,0.250000000000000000000000000000,0.139266240608920038868134838594,4 173,173_0,COMPLETED,BoTorch,0.262815703925981525301835972641,184,0.001000000000000000020816681712,0.200000000000000011102230246252,3 174,174_0,COMPLETED,BoTorch,0.305076269067266814261074614478,297,0.050000000000000002775557561563,0.086755174611452920419019108067,3 175,175_0,COMPLETED,BoTorch,0.376594148537134243426294233359,605,0.005000000000000000104083408559,0.058045951165739989585645730585,2 176,176_0,COMPLETED,BoTorch,0.267066766691672952127589724114,169,0.100000000000000005551115123126,0.200000000000000011102230246252,3 177,177_0,COMPLETED,BoTorch,0.307326831707926961811949695402,288,0.100000000000000005551115123126,0.200000000000000011102230246252,4 178,178_0,COMPLETED,BoTorch,0.266066516629157256978999157582,167,0.250000000000000000000000000000,0.163251171271723283240362434299,4 179,179_0,RUNNING,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,3 180,180_0,COMPLETED,BoTorch,0.242560640160039975299355319294,163,0.050000000000000002775557561563,0.200000000000000011102230246252,4 181,181_0,COMPLETED,BoTorch,0.263815953988497109428124076658,166,0.250000000000000000000000000000,0.200000000000000011102230246252,4 182,182_0,COMPLETED,BoTorch,0.266566641660415104553294440848,172,0.025000000000000001387778780781,0.200000000000000011102230246252,3 183,183_0,COMPLETED,BoTorch,0.308327081770442656960540261935,160,0.010000000000000000208166817117,0.077079054102868360676126258113,3 184,184_0,COMPLETED,BoTorch,0.313578394598649667912582117424,363,0.001000000000000000020816681712,0.200000000000000011102230246252,4 185,185_0,COMPLETED,BoTorch,0.289072268067016802106650175119,249,0.025000000000000001387778780781,0.200000000000000011102230246252,3 186,186_0,COMPLETED,BoTorch,0.324831207801950516689259984560,305,0.050000000000000002775557561563,0.092948440917075603184827059522,3 187,187_0,COMPLETED,BoTorch,0.305826456614153530111366308120,299,0.010000000000000000208166817117,0.196773036907419873742242089065,3 188,188_0,COMPLETED,BoTorch,0.279569892473118253306552105641,219,0.010000000000000000208166817117,0.177131950551116412739816041721,3 189,189_0,COMPLETED,BoTorch,0.263815953988497109428124076658,166,0.250000000000000000000000000000,0.200000000000000011102230246252,3 190,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 191,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 192,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 193,193_0,FAILED,BoTorch,,156,0.001000000000000000020816681712,0.000000000000000000000000000000,3 194,194_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.101876220952681062481559592925,1 195,193_0,FAILED,BoTorch,,156,0.001000000000000000020816681712,0.000000000000000000000000000000,3 196,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 197,197_0,FAILED,BoTorch,,138,0.001000000000000000020816681712,0.000000000000000000000000000000,3 198,198_0,COMPLETED,BoTorch,0.225556389097274267996340313402,100,0.001000000000000000020816681712,0.037994293868149099646647215422,4 199,199_0,FAILED,BoTorch,,147,0.001000000000000000020816681712,0.000000000000000000000000000000,3 200,200_0,COMPLETED,BoTorch,0.223805951487871968019760515745,124,0.100000000000000005551115123126,0.047017241312899360483612554162,3 201,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 202,202_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.091924450890514430856370609035,3 203,203_0,COMPLETED,BoTorch,0.224806201550387552146048619761,115,0.001000000000000000020816681712,0.041915728710977984139418595078,3 204,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 205,205_0,COMPLETED,BoTorch,0.216304076019004698494541116816,113,0.001000000000000000020816681712,0.042060731604721214582642829782,4 206,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 207,207_0,COMPLETED,BoTorch,0.231807951987996974096972735424,126,0.005000000000000000104083408559,0.089296087155725720196919326099,4 208,208_0,COMPLETED,BoTorch,0.255063765941485387500620163337,148,0.005000000000000000104083408559,0.106549246578776984906156144461,4 209,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 210,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 211,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 212,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 213,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 214,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 215,215_0,COMPLETED,BoTorch,0.229557389347336826546097654500,100,0.005000000000000000104083408559,0.002774243316007354853702793207,4 216,216_0,COMPLETED,BoTorch,0.405101275318829667781983516761,873,0.001000000000000000020816681712,0.065203717354405318906707123006,4 217,217_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,3 218,218_0,COMPLETED,BoTorch,0.233058264566141537521559712332,106,0.250000000000000000000000000000,0.028573333614170373651042211804,2 219,219_0,COMPLETED,BoTorch,0.307826956739184809386244978668,140,0.005000000000000000104083408559,0.125793879863307866973087811857,4 220,220_0,FAILED,BoTorch,,120,0.010000000000000000208166817117,0.000000000000000000000000000000,3 221,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 222,222_0,COMPLETED,BoTorch,0.354588647161790393447233782354,516,0.005000000000000000104083408559,0.200000000000000011102230246252,4 223,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 224,224_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.085149133521580211425572315420,2 225,225_0,COMPLETED,BoTorch,0.233808452113028253371851405973,132,0.010000000000000000208166817117,0.188744813439255088027834972308,3 226,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 227,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 228,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 229,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 230,230_0,COMPLETED,BoTorch,0.282320580145036248431722469832,100,0.001000000000000000020816681712,0.038138711481821963023008947857,4 231,231_0,COMPLETED,BoTorch,0.225556389097274267996340313402,112,0.001000000000000000020816681712,0.058898656224075411624863107818,3 232,232_0,COMPLETED,BoTorch,0.303075768942235534986195943929,298,0.025000000000000001387778780781,0.181126345411895595116646973111,3 233,233_0,COMPLETED,BoTorch,0.256814203550887687477199960995,186,0.250000000000000000000000000000,0.200000000000000011102230246252,4 234,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 235,235_0,COMPLETED,BoTorch,0.254563640910227539926324880071,207,0.010000000000000000208166817117,0.076815116231982533134825530396,4 236,236_0,COMPLETED,BoTorch,0.282320580145036248431722469832,204,0.010000000000000000208166817117,0.192900806479272907134614456481,3 237,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 238,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 239,239_0,COMPLETED,BoTorch,0.249562390597649397250279434957,125,0.025000000000000001387778780781,0.200000000000000011102230246252,4 240,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 241,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 242,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 243,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 244,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 245,245_0,COMPLETED,BoTorch,0.322330582645661389840086030745,314,0.010000000000000000208166817117,0.177513676964164202054519137164,1 246,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 247,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 248,248_0,COMPLETED,BoTorch,0.222805701425356383893472411728,107,0.001000000000000000020816681712,0.063553068942243190475593905830,4 249,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 250,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 251,251_0,COMPLETED,BoTorch,0.216054013503375830218544706440,113,0.001000000000000000020816681712,0.042065755358342678260630265186,4 252,252_0,COMPLETED,BoTorch,0.254313578394598671650328469696,164,0.025000000000000001387778780781,0.158831795514028983884458057219,3 253,253_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.107406421025384835044036435647,4 254,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 255,255_0,COMPLETED,BoTorch,0.368092023005751389774786730413,555,0.005000000000000000104083408559,0.105177191352630539089574313039,1 256,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 257,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 258,258_0,COMPLETED,BoTorch,0.281820455113778400857427186565,120,0.250000000000000000000000000000,0.017551693754480384573879447885,2 259,259_0,FAILED,BoTorch,,103,0.001000000000000000020816681712,0.000000000000000000000000000000,4 260,260_0,FAILED,BoTorch,,210,0.250000000000000000000000000000,0.000000000000000000000000000000,1 261,261_0,FAILED,BoTorch,,154,0.001000000000000000020816681712,0.000000000000000000000000000000,4 262,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 263,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 264,264_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.097343657728073004764546283241,1 265,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 266,266_0,FAILED,BoTorch,,115,0.001000000000000000020816681712,0.000000000000000000000000000000,3 267,267_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.080269216156161171671357124069,3 268,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 269,269_0,COMPLETED,BoTorch,0.319829957489372374013214539445,295,0.001000000000000000020816681712,0.108620321475835807101262275864,4 270,270_0,COMPLETED,BoTorch,0.260065016254063530176665608451,178,0.100000000000000005551115123126,0.186825317231154058861264388725,2 271,271_0,FAILED,BoTorch,,133,0.250000000000000000000000000000,0.000000000000000000000000000000,4 272,272_0,FAILED,BoTorch,,101,0.005000000000000000104083408559,0.000000000000000000000000000000,3 273,273_0,COMPLETED,BoTorch,0.222805701425356383893472411728,114,0.250000000000000000000000000000,0.000866549584994571867914425756,2 274,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3 275,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 276,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 277,277_0,COMPLETED,BoTorch,0.301825456364090971561608967022,180,0.050000000000000002775557561563,0.200000000000000011102230246252,4 278,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 279,279_0,COMPLETED,BoTorch,0.294323580895223813058692030609,264,0.250000000000000000000000000000,0.200000000000000011102230246252,3 280,280_0,COMPLETED,BoTorch,0.235558889722430553348431203631,102,0.001000000000000000020816681712,0.104551787020007405648591713998,2 281,281_0,FAILED,BoTorch,,104,0.001000000000000000020816681712,0.000000000000000000000000000000,3 282,282_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.098937027884926059817516375006,1 283,283_0,FAILED,BoTorch,,116,0.001000000000000000020816681712,0.000000000000000000000000000000,3 284,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 285,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 286,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 287,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 288,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 289,289_0,COMPLETED,BoTorch,0.263315828957239261853828793392,100,0.001000000000000000020816681712,0.037732041844681492304136583016,3 290,290_0,FAILED,BoTorch,,131,0.001000000000000000020816681712,0.000000000000000000000000000000,3 291,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 292,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 293,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 294,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 295,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 296,296_0,COMPLETED,BoTorch,0.224806201550387552146048619761,100,0.001000000000000000020816681712,0.038209858080888031706123797449,4 297,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 298,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 299,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 300,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 301,301_0,COMPLETED,BoTorch,0.281570392598149532581430776190,227,0.025000000000000001387778780781,0.074601440920845557558394034459,4 302,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 303,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 304,304_0,FAILED,BoTorch,,442,0.005000000000000000104083408559,0.000000000000000000000000000000,1 305,305_0,COMPLETED,BoTorch,0.396099024756189077578483193065,698,0.025000000000000001387778780781,0.075019253222113863044384629575,4 306,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 307,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 308,308_0,COMPLETED,BoTorch,0.234558639659914969222143099614,100,0.001000000000000000020816681712,0.038009937163653993719147905495,4 309,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 310,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 311,311_0,COMPLETED,BoTorch,0.350837709427356814195775314147,492,0.005000000000000000104083408559,0.110338863498554096143067226876,1 312,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 313,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 314,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 315,315_0,COMPLETED,BoTorch,0.394848712178044514153896216158,708,0.025000000000000001387778780781,0.076822910841062769238263285843,4 316,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 317,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 318,23_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2 319,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 320,320_0,COMPLETED,BoTorch,0.307826956739184809386244978668,309,0.001000000000000000020816681712,0.200000000000000011102230246252,2 321,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 322,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 323,323_0,COMPLETED,BoTorch,0.338334583645911513016812932619,438,0.250000000000000000000000000000,0.089149457290131350895023842895,2 324,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 325,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 326,326_0,COMPLETED,BoTorch,0.403600900225056236081400129478,822,0.025000000000000001387778780781,0.200000000000000011102230246252,3 327,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 328,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 329,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 330,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 331,331_0,FAILED,BoTorch,,242,0.001000000000000000020816681712,0.000000000000000000000000000000,2 332,332_0,COMPLETED,BoTorch,0.396849212303075793428774886706,757,0.025000000000000001387778780781,0.199959252023311218060541705199,1 333,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 334,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 335,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 336,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 337,337_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.003644233271601659759908464764,4 338,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 339,339_0,COMPLETED,BoTorch,0.214303575893973530241964908782,123,0.001000000000000000020816681712,0.049161433686879987825513182997,4 340,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4 341,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 342,342_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.002785043614937771343925687617,4 343,343_0,COMPLETED,BoTorch,0.298824706176544108160442192457,253,0.025000000000000001387778780781,0.103692612360672800631000711746,4 344,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 345,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 346,346_0,FAILED,BoTorch,,127,0.001000000000000000020816681712,0.000000000000000000000000000000,4 347,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 348,348_0,FAILED,BoTorch,,128,0.001000000000000000020816681712,0.000000000000000000000000000000,4 349,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 350,350_0,COMPLETED,BoTorch,0.225556389097274267996340313402,100,0.001000000000000000020816681712,0.038014962924231454621804005001,4 351,351_0,FAILED,BoTorch,,126,0.001000000000000000020816681712,0.000000000000000000000000000000,4 352,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 353,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 354,354_0,COMPLETED,BoTorch,0.294323580895223813058692030609,205,0.050000000000000002775557561563,0.200000000000000011102230246252,4 355,355_0,COMPLETED,BoTorch,0.226056514128532115570635596669,113,0.001000000000000000020816681712,0.029293654769387195146990165995,4 356,356_0,FAILED,BoTorch,,212,0.250000000000000000000000000000,0.000000000000000000000000000000,4 357,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 358,348_0,FAILED,BoTorch,,128,0.001000000000000000020816681712,0.000000000000000000000000000000,4 359,359_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.102077777838947142408088097909,1 360,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 361,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 362,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 363,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 364,364_0,COMPLETED,BoTorch,0.244061015253813406999938706576,138,0.001000000000000000020816681712,0.045118079302409623554392936740,2 365,365_0,COMPLETED,BoTorch,0.233558389597399385095854995598,137,0.250000000000000000000000000000,0.102080727218184383331411879681,4 366,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4 367,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4 368,368_0,COMPLETED,BoTorch,0.328582145536384095940718452766,250,0.025000000000000001387778780781,0.200000000000000011102230246252,4 369,369_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.005000000000000000104083408559,0.059094712025506257457863057425,4 370,370_0,FAILED,BoTorch,,169,0.001000000000000000020816681712,0.000000000000000000000000000000,1 371,371_0,RUNNING,BoTorch,,520,0.005000000000000000104083408559,0.115862528376004414454314428440,1 372,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 373,373_0,COMPLETED,BoTorch,0.287821955488872238682063198212,226,0.010000000000000000208166817117,0.187689834724264648091462959201,2 374,374_0,COMPLETED,BoTorch,0.356089022255563936170119632152,514,0.005000000000000000104083408559,0.117203552200532523652753980059,1 375,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 376,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4 377,377_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.005000000000000000104083408559,0.059090670981739881750804954663,4 378,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 379,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 380,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 381,381_0,FAILED,BoTorch,,135,0.001000000000000000020816681712,0.000000000000000000000000000000,4 382,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 383,383_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,4 384,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 385,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 386,386_0,COMPLETED,BoTorch,0.235808952238059532646730076522,136,0.005000000000000000104083408559,0.047075446065609448387245805634,4 387,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 388,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 389,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 390,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 391,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4 392,392_0,COMPLETED,BoTorch,0.234808702175543837498139509989,138,0.050000000000000002775557561563,0.137539257229414318972615660641,4 393,393_0,FAILED,BoTorch,,143,0.001000000000000000020816681712,0.000000000000000000000000000000,4 394,393_0,FAILED,BoTorch,,143,0.001000000000000000020816681712,0.000000000000000000000000000000,4 395,395_0,COMPLETED,BoTorch,0.270317579394848683804752909055,105,0.005000000000000000104083408559,0.174345284829028612794132868657,3 396,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 397,397_0,COMPLETED,BoTorch,0.397099274818704661704771297082,680,0.005000000000000000104083408559,0.128880008789946209901700058253,3 398,398_0,COMPLETED,BoTorch,0.232808202050512669245563301956,133,0.005000000000000000104083408559,0.006174432960647995710656843471,3 399,399_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,4 400,400_0,COMPLETED,BoTorch,0.377094273568392091000589516625,633,0.001000000000000000020816681712,0.164812799567015444424100678589,3 401,23_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2 402,402_0,FAILED,BoTorch,,146,0.001000000000000000020816681712,0.000000000000000000000000000000,4 403,403_0,COMPLETED,BoTorch,0.294073518379594944782695620233,301,0.050000000000000002775557561563,0.154179740280119992323903943543,3 404,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 405,405_0,COMPLETED,BoTorch,0.375343835958989791024009718967,656,0.005000000000000000104083408559,0.110598722412879477139391326546,3 406,406_0,FAILED,BoTorch,,233,0.005000000000000000104083408559,0.000000000000000000000000000000,3 407,407_0,FAILED,BoTorch,,230,0.005000000000000000104083408559,0.000000000000000000000000000000,4 408,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4 409,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 410,410_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.100734995186227138663781488503,1 411,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 412,412_0,FAILED,BoTorch,,144,0.001000000000000000020816681712,0.000000000000000000000000000000,4 413,383_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,4 414,414_0,COMPLETED,BoTorch,0.285821455363840959407184527663,233,0.005000000000000000104083408559,0.016954031106625421648770313254,4 415,415_0,RUNNING,BoTorch,,113,0.001000000000000000020816681712,0.029316033192138924584613235425,4 416,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 417,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 418,418_0,FAILED,BoTorch,,145,0.001000000000000000020816681712,0.000000000000000000000000000000,4 419,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 420,420_0,COMPLETED,BoTorch,0.255063765941485387500620163337,148,0.100000000000000005551115123126,0.021914020900722232243484910441,3 421,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 422,399_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,4 423,402_0,FAILED,BoTorch,,146,0.001000000000000000020816681712,0.000000000000000000000000000000,4 424,424_0,COMPLETED,BoTorch,0.418104526131532927557543644070,798,0.001000000000000000020816681712,0.145136112966548158631496789894,2 425,425_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.050000000000000002775557561563,0.010529777222508399439626636251,3 426,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 427,427_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,3 428,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 429,429_0,COMPLETED,BoTorch,0.253313328332082976501737903163,161,0.100000000000000005551115123126,0.200000000000000011102230246252,4 430,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4 431,431_0,COMPLETED,BoTorch,0.253563390847711955800036776054,146,0.005000000000000000104083408559,0.043630832122724706734206989722,3 432,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4 433,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 434,434_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,3 435,435_0,FAILED,BoTorch,,165,0.010000000000000000208166817117,0.000000000000000000000000000000,4 436,399_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,4 437,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4 438,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 439,381_0,FAILED,BoTorch,,135,0.001000000000000000020816681712,0.000000000000000000000000000000,4 440,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 441,441_0,COMPLETED,BoTorch,0.263315828957239261853828793392,159,0.250000000000000000000000000000,0.200000000000000011102230246252,4 442,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 443,443_0,COMPLETED,BoTorch,0.249812453113278265526275845332,115,0.010000000000000000208166817117,0.200000000000000011102230246252,4 444,444_0,FAILED,BoTorch,,149,0.001000000000000000020816681712,0.000000000000000000000000000000,2 445,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4 446,446_0,COMPLETED,BoTorch,0.237559389847461832623309874180,108,0.010000000000000000208166817117,0.055165423534351457068858337607,4 447,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4 448,448_0,COMPLETED,BoTorch,0.409852463115778942182032551500,828,0.050000000000000002775557561563,0.104987838324140700385633806491,3 449,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4 450,450_0,COMPLETED,BoTorch,0.218054513628407109493423376989,111,0.010000000000000000208166817117,0.184924891557410431275343398738,4 451,451_0,COMPLETED,BoTorch,0.219804951237809409470003174647,116,0.001000000000000000020816681712,0.056557617161534082250717858642,4 452,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 453,453_0,COMPLETED,BoTorch,0.231307826956739237544979914674,123,0.001000000000000000020816681712,0.006998235909252846845274298460,4 454,454_0,COMPLETED,BoTorch,0.252063015753938524099453388772,213,0.010000000000000000208166817117,0.092263341324696171441566150406,4 455,455_0,COMPLETED,BoTorch,0.358589647411852951996991123451,454,0.250000000000000000000000000000,0.129329853696432212073474943281,4 456,456_0,COMPLETED,BoTorch,0.252563140785196260651446209522,144,0.025000000000000001387778780781,0.086246433845858061495448509959,4 457,457_0,COMPLETED,BoTorch,0.218054513628407109493423376989,111,0.025000000000000001387778780781,0.099771724707667874820771203304,4 458,458_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,3 459,459_0,COMPLETED,BoTorch,0.358339584896224083720994713076,484,0.005000000000000000104083408559,0.103100456426519868080582398306,1 460,460_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.099660230246399750253516458542,1 461,197_0,FAILED,BoTorch,,138,0.001000000000000000020816681712,0.000000000000000000000000000000,3 462,462_0,FAILED,BoTorch,,132,0.001000000000000000020816681712,0.000000000000000000000000000000,4 463,463_0,COMPLETED,BoTorch,0.233558389597399385095854995598,137,0.005000000000000000104083408559,0.087213788633888456036657998993,4 464,464_0,RUNNING,BoTorch,,100,0.025000000000000001387778780781,0.106803489014117319877428258224,1 465,465_0,FAILED,BoTorch,,130,0.001000000000000000020816681712,0.000000000000000000000000000000,4 466,197_0,FAILED,BoTorch,,138,0.001000000000000000020816681712,0.000000000000000000000000000000,3 467,467_0,COMPLETED,BoTorch,0.214803700925231266793957729533,100,0.005000000000000000104083408559,0.113063467936606987240821808882,2 468,468_0,FAILED,BoTorch,,118,0.001000000000000000020816681712,0.000000000000000000000000000000,4 469,469_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.097970582964692493055380850819,3 470,470_0,COMPLETED,BoTorch,0.240310077519379827748480238370,138,0.001000000000000000020816681712,0.001068359383450949440241828370,4 471,471_0,FAILED,BoTorch,,144,0.001000000000000000020816681712,0.000000000000000000000000000000,3 472,427_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,3 473,473_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.097955631970960743704068818261,3 474,474_0,COMPLETED,BoTorch,0.369342335583895953199373707321,582,0.050000000000000002775557561563,0.200000000000000011102230246252,2 475,475_0,COMPLETED,BoTorch,0.349337334333583382495191926864,479,0.005000000000000000104083408559,0.110874967982368621832733879273,1 476,476_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.109587519917548503745052812519,1 477,477_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.108314321565190885277019106070,1 478,478_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.112717130664233902703763590125,1 479,479_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.106477629309613797126132794801,1 480,480_0,COMPLETED,BoTorch,0.257814453613403382625790527527,135,0.001000000000000000020816681712,0.034580827181856177432450749620,4 481,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 482,482_0,COMPLETED,BoTorch,0.376094023505876506874301412608,594,0.010000000000000000208166817117,0.071391435460074689767218103498,4 483,483_0,COMPLETED,BoTorch,0.229307326831707958270101244125,133,0.001000000000000000020816681712,0.040022040532536731771706683958,4 484,484_0,COMPLETED,BoTorch,0.234058514628657121647847816348,128,0.001000000000000000020816681712,0.042236986856669260503860385825,4 485,485_0,COMPLETED,BoTorch,0.364841210302575658097623545473,545,0.005000000000000000104083408559,0.179887075852265265751839251607,3 486,486_0,COMPLETED,BoTorch,0.357339334833708388572404146544,476,0.025000000000000001387778780781,0.097981242677061333723464997547,4 487,487_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.108006905580991977022797811969,1 488,488_0,COMPLETED,BoTorch,0.228057014253563394845514267217,137,0.001000000000000000020816681712,0.043458904588374654143212438839,4 489,489_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.109970037435713943740900333523,1 490,490_0,COMPLETED,BoTorch,0.243310827706926691149647012935,100,0.001000000000000000020816681712,0.002400710837877469572970712264,1 491,491_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.113228570859403743220639171341,1 492,492_0,COMPLETED,BoTorch,0.330582645661415375215597123315,404,0.100000000000000005551115123126,0.105164585147430836298276801699,1 493,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 494,494_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.113494349156980783854820060697,1 495,434_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,3 496,496_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.111564450867507616860230257316,1 497,497_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.159922395950273243947492574080,3 498,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 499,499_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.112127652541063327351622547212,1 500,500_0,COMPLETED,BoTorch,0.245311327831957970424525683484,161,0.005000000000000000104083408559,0.000485305960241321464321284651,2 501,501_0,RUNNING,BoTorch,,100,0.025000000000000001387778780781,0.113949093451803262766475199896,1 502,502_0,RUNNING,BoTorch,,100,0.010000000000000000208166817117,0.104977585740612180953412746476,3 503,23_0,RUNNING,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2 504,79_0,RUNNING,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
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 1727442458,1727442499,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 639 confidence 0.01 feature_proportion 0.03538897037506104 n_clusters 4,639,0.01,0.03538897037506104,4,0.3688422105526381,0,None,i7186,37,0.05461365341335335 1727442461,1727442504,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 769 confidence 0.025 feature_proportion 0.17487455606460572 n_clusters 3,769,0.025,0.17487455606460572,3,0.3965991497874468,0,None,i7186,39,0.061327831957989506 1727442478,1727442518,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 594 confidence 0.1 feature_proportion 0.08895751405507327 n_clusters 1,594,0.1,0.08895751405507327,1,0.3760940235058765,0,None,i7186,36,0.05316329082270567 1727442518,1727442562,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 876 confidence 0.05 feature_proportion 0.060447103716433054 n_clusters 1,876,0.05,0.060447103716433054,1,0.4066016504126031,0,None,i7186,40,0.07843627573560058 1727442509,1727442567,58,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 224 confidence 0.1 feature_proportion 0.17062371838837864 n_clusters 1,224,0.1,0.17062371838837864,1,0.2728182045511378,0,None,i7177,26,0.02306826706676669 1727442509,1727442568,59,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 feature_proportion 0.04503402542322874 n_clusters 2,138,0.001,0.04503402542322874,2,0.24381095273818454,0,None,i7177,27,0.01592398099524881 1727442496,1727442569,73,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 180 confidence 0.05 feature_proportion 0.02778256889432669 n_clusters 4,180,0.05,0.02778256889432669,4,0.27681920480120026,0,None,i7181,28,0.01738529870562879 1727442496,1727442570,74,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 237 confidence 0.005 feature_proportion 0.1365447871387005 n_clusters 2,237,0.005,0.1365447871387005,2,0.2820705176294074,0,None,i7181,29,0.02398933066599983 1727442509,1727442572,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 442 confidence 0.001 feature_proportion 0.04553159829229117 n_clusters 1,442,0.001,0.04553159829229117,1,0.35133783445861466,0,None,i7177,31,0.041510377594398594 1727442509,1727442572,63,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 538 confidence 0.001 feature_proportion 0.07916997149586678 n_clusters 2,538,0.001,0.07916997149586678,2,0.3545886471617904,0,None,i7177,32,0.04788697174293574 1727442509,1727442574,65,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 699 confidence 0.005 feature_proportion 0.19887305721640589 n_clusters 1,699,0.005,0.19887305721640589,1,0.3950987746936734,0,None,i7177,33,0.061702925731432864 1727442509,1727442575,66,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 657 confidence 0.025 feature_proportion 0.04751168489456177 n_clusters 4,657,0.025,0.04751168489456177,4,0.37134283570892723,0,None,i7177,34,0.05411352838209552 1727442509,1727442576,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 804 confidence 0.005 feature_proportion 0.12379424516111613 n_clusters 4,804,0.005,0.12379424516111613,4,0.41435358839709924,0,None,i7177,35,0.0758522964074352 1727442509,1727442576,67,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 899 confidence 0.001 feature_proportion 0.16347774770110846 n_clusters 1,899,0.001,0.16347774770110846,1,0.39909977494373594,0,None,i7177,35,0.08093690089188964 1727442509,1727442578,69,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 971 confidence 0.005 feature_proportion 0.03258454278111458 n_clusters 1,971,0.005,0.03258454278111458,1,0.41510377594398595,0,None,i7177,37,0.0756022338918063 1727442496,1727442578,82,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 609 confidence 0.001 feature_proportion 0.14215138740837574 n_clusters 4,609,0.001,0.14215138740837574,4,0.3723430857714428,0,None,i7181,37,0.053913478369592406 1727442496,1727442580,84,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 726 confidence 0.005 feature_proportion 0.013220926560461522 n_clusters 4,726,0.005,0.013220926560461522,4,0.38034508627156793,0,None,i7181,40,0.06539134783695923 1727442496,1727442581,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 847 confidence 0.001 feature_proportion 0.09601190835237504 n_clusters 3,847,0.001,0.09601190835237504,3,0.39809952488122036,0,None,i7181,40,0.08127031757939483 1727442496,1727442581,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 815 confidence 0.005 feature_proportion 0.16886854451149702 n_clusters 3,815,0.005,0.16886854451149702,3,0.4148537134283571,0,None,i7181,40,0.07568558806368259 1727442496,1727442581,85,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 800 confidence 0.01 feature_proportion 0.03590900525450707 n_clusters 2,800,0.01,0.03590900525450707,2,0.4038509627406852,0,None,i7181,40,0.07935317162623988 1727442702,1727442706,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 2,100,0.001,0,2,None,1,None,i7181 1727442703,1727442707,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727442684,1727442714,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.044424322962941847 n_clusters 2,100,0.005,0.044424322962941847,2,0.26606651662915726,0,None,i7186,25,0.010157944891628313 1727442684,1727442714,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.05717938059489365 n_clusters 2,100,0.025,0.05717938059489365,2,0.28157039259814953,0,None,i7186,26,0.009239489359519367 1727442684,1727442714,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.03771657066948318 n_clusters 3,100,0.001,0.03771657066948318,3,0.23630907726931738,0,None,i7186,26,0.011266705565280206 1727442724,1727442728,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727442702,1727442732,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.1588293221682167 n_clusters 3,100,0.1,0.1588293221682167,3,0.28157039259814953,0,None,i7181,26,0.009239489359519367 1727442703,1727442732,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.006200475445037423 n_clusters 2,100,0.01,0.006200475445037423,2,0.28157039259814953,0,None,i7181,26,0.009239489359519367 1727442702,1727442733,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.08135908929996705 n_clusters 3,100,0.001,0.08135908929996705,3,0.27431857964491124,0,None,i7181,26,0.00967347099932878 1727442703,1727442733,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.0687298632658901 n_clusters 1,100,0.001,0.0687298632658901,1,0.23755938984746183,0,None,i7181,26,0.01123197466033175 1727442704,1727442733,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.04967810961752275 n_clusters 1,100,0.025,0.04967810961752275,1,0.21030257564391097,0,None,i7181,26,0.011358102683565628 1727442731,1727442734,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7177 1727442723,1727442748,25,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.10798079540881261 n_clusters 1,100,0.05,0.10798079540881261,1,0.21930482620655167,0,None,i7177,21,0.01112120135296982 1727442724,1727442753,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.09433494538647529 n_clusters 2,100,0.25,0.09433494538647529,2,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727442724,1727442753,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.056389409209062095 n_clusters 2,100,0.01,0.056389409209062095,2,0.24281070267566895,0,None,i7181,26,0.010502625656414102 1727442724,1727442753,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.0921184897061867 n_clusters 4,100,0.001,0.0921184897061867,4,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727442724,1727442754,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.06271045894458567 n_clusters 3,100,0.005,0.06271045894458567,3,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727442731,1727442755,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.044856578283365936 n_clusters 1,100,0.25,0.044856578283365936,1,0.28157039259814953,0,None,i7177,20,0.009239489359519367 1727442744,1727442773,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.12942683263229005 n_clusters 4,100,0.005,0.12942683263229005,4,0.2298074518629657,0,None,i7181,25,0.010844816467274714 1727442744,1727442773,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.0034204333029426133 n_clusters 3,100,0.005,0.0034204333029426133,3,0.2378094523630908,0,None,i7186,25,0.010634237506745105 1727442806,1727442810,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727442822,1727442826,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727442846,1727442850,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7181 1727442846,1727442850,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7181 1727442846,1727442850,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 190 confidence 0.001 feature_proportion 0 n_clusters 3,190,0.001,0,3,None,1,None,i7186 1727442822,1727442851,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.05972976549841274 n_clusters 1,100,0.025,0.05972976549841274,1,0.21030257564391097,0,None,i7186,26,0.011358102683565628 1727442822,1727442852,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.06178035295165191 n_clusters 1,100,0.025,0.06178035295165191,1,0.21030257564391097,0,None,i7186,26,0.011358102683565628 1727442826,1727442859,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 224 confidence 0.1 feature_proportion 0.17062696487680676 n_clusters 1,224,0.1,0.17062696487680676,1,0.2728182045511378,0,None,i7181,29,0.02306826706676669 1727442846,1727442876,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 4,100,0.25,0.2,4,0.28157039259814953,0,None,i7181,26,0.009239489359519367 1727442846,1727442879,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 245 confidence 0.25 feature_proportion 0.12981091944130066 n_clusters 2,245,0.25,0.12981091944130066,2,0.2945736434108527,0,None,i7181,29,0.02315578894723681 1727442846,1727442880,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 249 confidence 0.25 feature_proportion 0.2 n_clusters 4,249,0.25,0.2,4,0.2890722680670168,0,None,i7181,29,0.023522547303492534 1727442852,1727442884,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.25 feature_proportion 0.00686604957418456 n_clusters 2,104,0.25,0.00686604957418456,2,0.22280570142535638,0,None,i7181,29,0.01132715611335266 1727442946,1727442950,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727442926,1727442955,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.10835586980082207 n_clusters 1,100,0.01,0.10835586980082207,1,0.21680420105026255,0,None,i7186,25,0.011187007278135324 1727442942,1727442971,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.1057172433586296 n_clusters 1,100,0.025,0.1057172433586296,1,0.21505376344086025,0,None,i7181,25,0.011233071425751173 1727442942,1727442971,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.2 n_clusters 4,100,0.01,0.2,4,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727442942,1727442971,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10632263436215086 n_clusters 1,100,0.025,0.10632263436215086,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727442946,1727442975,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.09377977096828483 n_clusters 4,100,0.025,0.09377977096828483,4,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727442966,1727442995,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.09693556425362757 n_clusters 4,100,0.01,0.09693556425362757,4,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727442966,1727442995,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 4,100,0.001,0.2,4,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727442966,1727442997,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 163 confidence 0.1 feature_proportion 0.014601076067599514 n_clusters 4,163,0.1,0.014601076067599514,4,0.24256064016003998,0,None,i7181,27,0.017363036411276733 1727442966,1727442998,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 215 confidence 0.01 feature_proportion 0.11508659639114759 n_clusters 4,215,0.01,0.11508659639114759,4,0.29232308077019253,0,None,i7186,28,0.020563964520541902 1727443062,1727443065,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443072,1727443076,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443052,1727443082,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.08590986666442471 n_clusters 1,100,0.025,0.08590986666442471,1,0.21030257564391097,0,None,i7186,26,0.011358102683565628 1727443092,1727443095,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727443092,1727443097,5,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 266 confidence 0.001 feature_proportion 0 n_clusters 4,266,0.001,0,4,None,1,None,i7186 1727443072,1727443102,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.08121625754024035 n_clusters 1,100,0.01,0.08121625754024035,1,0.22580645161290325,0,None,i7186,26,0.010950105947539516 1727443092,1727443120,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.09073418590457916 n_clusters 4,100,0.1,0.09073418590457916,4,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727443092,1727443123,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 177 confidence 0.005 feature_proportion 0.06713159217480139 n_clusters 4,177,0.005,0.06713159217480139,4,0.2568142035508877,0,None,i7186,27,0.01833791781278653 1727443112,1727443141,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.12941008866514 n_clusters 4,100,0.005,0.12941008866514,4,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727443112,1727443142,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 confidence 0.25 feature_proportion 0.1284788866967059 n_clusters 3,128,0.25,0.1284788866967059,3,0.23230807701925482,0,None,i7181,26,0.013653413353338334 1727443112,1727443142,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.001 feature_proportion 0.043198331606128994 n_clusters 4,115,0.001,0.043198331606128994,4,0.24481120280070012,0,None,i7186,26,0.012033311358142567 1727443112,1727443142,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 180 confidence 0.05 feature_proportion 0.027794026254146523 n_clusters 4,180,0.05,0.027794026254146523,4,0.27606901725431354,0,None,i7181,27,0.017421021922147204 1727443224,1727443228,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727443242,1727443246,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7181 1727443243,1727443246,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443243,1727443246,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443242,1727443247,5,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 confidence 0.25 feature_proportion 0 n_clusters 3,233,0.25,0,3,None,1,None,i7186 1727443264,1727443268,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727443272,1727443276,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7181 1727443272,1727443277,5,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 257 confidence 0.25 feature_proportion 0 n_clusters 1,257,0.25,0,1,None,1,None,i7186 1727443284,1727443288,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.25 feature_proportion 0 n_clusters 2,183,0.25,0,2,None,1,None,i7186 1727443264,1727443294,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.07760092767138999 n_clusters 1,100,0.025,0.07760092767138999,1,0.21030257564391097,0,None,i7186,26,0.011358102683565628 1727443264,1727443294,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.1478317434272419 n_clusters 4,100,0.001,0.1478317434272419,4,0.2325581395348837,0,None,i7186,26,0.010772429949592661 1727443302,1727443306,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443303,1727443307,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 232 confidence 0.1 feature_proportion 0 n_clusters 2,232,0.1,0,2,None,1,None,i7186 1727443284,1727443312,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.28157039259814953,0,None,i7181,24,0.009239489359519367 1727443437,1727443441,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727443453,1727443457,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 191 confidence 0.25 feature_proportion 0 n_clusters 3,191,0.25,0,3,None,1,None,i7186 1727443477,1727443481,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7181 1727443477,1727443481,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443453,1727443487,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 285 confidence 0.1 feature_proportion 0.08702687067937716 n_clusters 4,285,0.1,0.08702687067937716,4,0.3103275818954738,0,None,i7186,31,0.02550637659414854 1727443497,1727443501,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727443477,1727443508,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 170 confidence 0.1 feature_proportion 0.03572882124900153 n_clusters 3,170,0.1,0.03572882124900153,3,0.3195798949737434,0,None,i7181,27,0.014651390120257339 1727443477,1727443508,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.25 feature_proportion 0.0068724042766892535 n_clusters 2,104,0.25,0.0068724042766892535,2,0.22280570142535638,0,None,i7186,27,0.01132715611335266 1727443483,1727443513,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.025 feature_proportion 0.05071001538600794 n_clusters 4,183,0.025,0.05071001538600794,4,0.2593148287071768,0,None,i7181,27,0.0191297824456114 1727443513,1727443517,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.25 feature_proportion 0 n_clusters 4,250,0.25,0,4,None,1,None,i7186 1727443517,1727443520,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7181 1727443497,1727443529,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 196 confidence 0.05 feature_proportion 0.032618923786967416 n_clusters 3,196,0.05,0.032618923786967416,3,0.26531632908227054,0,None,i7186,28,0.019820744659849173 1727443537,1727443541,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443513,1727443544,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 confidence 0.005 feature_proportion 0.012694664077119819 n_clusters 4,154,0.005,0.012694664077119819,4,0.24931232808202053,0,None,i7186,27,0.016358256230724347 1727443543,1727443546,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727443537,1727443566,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727443537,1727443568,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 193 confidence 0.05 feature_proportion 0.10166964744125272 n_clusters 4,193,0.05,0.10166964744125272,4,0.2623155788947237,0,None,i7186,27,0.019978678880246376 1727443672,1727443676,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443692,1727443696,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443692,1727443696,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443712,1727443716,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727443723,1727443726,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443712,1727443742,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.09829526813441652 n_clusters 4,100,0.05,0.09829526813441652,4,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727443712,1727443744,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 178 confidence 0.005 feature_proportion 0.021807185621937494 n_clusters 4,178,0.005,0.021807185621937494,4,0.26006501625406353,0,None,i7186,28,0.018183117207873394 1727443723,1727443751,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.001 feature_proportion 0.04833185333078999 n_clusters 4,116,0.001,0.04833185333078999,4,0.2225556389097274,0,None,i7181,24,0.01270772238514174 1727443732,1727443763,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.01 feature_proportion 0.01864848556422216 n_clusters 3,169,0.01,0.01864848556422216,3,0.26706676669167295,0,None,i7186,27,0.017038350496715086 1727443772,1727443776,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443752,1727443782,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 confidence 0.1 feature_proportion 0.06489721777010474 n_clusters 4,154,0.1,0.06489721777010474,4,0.24931232808202053,0,None,i7181,26,0.016358256230724347 1727443752,1727443782,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.025 feature_proportion 0.08763265457525676 n_clusters 4,142,0.025,0.08763265457525676,4,0.23555888972243055,0,None,i7186,26,0.015050058810999047 1727443752,1727443785,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 228 confidence 0.01 feature_proportion 0.03944609579512998 n_clusters 4,228,0.01,0.03944609579512998,4,0.2873218304576144,0,None,i7186,29,0.022161790447611903 1727443783,1727443787,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443792,1727443796,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443792,1727443796,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443772,1727443800,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.05616208578758391 n_clusters 4,100,0.1,0.05616208578758391,4,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727443772,1727443802,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 confidence 0.005 feature_proportion 0.012700182286472237 n_clusters 4,154,0.005,0.012700182286472237,4,0.24756189047261812,0,None,i7181,26,0.016431191131116112 1727443920,1727443924,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443933,1727443962,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.08091602944996185 n_clusters 1,100,0.025,0.08091602944996185,1,0.21030257564391097,0,None,i7186,25,0.011358102683565628 1727443960,1727443963,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7181 1727443960,1727443964,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727443940,1727443974,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 256 confidence 0.01 feature_proportion 0.08167138955746325 n_clusters 3,256,0.01,0.08167138955746325,3,0.2928232058014504,0,None,i7186,31,0.02493480512985389 1727443980,1727443984,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727443960,1727443989,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 132 confidence 0.05 feature_proportion 0.1946643989852793 n_clusters 4,132,0.05,0.1946643989852793,4,0.23380845211302825,0,None,i7181,25,0.014072483638150916 1727443993,1727443998,5,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 273 confidence 0.25 feature_proportion 0 n_clusters 4,273,0.25,0,4,None,1,None,i7186 1727443980,1727444011,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 149 confidence 0.05 feature_proportion 0.11571777286126102 n_clusters 4,149,0.05,0.11571777286126102,4,0.24931232808202053,0,None,i7186,27,0.01570392598149537 1727443993,1727444024,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 161 confidence 0.005 feature_proportion 0.07235030395993888 n_clusters 4,161,0.005,0.07235030395993888,4,0.253313328332083,0,None,i7186,27,0.01689552822988356 1727444000,1727444029,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.001 feature_proportion 0.04323217947365169 n_clusters 4,115,0.001,0.04323217947365169,4,0.2583145786446611,0,None,i7181,25,0.012786529965824791 1727444040,1727444043,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 4,100,0.1,0,4,None,1,None,i7181 1727444040,1727444043,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727444040,1727444044,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727444020,1727444049,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.08108673541270273 n_clusters 1,100,0.025,0.08108673541270273,1,0.21030257564391097,0,None,i7186,25,0.011358102683565628 1727444020,1727444050,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.03360432322736148 n_clusters 4,100,0.1,0.03360432322736148,4,0.28157039259814953,0,None,i7186,26,0.009239489359519367 1727444060,1727444064,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727444053,1727444084,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 198 confidence 0.01 feature_proportion 0.054554901597087306 n_clusters 4,198,0.01,0.054554901597087306,4,0.2800700175043761,0,None,i7181,27,0.019044234742896248 1727444191,1727444221,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 117 confidence 0.025 feature_proportion 0.07645801505157195 n_clusters 1,117,0.025,0.07645801505157195,1,0.22305576394098525,0,None,i7186,26,0.01269256708116423 1727444204,1727444234,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.01 feature_proportion 0.07579457739503703 n_clusters 1,112,0.01,0.07579457739503703,1,0.2370592648162041,0,None,i7186,26,0.012268218569793961 1727444211,1727444240,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 113 confidence 0.01 feature_proportion 0.07827742721999709 n_clusters 1,113,0.01,0.07827742721999709,1,0.21880470117529383,0,None,i7186,26,0.012821387164973061 1727444231,1727444260,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.01 feature_proportion 0.07780824193650142 n_clusters 1,111,0.01,0.07780824193650142,1,0.22930732683170796,0,None,i7181,25,0.012135386787873438 1727444231,1727444260,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.01 feature_proportion 0.08203324982953192 n_clusters 1,112,0.01,0.08203324982953192,1,0.2370592648162041,0,None,i7181,25,0.012268218569793961 1727444231,1727444261,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.025 feature_proportion 0.07667772663999667 n_clusters 1,111,0.025,0.07667772663999667,1,0.2145536384096024,0,None,i7186,26,0.012210195405994356 1727444251,1727444281,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.025 feature_proportion 0.07993414230480252 n_clusters 1,112,0.025,0.07993414230480252,1,0.24381095273818454,0,None,i7186,25,0.011708809555330008 1727444251,1727444281,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.01 feature_proportion 0.07860665809736578 n_clusters 1,111,0.01,0.07860665809736578,1,0.22930732683170796,0,None,i7186,26,0.012135386787873438 1727444263,1727444287,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.09397691201113999 n_clusters 3,100,0.005,0.09397691201113999,3,0.28157039259814953,0,None,i7177,20,0.009239489359519367 1727444263,1727444287,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.07387397543403507 n_clusters 1,100,0.25,0.07387397543403507,1,0.28157039259814953,0,None,i7177,21,0.009239489359519367 1727444291,1727444321,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 110 confidence 0.025 feature_proportion 0.07767185857705741 n_clusters 1,110,0.025,0.07767185857705741,1,0.2198049512378094,0,None,i7186,26,0.012060157896617012 1727444291,1727444327,36,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 368 confidence 0.05 feature_proportion 0.06699004541965811 n_clusters 1,368,0.05,0.06699004541965811,1,0.3205801450362591,0,None,i7186,32,0.035703370287016194 1727444324,1727444327,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727444311,1727444340,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.025 feature_proportion 0.08094003475978388 n_clusters 1,111,0.025,0.08094003475978388,1,0.2145536384096024,0,None,i7181,25,0.012210195405994356 1727444311,1727444341,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.01 feature_proportion 0.07828620101934312 n_clusters 1,112,0.01,0.07828620101934312,1,0.2370592648162041,0,None,i7186,26,0.012268218569793961 1727444324,1727444353,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.01 feature_proportion 0.07893192970214852 n_clusters 1,112,0.01,0.07893192970214852,1,0.2370592648162041,0,None,i7181,25,0.012268218569793961 1727444331,1727444361,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.025 feature_proportion 0.07848861678730398 n_clusters 1,112,0.025,0.07848861678730398,1,0.24381095273818454,0,None,i7186,26,0.011708809555330008 1727444351,1727444383,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 201 confidence 0.025 feature_proportion 0.04880628092606461 n_clusters 3,201,0.025,0.04880628092606461,3,0.2738184546136534,0,None,i7186,28,0.02044955683365286 1727444474,1727444503,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.09056123385347958 n_clusters 1,100,0.025,0.09056123385347958,1,0.21030257564391097,0,None,i7186,25,0.011358102683565628 1727444504,1727444508,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.001 feature_proportion 0 n_clusters 3,153,0.001,0,3,None,1,None,i7186 1727444525,1727444529,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.001 feature_proportion 0 n_clusters 3,153,0.001,0,3,None,1,None,i7186 1727444504,1727444533,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.08871484946421278 n_clusters 1,100,0.025,0.08871484946421278,1,0.21030257564391097,0,None,i7181,25,0.011358102683565628 1727444504,1727444534,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.09126501188188944 n_clusters 1,100,0.025,0.09126501188188944,1,0.21030257564391097,0,None,i7186,25,0.011358102683565628 1727444525,1727444554,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.08912512824858032 n_clusters 1,100,0.025,0.08912512824858032,1,0.21030257564391097,0,None,i7186,25,0.011358102683565628 1727444534,1727444564,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 105 confidence 0.005 feature_proportion 0.03391471020990066 n_clusters 3,105,0.005,0.03391471020990066,3,0.2308077019254814,0,None,i7186,26,0.011419521547053429 1727444545,1727444574,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.08488583004358517 n_clusters 1,100,0.025,0.08488583004358517,1,0.21030257564391097,0,None,i7186,25,0.011358102683565628 1727444585,1727444589,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.001 feature_proportion 0 n_clusters 3,151,0.001,0,3,None,1,None,i7186 1727444565,1727444596,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 184 confidence 0.001 feature_proportion 0.2 n_clusters 4,184,0.001,0.2,4,0.2583145786446611,0,None,i7186,28,0.019179794948737186 1727444564,1727444608,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 1000 confidence 0.25 feature_proportion 0.2 n_clusters 4,1000,0.25,0.2,4,0.41335333833458365,0,None,i7181,40,0.07618571309494039 1727444585,1727444631,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 1000 confidence 0.025 feature_proportion 0.06918470804146085 n_clusters 3,1000,0.025,0.06918470804146085,3,0.41335333833458365,0,None,i7186,42,0.07618571309494039 1727444605,1727444636,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 168 confidence 0.25 feature_proportion 0.13926624060892004 n_clusters 4,168,0.25,0.13926624060892004,4,0.27231807951987996,0,None,i7186,27,0.01679965445906931 1727444595,1727444639,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 967 confidence 0.025 feature_proportion 0.0006486554217567504 n_clusters 4,967,0.025,0.0006486554217567504,4,0.3988497124281071,0,None,i7186,41,0.08102025506376592 1727444625,1727444655,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 184 confidence 0.001 feature_proportion 0.2 n_clusters 3,184,0.001,0.2,3,0.2628157039259815,0,None,i7181,27,0.018954738684671166 1727444625,1727444658,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 297 confidence 0.05 feature_proportion 0.08675517461145292 n_clusters 3,297,0.05,0.08675517461145292,3,0.3050762690672668,0,None,i7181,30,0.028069517379344835 1727444645,1727444685,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 605 confidence 0.005 feature_proportion 0.05804595116573999 n_clusters 2,605,0.005,0.05804595116573999,2,0.37659414853713424,0,None,i7186,37,0.05306326581645412 1727444770,1727444801,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.1 feature_proportion 0.2 n_clusters 3,169,0.1,0.2,3,0.26706676669167295,0,None,i7186,27,0.017038350496715086 1727444805,1727444809,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.001 feature_proportion 0 n_clusters 3,139,0.001,0,3,None,1,None,i7186 1727444790,1727444824,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 288 confidence 0.1 feature_proportion 0.2 n_clusters 4,288,0.1,0.2,4,0.30732683170792696,0,None,i7186,31,0.027881970492623157 1727444805,1727444836,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 167 confidence 0.25 feature_proportion 0.16325117127172328 n_clusters 4,167,0.25,0.16325117127172328,4,0.26606651662915726,0,None,i7186,27,0.017083816408647617 1727444810,1727444840,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 163 confidence 0.05 feature_proportion 0.2 n_clusters 4,163,0.05,0.2,4,0.24256064016003998,0,None,i7181,26,0.017363036411276733 1727444830,1727444862,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 166 confidence 0.25 feature_proportion 0.2 n_clusters 4,166,0.25,0.2,4,0.2638159539884971,0,None,i7186,28,0.016438892331778598 1727444830,1727444862,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 172 confidence 0.025 feature_proportion 0.2 n_clusters 3,172,0.025,0.2,3,0.2665666416604151,0,None,i7186,28,0.017061083452681352 1727444850,1727444881,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 160 confidence 0.01 feature_proportion 0.07707905410286836 n_clusters 3,160,0.01,0.07707905410286836,3,0.30832708177044266,0,None,i7186,27,0.013899308160373424 1727444850,1727444885,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 363 confidence 0.001 feature_proportion 0.2 n_clusters 4,363,0.001,0.2,4,0.31357839459864967,0,None,i7181,31,0.03283320830207552 1727444865,1727444897,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 249 confidence 0.025 feature_proportion 0.2 n_clusters 3,249,0.025,0.2,3,0.2890722680670168,0,None,i7181,29,0.023522547303492534 1727444870,1727444904,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 305 confidence 0.05 feature_proportion 0.0929484409170756 n_clusters 3,305,0.05,0.0929484409170756,3,0.3248312078019505,0,None,i7186,31,0.02642327248478786 1727444890,1727444924,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 299 confidence 0.01 feature_proportion 0.19677303690741987 n_clusters 3,299,0.01,0.19677303690741987,3,0.30582645661415353,0,None,i7186,31,0.028007001750437608 1727444895,1727444928,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 219 confidence 0.01 feature_proportion 0.1771319505511164 n_clusters 3,219,0.01,0.1771319505511164,3,0.27956989247311825,0,None,i7186,29,0.02131415206742862 1727444910,1727444941,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 166 confidence 0.25 feature_proportion 0.2 n_clusters 3,166,0.25,0.2,3,0.2638159539884971,0,None,i7186,27,0.016438892331778598 1727445098,1727445102,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445098,1727445102,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727445106,1727445109,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445118,1727445122,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.001 feature_proportion 0 n_clusters 3,156,0.001,0,3,None,1,None,i7186 1727445138,1727445142,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.001 feature_proportion 0 n_clusters 3,156,0.001,0,3,None,1,None,i7186 1727445158,1727445162,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727445158,1727445162,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 feature_proportion 0 n_clusters 3,138,0.001,0,3,None,1,None,i7186 1727445136,1727445165,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10187622095268106 n_clusters 1,100,0.025,0.10187622095268106,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727445196,1727445200,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 147 confidence 0.001 feature_proportion 0 n_clusters 3,147,0.001,0,3,None,1,None,i7186 1727445178,1727445208,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.0379942938681491 n_clusters 4,100,0.001,0.0379942938681491,4,0.22555638909727427,0,None,i7186,27,0.011895831100632302 1727445218,1727445222,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445196,1727445226,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 124 confidence 0.1 feature_proportion 0.04701724131289936 n_clusters 3,124,0.1,0.04701724131289936,3,0.22380595148787197,0,None,i7186,26,0.013487242778436544 1727445238,1727445242,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445218,1727445249,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.09192445089051443 n_clusters 3,100,0.01,0.09192445089051443,3,0.28157039259814953,0,None,i7186,27,0.009239489359519367 1727445226,1727445255,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.001 feature_proportion 0.041915728710977984 n_clusters 3,115,0.001,0.041915728710977984,3,0.22480620155038755,0,None,i7181,25,0.01303450862715679 1727445256,1727445285,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 113 confidence 0.001 feature_proportion 0.042060731604721215 n_clusters 4,113,0.001,0.042060731604721215,4,0.2163040760190047,0,None,i7186,25,0.01289716368486061 1727445449,1727445453,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445489,1727445492,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727445489,1727445493,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727445467,1727445497,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 confidence 0.005 feature_proportion 0.08929608715572572 n_clusters 4,126,0.005,0.08929608715572572,4,0.23180795198799697,0,None,i7186,26,0.013670084187713595 1727445467,1727445498,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 148 confidence 0.005 feature_proportion 0.10654924657877698 n_clusters 4,148,0.005,0.10654924657877698,4,0.2550637659414854,0,None,i7186,27,0.014878719679919978 1727445497,1727445500,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7181 1727445509,1727445513,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727445527,1727445530,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727445549,1727445553,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727445569,1727445573,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 3,100,0.05,0,3,None,1,None,i7186 1727445549,1727445579,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.002774243316007355 n_clusters 4,100,0.005,0.002774243316007355,4,0.22955738934733683,0,None,i7186,26,0.010851397059791263 1727445557,1727445602,45,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 873 confidence 0.001 feature_proportion 0.06520371735440532 n_clusters 4,873,0.001,0.06520371735440532,4,0.40510127531882967,0,None,i7186,42,0.07893640076685839 1727445609,1727445613,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 120 confidence 0.01 feature_proportion 0 n_clusters 3,120,0.01,0,3,None,1,None,i7186 1727445587,1727445616,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 106 confidence 0.25 feature_proportion 0.028573333614170374 n_clusters 2,106,0.25,0.028573333614170374,2,0.23305826456614154,0,None,i7186,25,0.011357005918146203 1727445617,1727445620,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727445609,1727445640,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.005 feature_proportion 0.12579387986330787 n_clusters 4,140,0.005,0.12579387986330787,4,0.3078269567391848,0,None,i7186,27,0.012373463736304446 1727445629,1727445668,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 516 confidence 0.005 feature_proportion 0.2 n_clusters 4,516,0.005,0.2,4,0.3545886471617904,0,None,i7186,35,0.04788697174293574 1727445833,1727445837,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445873,1727445877,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445853,1727445882,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.08514913352158021 n_clusters 2,100,0.01,0.08514913352158021,2,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727445887,1727445891,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727445873,1727445903,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 132 confidence 0.01 feature_proportion 0.1887448134392551 n_clusters 3,132,0.01,0.1887448134392551,3,0.23380845211302825,0,None,i7186,26,0.014072483638150916 1727445913,1727445917,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727445913,1727445917,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727445933,1727445963,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.03813871148182196 n_clusters 4,100,0.001,0.03813871148182196,4,0.28232058014503625,0,None,i7186,26,0.010273997070696246 1727445948,1727445977,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.001 feature_proportion 0.05889865622407541 n_clusters 3,112,0.001,0.05889865622407541,3,0.22555638909727427,0,None,i7186,26,0.012245708485945016 1727445948,1727445982,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 298 confidence 0.025 feature_proportion 0.1811263454118956 n_clusters 3,298,0.025,0.1811263454118956,3,0.30307576894223553,0,None,i7186,31,0.028236225723097443 1727445973,1727446004,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 186 confidence 0.25 feature_proportion 0.2 n_clusters 4,186,0.25,0.2,4,0.2568142035508877,0,None,i7186,27,0.01925481370342586 1727446008,1727446014,6,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7181 1727446008,1727446014,6,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727445993,1727446025,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 204 confidence 0.01 feature_proportion 0.1929008064792729 n_clusters 3,204,0.01,0.1929008064792729,3,0.28232058014503625,0,None,i7186,28,0.01997721652635381 1727445993,1727446026,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 207 confidence 0.01 feature_proportion 0.07681511623198253 n_clusters 4,207,0.01,0.07681511623198253,4,0.25456364091022754,0,None,i7186,29,0.021519268706065405 1727446033,1727446037,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727446038,1727446068,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 125 confidence 0.025 feature_proportion 0.2 n_clusters 4,125,0.025,0.2,4,0.2495623905976494,0,None,i7186,26,0.012656389903927595 1727446279,1727446282,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446293,1727446297,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446309,1727446312,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727446333,1727446337,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446333,1727446337,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727446369,1727446373,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727446369,1727446373,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727446353,1727446388,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 314 confidence 0.01 feature_proportion 0.1775136769641642 n_clusters 1,314,0.01,0.1775136769641642,1,0.3223305826456614,0,None,i7186,31,0.029052717724885768 1727446399,1727446407,8,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446399,1727446433,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 107 confidence 0.001 feature_proportion 0.06355306894224319 n_clusters 4,107,0.001,0.06355306894224319,4,0.22280570142535638,0,None,i7186,25,0.011641799338723568 1727446430,1727446433,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727446430,1727446459,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 113 confidence 0.001 feature_proportion 0.04206575535834268 n_clusters 4,113,0.001,0.04206575535834268,4,0.21605401350337583,0,None,i7186,26,0.012904741336849363 1727446460,1727446490,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.10740642102538484 n_clusters 4,100,0.01,0.10740642102538484,4,0.28157039259814953,0,None,i7186,26,0.009239489359519367 1727446460,1727446491,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 164 confidence 0.025 feature_proportion 0.15883179551402898 n_clusters 3,164,0.025,0.15883179551402898,3,0.25431357839459867,0,None,i7186,27,0.016852039096730703 1727446490,1727446493,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446490,1727446494,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7181 1727446520,1727446523,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727446490,1727446530,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 555 confidence 0.005 feature_proportion 0.10517719135263054 n_clusters 1,555,0.005,0.10517719135263054,1,0.3680920230057514,0,None,i7181,36,0.05476369092273069 1727446761,1727446790,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 120 confidence 0.25 feature_proportion 0.017551693754480385 n_clusters 2,120,0.25,0.017551693754480385,2,0.2818204551137784,0,None,i7186,25,0.011252813203300826 1727446791,1727446794,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 confidence 0.001 feature_proportion 0 n_clusters 4,154,0.001,0,4,None,1,None,i7181 1727446791,1727446794,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 103 confidence 0.001 feature_proportion 0 n_clusters 4,103,0.001,0,4,None,1,None,i7186 1727446791,1727446795,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 210 confidence 0.25 feature_proportion 0 n_clusters 1,210,0.25,0,1,None,1,None,i7181 1727446821,1727446824,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727446821,1727446824,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727446851,1727446855,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446851,1727446881,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.097343657728073 n_clusters 1,100,0.025,0.097343657728073,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727446881,1727446885,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.001 feature_proportion 0 n_clusters 3,115,0.001,0,3,None,1,None,i7186 1727446881,1727446909,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.08026921615616117 n_clusters 3,100,0.1,0.08026921615616117,3,0.28157039259814953,0,None,i7181,25,0.009239489359519367 1727446911,1727446915,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727446941,1727446945,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 confidence 0.25 feature_proportion 0 n_clusters 4,133,0.25,0,4,None,1,None,i7181 1727446911,1727446946,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 295 confidence 0.001 feature_proportion 0.10862032147583581 n_clusters 4,295,0.001,0.10862032147583581,4,0.3198299574893724,0,None,i7186,30,0.02684004334416937 1727446941,1727446973,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 178 confidence 0.1 feature_proportion 0.18682531723115406 n_clusters 2,178,0.1,0.18682531723115406,2,0.26006501625406353,0,None,i7186,27,0.018183117207873394 1727446972,1727446975,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.005 feature_proportion 0 n_clusters 3,101,0.005,0,3,None,1,None,i7186 1727446971,1727447000,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 114 confidence 0.25 feature_proportion 0.0008665495849945719 n_clusters 2,114,0.25,0.0008665495849945719,2,0.22280570142535638,0,None,i7181,24,0.012326611064530837 1727447002,1727447005,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 3,100,0.001,0,3,None,1,None,i7186 1727447213,1727447217,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727447213,1727447217,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727447243,1727447247,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447243,1727447274,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 180 confidence 0.05 feature_proportion 0.2 n_clusters 4,180,0.05,0.2,4,0.30182545636409097,0,None,i7186,27,0.016194524821681613 1727447253,1727447287,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 264 confidence 0.25 feature_proportion 0.2 n_clusters 3,264,0.25,0.2,3,0.2943235808952238,0,None,i7186,30,0.024827635480298645 1727447293,1727447297,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.001 feature_proportion 0 n_clusters 3,104,0.001,0,3,None,1,None,i7186 1727447273,1727447302,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 102 confidence 0.001 feature_proportion 0.1045517870200074 n_clusters 2,102,0.001,0.1045517870200074,2,0.23555888972243055,0,None,i7181,25,0.01069346283939406 1727447313,1727447317,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.001 feature_proportion 0 n_clusters 3,116,0.001,0,3,None,1,None,i7186 1727447303,1727447333,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.09893702788492606 n_clusters 1,100,0.025,0.09893702788492606,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727447333,1727447337,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447353,1727447357,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447364,1727447367,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727447393,1727447397,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447394,1727447398,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727447424,1727447428,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 131 confidence 0.001 feature_proportion 0 n_clusters 3,131,0.001,0,3,None,1,None,i7186 1727447413,1727447443,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.03773204184468149 n_clusters 3,100,0.001,0.03773204184468149,3,0.26331582895723926,0,None,i7186,26,0.01023228780168015 1727447454,1727447458,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447666,1727447669,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447696,1727447699,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727447696,1727447700,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727447726,1727447730,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447726,1727447755,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.03820985808088803 n_clusters 4,100,0.001,0.03820985808088803,4,0.22480620155038755,0,None,i7186,25,0.010976428317605718 1727447756,1727447760,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727447756,1727447760,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447786,1727447790,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447816,1727447820,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7181 1727447817,1727447820,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447817,1727447850,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 227 confidence 0.025 feature_proportion 0.07460144092084556 n_clusters 4,227,0.025,0.07460144092084556,4,0.28157039259814953,0,None,i7186,29,0.022521255313828457 1727447847,1727447851,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447877,1727447883,6,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 442 confidence 0.005 feature_proportion 0 n_clusters 1,442,0.005,0,1,None,1,None,i7186 1727447907,1727447910,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447907,1727447911,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727447877,1727447918,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 698 confidence 0.025 feature_proportion 0.07501925322211386 n_clusters 4,698,0.025,0.07501925322211386,4,0.3960990247561891,0,None,i7186,37,0.06145286321580394 1727447937,1727447941,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727447937,1727447967,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.038009937163653994 n_clusters 4,100,0.001,0.038009937163653994,4,0.23455863965991497,0,None,i7186,26,0.01163862394169971 1727448149,1727448152,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448209,1727448213,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448209,1727448213,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7181 1727448209,1727448213,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727448179,1727448217,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 492 confidence 0.005 feature_proportion 0.1103388634985541 n_clusters 1,492,0.005,0.1103388634985541,1,0.3508377094273568,0,None,i7186,35,0.04158182402743543 1727448270,1727448273,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448270,1727448273,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727448239,1727448281,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 708 confidence 0.025 feature_proportion 0.07682291084106277 n_clusters 4,708,0.025,0.07682291084106277,4,0.3948487121780445,0,None,i7186,38,0.06176544136034008 1727448300,1727448304,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 2,100,0.001,0,2,None,1,None,i7186 1727448301,1727448304,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448330,1727448334,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448360,1727448364,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448330,1727448365,35,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 309 confidence 0.001 feature_proportion 0.2 n_clusters 2,309,0.001,0.2,2,0.3078269567391848,0,None,i7186,31,0.030371229170929093 1727448391,1727448394,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448414,1727448417,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448391,1727448429,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 438 confidence 0.25 feature_proportion 0.08914945729013135 n_clusters 2,438,0.25,0.08914945729013135,2,0.3383345836459115,0,None,i7186,34,0.037946986746686666 1727448413,1727448456,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 822 confidence 0.025 feature_proportion 0.2 n_clusters 3,822,0.025,0.2,3,0.40360090022505624,0,None,i7181,39,0.0794365257981162 1727448663,1727448666,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448693,1727448697,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448694,1727448698,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448723,1727448727,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448754,1727448758,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 242 confidence 0.001 feature_proportion 0 n_clusters 2,242,0.001,0,2,None,1,None,i7186 1727448784,1727448787,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448784,1727448791,7,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7181 1727448755,1727448796,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 757 confidence 0.025 feature_proportion 0.19995925202331122 n_clusters 1,757,0.025,0.19995925202331122,1,0.3968492123030758,0,None,i7186,38,0.06126531632908226 1727448814,1727448818,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448814,1727448818,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448844,1727448873,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.0036442332716016598 n_clusters 4,100,0.005,0.0036442332716016598,4,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727448875,1727448879,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448875,1727448904,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 123 confidence 0.001 feature_proportion 0.04916143368687999 n_clusters 4,123,0.001,0.04916143368687999,4,0.21430357589397353,0,None,i7181,25,0.01379377102340101 1727448905,1727448909,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 4,100,0.001,0,4,None,1,None,i7186 1727448905,1727448909,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727448935,1727448964,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.0027850436149377713 n_clusters 4,100,0.005,0.0027850436149377713,4,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727448965,1727448999,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 253 confidence 0.025 feature_proportion 0.1036926123606728 n_clusters 4,253,0.025,0.1036926123606728,4,0.2988247061765441,0,None,i7186,29,0.02450612653163291 1727449189,1727449193,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449208,1727449211,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449229,1727449233,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 127 confidence 0.001 feature_proportion 0 n_clusters 4,127,0.001,0,4,None,1,None,i7186 1727449249,1727449253,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449268,1727449272,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 confidence 0.001 feature_proportion 0 n_clusters 4,128,0.001,0,4,None,1,None,i7186 1727449289,1727449293,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449329,1727449332,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 confidence 0.001 feature_proportion 0 n_clusters 4,126,0.001,0,4,None,1,None,i7186 1727449309,1727449339,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.038014962924231455 n_clusters 4,100,0.001,0.038014962924231455,4,0.22555638909727427,0,None,i7186,26,0.011252813203300826 1727449349,1727449353,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449359,1727449363,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449409,1727449413,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 212 confidence 0.25 feature_proportion 0 n_clusters 4,212,0.25,0,4,None,1,None,i7181 1727449389,1727449421,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 205 confidence 0.05 feature_proportion 0.2 n_clusters 4,205,0.05,0.2,4,0.2943235808952238,0,None,i7186,28,0.01931038315134339 1727449429,1727449433,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449409,1727449439,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 113 confidence 0.001 feature_proportion 0.029293654769387195 n_clusters 4,113,0.001,0.029293654769387195,4,0.22605651412853212,0,None,i7186,26,0.012995436359089773 1727449449,1727449453,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 confidence 0.001 feature_proportion 0 n_clusters 4,128,0.001,0,4,None,1,None,i7186 1727449489,1727449493,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449469,1727449499,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10207777783894714 n_clusters 1,100,0.025,0.10207777783894714,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727449781,1727449784,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449809,1727449813,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727449811,1727449814,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449829,1727449860,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 feature_proportion 0.045118079302409624 n_clusters 2,138,0.001,0.045118079302409624,2,0.2440610152538134,0,None,i7186,27,0.015913978494623657 1727449869,1727449873,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.001 feature_proportion 0 n_clusters 4,139,0.001,0,4,None,1,None,i7186 1727449849,1727449880,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.25 feature_proportion 0.10208072721818438 n_clusters 4,137,0.25,0.10208072721818438,4,0.23355838959739939,0,None,i7186,26,0.014584003143643052 1727449890,1727449893,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.001 feature_proportion 0 n_clusters 4,139,0.001,0,4,None,1,None,i7186 1727449910,1727449943,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.025 feature_proportion 0.2 n_clusters 4,250,0.025,0.2,4,0.3285821455363841,0,None,i7186,29,0.020888555472201382 1727449950,1727449953,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.001 feature_proportion 0 n_clusters 1,169,0.001,0,1,None,1,None,i7186 1727449930,1727449959,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.005 feature_proportion 0.05909471202550626 n_clusters 4,104,0.005,0.05909471202550626,4,0.22280570142535638,0,None,i7186,25,0.01132715611335266 1727449990,1727449993,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727449961,1727450000,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 520 confidence 0.005 feature_proportion 0.11586252837600441 n_clusters 1,520,0.005,0.11586252837600441,1,0.37209302325581395,0,None,i7186,35,0.04496957572726515 1727450010,1727450042,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 226 confidence 0.01 feature_proportion 0.18768983472426465 n_clusters 2,226,0.01,0.18768983472426465,2,0.28782195548887224,0,None,i7186,28,0.022130532633158288 1727450050,1727450053,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450051,1727450055,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.001 feature_proportion 0 n_clusters 4,139,0.001,0,4,None,1,None,i7181 1727450021,1727450061,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 514 confidence 0.005 feature_proportion 0.11720355220053252 n_clusters 1,514,0.005,0.11720355220053252,1,0.35608902225556394,0,None,i7186,35,0.04763690922730682 1727450082,1727450111,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.005 feature_proportion 0.05909067098173988 n_clusters 4,104,0.005,0.05909067098173988,4,0.22280570142535638,0,None,i7186,25,0.01132715611335266 1727450110,1727450113,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450330,1727450333,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727450370,1727450373,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450384,1727450388,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 confidence 0.001 feature_proportion 0 n_clusters 4,135,0.001,0,4,None,1,None,i7186 1727450390,1727450393,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727450430,1727450434,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 feature_proportion 0 n_clusters 4,142,0.001,0,4,None,1,None,i7186 1727450444,1727450448,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727450450,1727450453,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450475,1727450505,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 136 confidence 0.005 feature_proportion 0.04707544606560945 n_clusters 4,136,0.005,0.04707544606560945,4,0.23580895223805953,0,None,i7186,26,0.014503625906476619 1727450505,1727450508,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450510,1727450513,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727450535,1727450538,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450565,1727450569,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450590,1727450594,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.001 feature_proportion 0 n_clusters 4,139,0.001,0,4,None,1,None,i7186 1727450595,1727450626,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.05 feature_proportion 0.13753925722941432 n_clusters 4,138,0.05,0.13753925722941432,4,0.23480870217554384,0,None,i7186,27,0.015077843534957815 1727450626,1727450629,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 confidence 0.001 feature_proportion 0 n_clusters 4,143,0.001,0,4,None,1,None,i7186 1727450650,1727450654,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 confidence 0.001 feature_proportion 0 n_clusters 4,143,0.001,0,4,None,1,None,i7186 1727450950,1727450954,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727450928,1727450957,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 105 confidence 0.005 feature_proportion 0.1743452848290286 n_clusters 3,105,0.005,0.1743452848290286,3,0.2703175793948487,0,None,i7186,25,0.010043051303366383 1727450950,1727450991,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 680 confidence 0.005 feature_proportion 0.1288800087899462 n_clusters 3,680,0.005,0.1288800087899462,3,0.39709927481870466,0,None,i7186,37,0.061202800700175045 1727450990,1727450994,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.001 feature_proportion 0 n_clusters 4,141,0.001,0,4,None,1,None,i7186 1727450970,1727451000,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 confidence 0.005 feature_proportion 0.006174432960647996 n_clusters 3,133,0.005,0.006174432960647996,3,0.23280820205051267,0,None,i7186,26,0.014610795556031864 1727451010,1727451050,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 633 confidence 0.001 feature_proportion 0.16481279956701544 n_clusters 3,633,0.001,0.16481279956701544,3,0.3770942735683921,0,None,i7186,36,0.05296324081020255 1727451048,1727451052,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 2,100,0.001,0,2,None,1,None,i7186 1727451070,1727451074,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 confidence 0.001 feature_proportion 0 n_clusters 4,146,0.001,0,4,None,1,None,i7186 1727451070,1727451104,34,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 301 confidence 0.05 feature_proportion 0.15417974028012 n_clusters 3,301,0.05,0.15417974028012,3,0.29407351837959494,0,None,i7186,30,0.02898641326998416 1727451108,1727451113,5,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7181 1727451150,1727451154,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 confidence 0.005 feature_proportion 0 n_clusters 3,233,0.005,0,3,None,1,None,i7186 1727451130,1727451171,41,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 656 confidence 0.005 feature_proportion 0.11059872241287948 n_clusters 3,656,0.005,0.11059872241287948,3,0.3753438359589898,0,None,i7186,38,0.05331332833208301 1727451169,1727451173,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.005 feature_proportion 0 n_clusters 4,230,0.005,0,4,None,1,None,i7186 1727451190,1727451194,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.001 feature_proportion 0 n_clusters 4,137,0.001,0,4,None,1,None,i7186 1727451210,1727451214,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727451250,1727451254,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727451229,1727451259,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10073499518622714 n_clusters 1,100,0.025,0.10073499518622714,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727451530,1727451534,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.001 feature_proportion 0 n_clusters 4,144,0.001,0,4,None,1,None,i7186 1727451561,1727451565,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 feature_proportion 0 n_clusters 4,142,0.001,0,4,None,1,None,i7186 1727451590,1727451623,33,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 confidence 0.005 feature_proportion 0.01695403110662542 n_clusters 4,233,0.005,0.01695403110662542,4,0.28582145536384096,0,None,i7186,29,0.022255563890972743 1727451621,1727451625,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727451630,1727451634,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727451610,1727451640,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 113 confidence 0.001 feature_proportion 0.029316033192138925 n_clusters 4,113,0.001,0.029316033192138925,4,0.22230557639409854,0,None,i7186,26,0.012715300037130494 1727451670,1727451674,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 145 confidence 0.001 feature_proportion 0 n_clusters 4,145,0.001,0,4,None,1,None,i7186 1727451681,1727451685,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727451730,1727451735,5,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7186 1727451710,1727451741,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 148 confidence 0.1 feature_proportion 0.021914020900722232 n_clusters 3,148,0.1,0.021914020900722232,3,0.2550637659414854,0,None,i7186,27,0.014878719679919978 1727451742,1727451745,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.001 feature_proportion 0 n_clusters 4,141,0.001,0,4,None,1,None,i7186 1727451770,1727451774,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 confidence 0.001 feature_proportion 0 n_clusters 4,146,0.001,0,4,None,1,None,i7186 1727451790,1727451834,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 798 confidence 0.001 feature_proportion 0.14513611296654816 n_clusters 2,798,0.001,0.14513611296654816,2,0.4181045261315329,0,None,i7186,40,0.05595148787196798 1727451830,1727451834,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727451810,1727451840,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.0105297772225084 n_clusters 3,100,0.05,0.0105297772225084,3,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727451850,1727451854,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 feature_proportion 0 n_clusters 3,142,0.001,0,3,None,1,None,i7186 1727451891,1727451894,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727452131,1727452162,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 161 confidence 0.1 feature_proportion 0.2 n_clusters 4,161,0.1,0.2,4,0.253313328332083,0,None,i7186,27,0.01689552822988356 1727452166,1727452170,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.001 feature_proportion 0 n_clusters 4,137,0.001,0,4,None,1,None,i7186 1727452211,1727452214,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 confidence 0.001 feature_proportion 0 n_clusters 4,139,0.001,0,4,None,1,None,i7186 1727452191,1727452221,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 confidence 0.005 feature_proportion 0.04363083212272471 n_clusters 3,146,0.005,0.04363083212272471,3,0.25356339084771196,0,None,i7186,26,0.01493642641429588 1727452226,1727452230,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727452251,1727452255,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 3,140,0.001,0,3,None,1,None,i7186 1727452271,1727452275,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 165 confidence 0.01 feature_proportion 0 n_clusters 4,165,0.01,0,4,None,1,None,i7186 1727452291,1727452294,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.001 feature_proportion 0 n_clusters 4,141,0.001,0,4,None,1,None,i7186 1727452317,1727452321,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.001 feature_proportion 0 n_clusters 4,137,0.001,0,4,None,1,None,i7186 1727452347,1727452351,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727452371,1727452374,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 confidence 0.001 feature_proportion 0 n_clusters 4,135,0.001,0,4,None,1,None,i7186 1727452391,1727452394,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727452431,1727452434,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727452408,1727452438,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 159 confidence 0.25 feature_proportion 0.2 n_clusters 4,159,0.25,0.2,4,0.26331582895723926,0,None,i7186,27,0.015774777027590232 1727452451,1727452480,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.01 feature_proportion 0.2 n_clusters 4,115,0.01,0.2,4,0.24981245311327827,0,None,i7186,25,0.011881758318367472 1727452491,1727452494,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 149 confidence 0.001 feature_proportion 0 n_clusters 2,149,0.001,0,2,None,1,None,i7186 1727452791,1727452795,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 4,140,0.001,0,4,None,1,None,i7186 1727452811,1727452840,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 108 confidence 0.01 feature_proportion 0.05516542353435146 n_clusters 4,108,0.01,0.05516542353435146,4,0.23755938984746183,0,None,i7186,25,0.01123197466033175 1727452851,1727452855,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.001 feature_proportion 0 n_clusters 4,137,0.001,0,4,None,1,None,i7186 1727452890,1727452894,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.001 feature_proportion 0 n_clusters 4,137,0.001,0,4,None,1,None,i7186 1727452851,1727452894,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 828 confidence 0.05 feature_proportion 0.1049878383241407 n_clusters 3,828,0.05,0.1049878383241407,3,0.40985246311577894,0,None,i7186,40,0.07735267150120863 1727452911,1727452940,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.01 feature_proportion 0.18492489155741043 n_clusters 4,111,0.01,0.18492489155741043,4,0.2180545136284071,0,None,i7186,25,0.012110170399742793 1727452951,1727452955,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727452931,1727452960,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.001 feature_proportion 0.05655761716153408 n_clusters 4,116,0.001,0.05655761716153408,4,0.2198049512378094,0,None,i7186,26,0.012791076557018043 1727452971,1727453001,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 123 confidence 0.001 feature_proportion 0.006998235909252847 n_clusters 4,123,0.001,0.006998235909252847,4,0.23130782695673924,0,None,i7186,26,0.014158712091816055 1727453011,1727453043,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 213 confidence 0.01 feature_proportion 0.09226334132469617 n_clusters 4,213,0.01,0.09226334132469617,4,0.2520630157539385,0,None,i7186,28,0.02293220363914508 1727453031,1727453070,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 454 confidence 0.25 feature_proportion 0.1293298536964322 n_clusters 4,454,0.25,0.1293298536964322,4,0.35858964741185295,0,None,i7186,35,0.04047440431536455 1727453042,1727453073,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.025 feature_proportion 0.08624643384585806 n_clusters 4,144,0.025,0.08624643384585806,4,0.25256314078519626,0,None,i7186,28,0.014974897570546484 1727453091,1727453095,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.001 feature_proportion 0 n_clusters 3,141,0.001,0,3,None,1,None,i7186 1727453071,1727453100,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 111 confidence 0.025 feature_proportion 0.09977172470766787 n_clusters 4,111,0.025,0.09977172470766787,4,0.2180545136284071,0,None,i7186,25,0.012110170399742793 1727453111,1727453150,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 484 confidence 0.005 feature_proportion 0.10310045642651987 n_clusters 1,484,0.005,0.10310045642651987,1,0.3583395848962241,0,None,i7186,35,0.04051012753188297 1727453151,1727453180,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.09966023024639975 n_clusters 1,100,0.025,0.09966023024639975,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727453466,1727453470,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 feature_proportion 0 n_clusters 3,138,0.001,0,3,None,1,None,i7186 1727453491,1727453495,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 132 confidence 0.001 feature_proportion 0 n_clusters 4,132,0.001,0,4,None,1,None,i7186 1727453511,1727453541,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.005 feature_proportion 0.08721378863388846 n_clusters 4,137,0.005,0.08721378863388846,4,0.23355838959739939,0,None,i7186,26,0.014584003143643052 1727453557,1727453561,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 130 confidence 0.001 feature_proportion 0 n_clusters 4,130,0.001,0,4,None,1,None,i7186 1727453551,1727453580,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10680348901411732 n_clusters 1,100,0.025,0.10680348901411732,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727453588,1727453591,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 feature_proportion 0 n_clusters 3,138,0.001,0,3,None,1,None,i7186 1727453631,1727453635,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 confidence 0.001 feature_proportion 0 n_clusters 4,118,0.001,0,4,None,1,None,i7186 1727453611,1727453641,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.11306346793660699 n_clusters 2,100,0.005,0.11306346793660699,2,0.21480370092523127,0,None,i7186,26,0.011239652018267725 1727453648,1727453677,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.09797058296469249 n_clusters 3,100,0.01,0.09797058296469249,3,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727453678,1727453708,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 feature_proportion 0.0010683593834509494 n_clusters 4,138,0.001,0.0010683593834509494,4,0.24031007751937983,0,None,i7186,26,0.014874088892593519 1727453709,1727453713,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.001 feature_proportion 0 n_clusters 3,144,0.001,0,3,None,1,None,i7186 1727453732,1727453735,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 feature_proportion 0 n_clusters 3,142,0.001,0,3,None,1,None,i7186 1727453751,1727453780,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.09795563197096074 n_clusters 3,100,0.01,0.09795563197096074,3,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727453792,1727453831,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 582 confidence 0.05 feature_proportion 0.2 n_clusters 2,582,0.05,0.2,2,0.36934233558389595,0,None,i7186,36,0.05451362840710178 1727453799,1727453837,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 479 confidence 0.005 feature_proportion 0.11087496798236862 n_clusters 1,479,0.005,0.11087496798236862,1,0.3493373343335834,0,None,i7186,34,0.04179616332654592 1727454132,1727454161,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.1095875199175485 n_clusters 1,100,0.025,0.1095875199175485,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454163,1727454192,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10831432156519089 n_clusters 1,100,0.025,0.10831432156519089,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454192,1727454221,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.1127171306642339 n_clusters 1,100,0.025,0.1127171306642339,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454212,1727454241,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.1064776293096138 n_clusters 1,100,0.025,0.1064776293096138,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727454252,1727454255,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727454232,1727454262,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 confidence 0.001 feature_proportion 0.03458082718185618 n_clusters 4,135,0.001,0.03458082718185618,4,0.2578144536134034,0,None,i7186,27,0.01371771514307148 1727454284,1727454324,40,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 594 confidence 0.01 feature_proportion 0.07139143546007469 n_clusters 4,594,0.01,0.07139143546007469,4,0.3760940235058765,0,None,i7186,36,0.05316329082270567 1727454312,1727454344,32,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 confidence 0.001 feature_proportion 0.04002204053253673 n_clusters 4,133,0.001,0.04002204053253673,4,0.22930732683170796,0,None,i7186,27,0.014735826813846317 1727454332,1727454363,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 confidence 0.001 feature_proportion 0.04223698685666926 n_clusters 4,128,0.001,0.04223698685666926,4,0.23405851462865712,0,None,i7186,27,0.014566141535383848 1727454344,1727454383,39,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 545 confidence 0.005 feature_proportion 0.17988707585226527 n_clusters 3,545,0.005,0.17988707585226527,3,0.36484121030257566,0,None,i7186,35,0.04617821121947153 1727454373,1727454411,38,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 476 confidence 0.025 feature_proportion 0.09798124267706133 n_clusters 4,476,0.025,0.09798124267706133,4,0.3573393348337084,0,None,i7186,34,0.04065302039795664 1727454405,1727454433,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10800690558099198 n_clusters 1,100,0.025,0.10800690558099198,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454432,1727454463,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.001 feature_proportion 0.043458904588374654 n_clusters 4,137,0.001,0.043458904588374654,4,0.2280570142535634,0,None,i7186,27,0.01532790605058672 1727454452,1727454481,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.10997003743571394 n_clusters 1,100,0.025,0.10997003743571394,1,0.21505376344086025,0,None,i7186,26,0.011233071425751173 1727454813,1727454843,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.0024007108378774696 n_clusters 1,100,0.001,0.0024007108378774696,1,0.2433108277069267,0,None,i7186,26,0.012456239059764942 1727454829,1727454858,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.11322857085940374 n_clusters 1,100,0.025,0.11322857085940374,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454872,1727454876,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727454852,1727454889,37,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 404 confidence 0.1 feature_proportion 0.10516458514743084 n_clusters 1,404,0.1,0.10516458514743084,1,0.3305826456614154,0,None,i7186,33,0.03891597899474868 1727454933,1727454936,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0 n_clusters 3,140,0.001,0,3,None,1,None,i7186 1727454913,1727454943,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.11349434915698078 n_clusters 1,100,0.025,0.11349434915698078,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454951,1727454980,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.11156445086750762 n_clusters 1,100,0.025,0.11156445086750762,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727454981,1727455010,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.15992239595027324 n_clusters 3,100,0.01,0.15992239595027324,3,0.28157039259814953,0,None,i7186,25,0.009239489359519367 1727455011,1727455015,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727455033,1727455062,29,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.11212765254106333 n_clusters 1,100,0.025,0.11212765254106333,1,0.21505376344086025,0,None,i7186,25,0.011233071425751173 1727455053,1727455083,30,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 161 confidence 0.005 feature_proportion 0.00048530596024132146 n_clusters 2,161,0.005,0.00048530596024132146,2,0.24531132783195797,0,None,i7186,27,0.017243441295106385
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252001/2252001_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252004/2252004_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252008/2252008_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252015/2252015_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252018/2252018_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252020/2252020_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252023/2252023_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 190 confidence 0.001 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=(190, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252024/2252024_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 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_OutdoorObjects/0/single_runs/2252028/2252028_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252034/2252034_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252041/2252041_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252042/2252042_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252047/2252047_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252044/2252044_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 266 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=(266, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252052/2252052_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252053/2252053_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252054/2252054_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252055/2252055_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 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=(233, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252056/2252056_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252057/2252057_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252060/2252060_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 257 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=(257, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252061/2252061_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252062/2252062_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.25 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=(183, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252064/2252064_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252065/2252065_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 232 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=(232, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252066/2252066_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252067/2252067_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 191 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=(191, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252070/2252070_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252072/2252072_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252074/2252074_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252077/2252077_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 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=(250, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252078/2252078_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252080/2252080_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252082/2252082_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252083/2252083_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252084/2252084_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252085/2252085_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252086/2252086_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252089/2252089_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252095/2252095_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252098/2252098_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252099/2252099_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252100/2252100_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252101/2252101_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252104/2252104_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252106/2252106_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252109/2252109_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252111/2252111_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 273 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=(273, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252115/2252115_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252116/2252116_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252117/2252117_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 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_OutdoorObjects/0/single_runs/2252119/2252119_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252135/2252135_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252140/2252140_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.001 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=(153, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252142/2252142_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.001 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=(153, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252148/2252148_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.001 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=(151, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252171/2252171_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252172/2252172_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252173/2252173_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252174/2252174_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.001 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=(156, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252176/2252176_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.001 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=(156, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252177/2252177_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252178/2252178_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 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=(138, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252180/2252180_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 147 confidence 0.001 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=(147, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252182/2252182_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252185/2252185_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252188/2252188_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252191/2252191_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252192/2252192_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252193/2252193_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252194/2252194_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252195/2252195_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252196/2252196_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252200/2252200_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 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_OutdoorObjects/0/single_runs/2252203/2252203_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 120 confidence 0.01 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=(120, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252204/2252204_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252206/2252206_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252209/2252209_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252210/2252210_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252211/2252211_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252212/2252212_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252217/2252217_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252220/2252220_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252221/2252221_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252318/2252318_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252333/2252333_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252341/2252341_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252349/2252349_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252357/2252357_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252380/2252380_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252388/2252388_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252405/2252405_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252413/2252413_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252453/2252453_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252462/2252462_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252470/2252470_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252630/2252630_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 103 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=(103, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252643/2252643_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 210 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=(210, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252652/2252652_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 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=(154, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252655/2252655_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252668/2252668_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252687/2252687_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252695/2252695_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.001 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=(115, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252711/2252711_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252735/2252735_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 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=(133, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252743/2252743_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.005 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=(101, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252766/2252766_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252916/2252916_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252924/2252924_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252947/2252947_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252971/2252971_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.001 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=(104, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252994/2252994_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.001 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=(116, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253009/2253009_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253024/2253024_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253032/2253032_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253047/2253047_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253055/2253055_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253078/2253078_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 131 confidence 0.001 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=(131, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253086/2253086_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253221/2253221_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253236/2253236_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253244/2253244_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253259/2253259_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253282/2253282_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253283/2253283_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253298/2253298_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253313/2253313_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253329/2253329_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253337/2253337_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253352/2253352_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 442 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=(442, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253375/2253375_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253383/2253383_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253406/2253406_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253542/2253542_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253572/2253572_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253580/2253580_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253588/2253588_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253618/2253618_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253626/2253626_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253634/2253634_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2253642/2253642_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253672/2253672_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253680/2253680_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253703/2253703_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253718/2253718_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253798/2253798_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253813/2253813_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253828/2253828_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253836/2253836_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253851/2253851_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 242 confidence 0.001 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=(242, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253874/2253874_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253882/2253882_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253890/2253890_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253905/2253905_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253935/2253935_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253951/2253951_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253966/2253966_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254144/2254144_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254159/2254159_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254174/2254174_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 127 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=(127, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254189/2254189_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254197/2254197_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 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=(128, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254212/2254212_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254235/2254235_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 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=(126, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254249/2254249_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254258/2254258_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254296/2254296_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 212 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=(212, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254309/2254309_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254326/2254326_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 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=(128, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254349/2254349_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254519/2254519_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254528/2254528_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254543/2254543_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254575/2254575_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254589/2254589_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254625/2254625_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 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=(169, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254657/2254657_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254689/2254689_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254697/2254697_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254727/2254727_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254869/2254869_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254891/2254891_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254899/2254899_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 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=(135, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254907/2254907_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254929/2254929_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254937/2254937_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2254952/2254952_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254982/2254982_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254990/2254990_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255005/2255005_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255020/2255020_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255035/2255035_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255058/2255058_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 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=(143, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255073/2255073_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 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=(143, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255264/2255264_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255302/2255302_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255332/2255332_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2255340/2255340_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 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=(146, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255370/2255370_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255400/2255400_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 confidence 0.005 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=(233, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255415/2255415_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.005 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=(230, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255430/2255430_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255445/2255445_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255475/2255475_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255639/2255639_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 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=(144, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255655/2255655_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255689/2255689_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255705/2255705_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255720/2255720_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 145 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=(145, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255737/2255737_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2255761/2255761_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2255777/2255777_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255793/2255793_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 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=(146, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255832/2255832_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255846/2255846_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255862/2255862_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256048/2256048_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256078/2256078_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256093/2256093_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256108/2256108_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256130/2256130_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 165 confidence 0.01 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=(165, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256145/2256145_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256153/2256153_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256175/2256175_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256190/2256190_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 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=(135, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256205/2256205_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256235/2256235_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256272/2256272_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 149 confidence 0.001 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=(149, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256462/2256462_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256492/2256492_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256515/2256515_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256567/2256567_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256657/2256657_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.001 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256871/2256871_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 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=(138, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256886/2256886_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 132 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=(132, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256931/2256931_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 130 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=(130, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256946/2256946_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 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=(138, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256983/2256983_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 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=(118, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257028/2257028_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.001 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=(144, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257043/2257043_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257381/2257381_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257764/2257764_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257794/2257794_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257825/2257825_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
timestamp,ram_usage_mb,cpu_usage_percent 1727442452,476.59375,49.7 1727442452,476.59375,50.0 1727442452,476.59375,49.7 1727442452,476.59375,57.1 1727442452,476.59375,40.0 1727442452,476.59375,49.9 1727442452,476.59375,40.6 1727442498,485.51953125,49.8 1727442498,485.51953125,39.4 1727442498,485.51953125,50.2 1727442498,485.51953125,37.5 1727442504,486.4453125,49.7 1727442504,486.4453125,53.1 1727442504,486.4453125,48.8 1727442504,486.4453125,48.6 1727442506,486.49609375,49.9 1727442506,486.49609375,55.6 1727442506,486.49609375,51.2 1727442506,486.49609375,40.6 1727442508,486.49609375,49.9 1727442508,486.49609375,54.2 1727442508,486.49609375,48.5 1727442508,486.49609375,47.5 1727442511,486.5234375,49.9 1727442511,486.5234375,38.2 1727442511,486.5234375,52.5 1727442511,486.5234375,36.4 1727442513,486.52734375,49.9 1727442513,486.52734375,40.5 1727442513,486.52734375,52.8 1727442513,486.52734375,37.1 1727442515,486.52734375,49.9 1727442515,486.52734375,50.0 1727442515,486.52734375,48.5 1727442515,486.52734375,37.5 1727442518,486.53125,49.9 1727442518,486.53125,55.3 1727442518,486.53125,46.7 1727442518,486.53125,57.8 1727442520,486.53125,49.9 1727442520,486.53125,56.5 1727442520,486.53125,48.1 1727442520,486.53125,57.8 1727442522,486.53125,49.8 1727442522,486.53125,53.2 1727442522,486.53125,48.1 1727442522,486.53125,56.8 1727442525,486.53125,49.9 1727442525,486.53125,38.2 1727442525,486.53125,52.9 1727442525,486.53125,38.7 1727442527,486.53125,49.9 1727442527,486.53125,54.3 1727442527,486.53125,47.3 1727442527,486.53125,55.6 1727442529,486.53125,49.9 1727442529,486.53125,50.0 1727442529,486.53125,48.3 1727442529,486.53125,56.8 1727442532,486.53125,49.8 1727442532,486.53125,57.4 1727442532,486.53125,45.5 1727442532,486.53125,55.6 1727442534,486.53125,49.9 1727442534,486.53125,54.3 1727442534,486.53125,47.3 1727442534,486.53125,55.6 1727442536,486.53125,49.9 1727442536,486.53125,53.2 1727442536,486.53125,46.4 1727442536,486.53125,56.3 1727442538,486.53125,49.9 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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 (2024-09-27 15:07:36,251) - Starting with JobEnvironment(job_id=2251978, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-27 15:07:36,253) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2251978/2251978_submitted.pkl
Parameters: {"n_samples": 639, "confidence": 0.01, "feature_proportion": 0.03538897037506104, "n_clusters": 4}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 639 confidence 0.01 feature_proportion 0.03538897037506104 n_clusters 4
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: 1727442365.0
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: 1727442365.0
Last modification: 1727277242.0
Hostname: i7186
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 639 confidence 0.01 feature_proportion 0.03538897037506104 n_clusters 4
stdout:
RESULT: 0.3688422105526381
OO-Info: runtime: 37
OO-Info: lpd: 0.05461365341335335
Result: 0.3688422105526381
EXIT_CODE: 0
submitit INFO (2024-09-27 15:08:22,242) - Job completed successfully
submitit INFO (2024-09-27 15:08:22,249) - Exiting after successful completion
Job-Infos
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border: 0px;
}
.error_element {
background-color: #e57373;
border-radius: 10px;
padding: 4px;
display: none;
}
button {
background-color: #4eae46;
border: 1px solid #2A8387;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
cursor: pointer;
display: block;
line-height: 100%;
outline: 0;
padding: 11px 15px 12px;
text-align: center;
transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}
button:hover {
box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
text-decoration: none;
transition-duration: .15s, .15s;
}
button:active {
box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
}
button:disabled {
cursor: not-allowed;
opacity: .6;
}
button:disabled:active {
pointer-events: none;
}
button:disabled:hover {
box-shadow: none;
}
.half_width_td {
vertical-align: baseline;
width: 50%;
}
#scads_bar {
width: 100%;
min-height: 80px;
margin: 0;
padding: 0;
user-select: none;
user-drag: none;
-webkit-user-drag: none;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select: none;
display: -webkit-box;
}
.tab {
display: inline-block;
padding: 0px;
margin: 0px;
font-size: 16px;
font-weight: bold;
text-align: center;
border-radius: 25px;
text-decoration: none !important;
transition: background-color 0.3s, color 0.3s;
color: unset !important;
}
.tooltipster-base {
border: 1px solid black;
position: absolute;
border-radius: 8px;
padding: 2px;
color: white;
background-color: #61686f;
width: 70%;
min-width: 200px;
pointer-events: none;
}
td {
padding-top: 3px;
padding-bottom: 3px;
}
.left_side {
text-align: right;
}
.right_side {
text-align: left;
}
.spinner {
border: 8px solid rgba(0, 0, 0, 0.1);
border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 60px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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: :-webkit-scrollbar-button: vertical: end{
height: 17px;
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}
: :-webkit-scrollbar-button: horizontal: start{
width: 17px;
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}
: :-webkit-scrollbar-button: horizontal: end{
width: 17px;
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}
.window{
box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
border-top-left-radius: 8px;
border-top-right-radius: 8px;
padding: 0 0 3px;
-webkit-font-smoothing: antialiased
}
.title-bar{
background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
padding: 3px 5px 3px 3px;
border-top: 1px solid #0831d9;
border-left: 1px solid #0831d9;
border-right: 1px solid #001ea0;
border-top-left-radius: 8px;
border-top-right-radius: 7px;
font-size: 13px;
text-shadow: 1px 1px #0f1089;
height: 21px
}
.title-bar-text{
padding-left: 3px
}
.title-bar-controls{
display: flex
}
.title-bar-controls button{
min-width: 21px;
min-height: 21px;
margin-left: 2px;
background-repeat: no-repeat;
background-position: 50%;
box-shadow: none;
background-color: #0050ee;
transition: background .1s;
border: none
}
.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
box-shadow: none!important
}
.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
}
.status-bar{
margin: 0 3px;
box-shadow: inset 0 1px 2px grey;
padding: 2px 1px;
gap: 0
}
.status-bar-field{
-webkit-font-smoothing: antialiased;
box-shadow: none;
padding: 1px 2px;
border-right: 1px solid rgba(208,206,191,.75);
border-left: 1px solid hsla(0,0%,100%,.75)
}
.status-bar-field: first-of-type{
border-left: none
}
.status-bar-field: last-of-type{
border-right: none
}
button{
-webkit-font-smoothing: antialiased;
box-sizing: border-box;
border: 1px solid #003c74;
background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
box-shadow: none;
border-radius: 3px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: none;
background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
}
button: not(: disabled): hover{
box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
}
button.focused,button: focus{
box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
}
button: :-moz-focus-inner{
border: 0
}
input,label,option,select,textarea{
-webkit-font-smoothing: antialiased
}
input[type=radio]{
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
margin: 0;
background: 0;
position: fixed;
opacity: 0;
border: none
}
input[type=radio]+label{
line-height: 16px
}
input[type=radio]+label: before{
background: linear-gradient(135deg,#dcdcd7,#fff);
border-radius: 50%;
border: 1px solid #1d5281
}
input[type=radio]: not([disabled]): not(: active)+label: hover: before{
box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
}
input[type=radio]: active+label: before{
background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
}
input[type=radio]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
}
input[type=radio]: focus+label{
outline: 1px dotted #000
}
input[type=radio][disabled]+label: before{
border: 1px solid #cac8bb;
background: #fff
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
}
input[type=email],input[type=password],textarea: :selection{
background: #2267cb;
color: #fff
}
input[type=range]: :-webkit-slider-thumb{
height: 21px;
width: 11px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(-8px)
}
input[type=range]: :-moz-range-thumb{
height: 21px;
width: 11px;
border: 0;
border-radius: 0;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
transform: translateY(2px)
}
input[type=range]: :-webkit-slider-runnable-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range]: :-moz-range-track{
width: 100%;
height: 2px;
box-sizing: border-box;
background: #ecebe4;
border-right: 1px solid #f3f2ea;
border-bottom: 1px solid #f3f2ea;
border-radius: 2px;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid #f3f2ea;
border-right: 0;
border-bottom: 1px solid #f3f2ea;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
}
fieldset{
box-shadow: none;
background: #fff;
border: 1px solid #d0d0bf;
border-radius: 4px;
padding-top: 10px
}
legend{
background: transparent;
color: #0046d5
}
.field-row{
display: flex;
align-items: center
}
.field-row>*+*{
margin-left: 6px
}
[class^=field-row]+[class^=field-row]{
margin-top: 6px
}
.field-row-stacked{
display: flex;
flex-direction: column
}
.field-row-stacked *+*{
margin-top: 6px
}
menu[role=tablist] button{
background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
margin-left: -1px;
margin-right: 2px;
border-radius: 0;
border-color: #91a7b4;
border-top-right-radius: 3px;
border-top-left-radius: 3px;
padding: 0 12px 3px
}
menu[role=tablist] button: hover{
box-shadow: unset;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]{
border-color: #919b9c;
margin-right: -1px;
border-bottom: 1px solid transparent;
border-top: 1px solid #e68b2c;
box-shadow: inset 0 2px #ffc73c
}
menu[role=tablist] button[aria-selected=true]: first-of-type: before{
content: "";
display: block;
position: absolute;
z-index: -1;
top: 100%;
left: -1px;
height: 2px;
width: 0;
border-left: 1px solid #919b9c
}
[role=tabpanel]{
box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
}
ul.tree-view{
-webkit-font-smoothing: auto;
border: 1px solid #7f9db9;
padding: 2px 5px
}
@keyframes sliding{
0%{
transform: translateX(-30px)
}
to{
transform: translateX(100%)
}
}
progress{
box-sizing: border-box;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
height: 14px;
border: 1px solid #686868;
border-radius: 4px;
padding: 1px 2px 1px 0;
overflow: hidden;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress,progress: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
height: 14px
}
progress[value]: :-webkit-progress-bar{
background-color: transparent
}
progress[value]: :-webkit-progress-value{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress[value]: :-moz-progress-bar{
border-radius: 2px;
background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-webkit-progress-bar{
width: 100%;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
animation: sliding 2s linear 0s infinite
}
progress: not([value]): :-webkit-progress-bar: not([value]){
animation: sliding 2s linear 0s infinite;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]){
position: relative
}
progress: not([value]): before{
box-sizing: border-box;
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: #fff;
-webkit-box-shadow: inset 0 0 1px 0 #686868;
-moz-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before,progress: not([value]): before: not([value]){
box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): before: not([value]){
-moz-box-shadow: inset 0 0 1px 0 #686868;
-webkit-box-shadow: inset 0 0 1px 0 #686868
}
progress: not([value]): after{
box-sizing: border-box;
content: "";
position: absolute;
top: 1px;
left: 2px;
width: 100%;
height: calc(100% - 2px);
padding: 1px 2px;
border-radius: 2px;
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): after,progress: not([value]): after: not([value]){
animation: sliding 2s linear 0s infinite
}
progress: not([value]): after: not([value]){
background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
}
progress: not([value]): :-moz-progress-bar{
width: 100%;
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];
var tab_main_worker_cpu_ram_headers_json = [
"timestamp",
"ram_usage_mb",
"cpu_usage_percent"
];
"use strict";
function add_default_layout_data (layout) {
layout["width"] = get_graph_width();
layout["height"] = get_graph_height();
layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
return layout;
}
function get_marker_size() {
return 12;
}
function get_text_color() {
return theme == "dark" ? "white" : "black";
}
function get_font_size() {
return 14;
}
function get_graph_height() {
return 800;
}
function get_font_data() {
return {
size: get_font_size(),
color: get_text_color()
}
}
function get_axis_title_data(name, axis_type = "") {
if(axis_type) {
return {
text: name,
type: axis_type,
font: get_font_data()
};
}
return {
text: name,
font: get_font_data()
};
}
function get_graph_width() {
var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
return Math.max(800, Math.floor(width * 0.9));
}
function createTable(data, headers, table_name) {
if (!$("#" + table_name).length) {
console.error("#" + table_name + " not found");
return;
}
new gridjs.Grid({
columns: headers,
data: data,
search: true,
sort: true
}).render(document.getElementById(table_name));
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
colorize_table_entries();
add_colorize_to_gridjs_table();
}
function download_as_file(id, filename) {
var text = $("#" + id).text();
var blob = new Blob([text], {
type: "text/plain"
});
var link = document.createElement("a");
link.href = URL.createObjectURL(blob);
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
function copy_to_clipboard_from_id (id) {
var text = $("#" + id).text();
copy_to_clipboard(text);
}
function copy_to_clipboard(text) {
if (!navigator.clipboard) {
let textarea = document.createElement("textarea");
textarea.value = text;
document.body.appendChild(textarea);
textarea.select();
try {
document.execCommand("copy");
} catch (err) {
console.error("Copy failed:", err);
}
document.body.removeChild(textarea);
return;
}
navigator.clipboard.writeText(text).then(() => {
console.log("Text copied to clipboard");
}).catch(err => {
console.error("Failed to copy text:", err);
});
}
function filterNonEmptyRows(data) {
var new_data = [];
for (var row_idx = 0; row_idx < data.length; row_idx++) {
var line = data[row_idx];
var line_has_empty_data = false;
for (var col_idx = 0; col_idx < line.length; col_idx++) {
var col_header_name = tab_results_headers_json[col_idx];
var single_data_point = line[col_idx];
if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
line_has_empty_data = true;
continue;
}
}
if(!line_has_empty_data) {
new_data.push(line);
}
}
return new_data;
}
function make_text_in_parallel_plot_nicer() {
$(".parcoords g > g > text").each(function() {
if (theme == "dark") {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "white")
.css("stroke", "black")
.css("stroke-width", "2px")
.css("paint-order", "stroke fill");
} else {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "black")
.css("stroke", "unset")
.css("stroke-width", "unset")
.css("paint-order", "stroke fill");
}
});
}
function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
if ($("#parallel-plot").data("loaded") == "true") {
return;
}
dataArray = filterNonEmptyRows(dataArray);
const ignoreSet = new Set(ignoreColumns);
const numericalCols = [];
const categoricalCols = [];
const categoryMappings = {};
headers.forEach((header, colIndex) => {
if (ignoreSet.has(header)) return;
const values = dataArray.map(row => row[colIndex]);
if (values.every(val => !isNaN(parseFloat(val)))) {
numericalCols.push({ name: header, index: colIndex });
} else {
categoricalCols.push({ name: header, index: colIndex });
const uniqueValues = [...new Set(values)];
categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
}
});
const dimensions = [];
numericalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => parseFloat(row[col.index])),
range: [
Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
Math.max(...dataArray.map(row => parseFloat(row[col.index])))
]
});
});
categoricalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
tickvals: Object.values(categoryMappings[col.name]),
ticktext: Object.keys(categoryMappings[col.name])
});
});
let colorScale = null;
let colorValues = null;
if (resultNames.length > 1) {
let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
selectBox += '<option value="none">No color</option>';
var k = 0;
resultNames.forEach(resultName => {
var minMax = result_min_max[k];
if(minMax === undefined) {
minMax = "min [automatically chosen]"
}
selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
k = k + 1;
});
selectBox += '</select>';
$("#parallel-plot").before(selectBox);
$("#result-select").change(function() {
const selectedResult = $(this).val();
if (selectedResult === "none") {
colorValues = null;
colorScale = null;
} else {
const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
let minResult = Math.min(...colorValues);
let maxResult = Math.max(...colorValues);
var _result_min_max_idx = result_names.indexOf(selectedResult);
let invertColor = false;
if (result_min_max.length > _result_min_max_idx) {
invertColor = result_min_max[_result_min_max_idx] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
}
updatePlot();
});
} else {
let invertColor = false;
if (Object.keys(result_min_max).length == 1) {
invertColor = result_min_max[0] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
}
function updatePlot() {
const trace = {
type: 'parcoords',
dimensions: dimensions,
line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
unselected: {
line: {
color: get_text_color(),
opacity: 0
}
},
};
dimensions.forEach(dim => {
if (!dim.line) {
dim.line = {};
}
if (!dim.line.color) {
dim.line.color = 'rgba(169,169,169, 0.01)';
}
});
Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
make_text_in_parallel_plot_nicer();
}
updatePlot();
$("#parallel-plot").data("loaded", "true");
make_text_in_parallel_plot_nicer();
}
function plotWorkerUsage() {
if($("#workerUsagePlot").data("loaded") == "true") {
return;
}
var data = tab_worker_usage_csv_json;
if (!Array.isArray(data) || data.length === 0) {
console.error("Invalid or empty data provided.");
return;
}
let timestamps = [];
let desiredWorkers = [];
let realWorkers = [];
for (let i = 0; i < data.length; i++) {
let entry = data[i];
if (!Array.isArray(entry) || entry.length < 3) {
console.warn("Skipping invalid entry:", entry);
continue;
}
let unixTime = parseFloat(entry[0]);
let desired = parseInt(entry[1], 10);
let real = parseInt(entry[2], 10);
if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
console.warn("Skipping invalid numerical values:", entry);
continue;
}
timestamps.push(new Date(unixTime * 1000).toISOString());
desiredWorkers.push(desired);
realWorkers.push(real);
}
let trace1 = {
x: timestamps,
y: desiredWorkers,
mode: 'lines+markers',
name: 'Desired Workers',
line: {
color: 'blue'
}
};
let trace2 = {
x: timestamps,
y: realWorkers,
mode: 'lines+markers',
name: 'Real Workers',
line: {
color: 'red'
}
};
let layout = {
title: "Worker Usage Over Time",
xaxis: {
title: get_axis_title_data("Time", "date")
},
yaxis: {
title: get_axis_title_data("Number of Workers")
},
legend: {
x: 0,
y: 1
}
};
Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
$("#workerUsagePlot").data("loaded", "true");
}
function plotCPUAndRAMUsage() {
if($("#mainWorkerCPURAM").data("loaded") == "true") {
return;
}
var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
var trace1 = {
x: timestamps,
y: cpuUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'CPU Usage (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: ramUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'RAM Usage (MB)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'CPU and RAM Usage Over Time',
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
overlaying: 'y',
side: 'right',
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var data = [trace1, trace2];
Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
$("#mainWorkerCPURAM").data("loaded", "true");
}
function plotScatter2d() {
if ($("#plotScatter2d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter2d");
var minInput = document.getElementById("minValue");
var maxInput = document.getElementById("maxValue");
if (!minInput || !maxInput) {
minInput = document.createElement("input");
minInput.id = "minValue";
minInput.type = "number";
minInput.placeholder = "Min Value";
minInput.step = "any";
maxInput = document.createElement("input");
maxInput.id = "maxValue";
maxInput.type = "number";
maxInput.placeholder = "Max Value";
maxInput.step = "any";
var inputContainer = document.createElement("div");
inputContainer.style.marginBottom = "10px";
inputContainer.appendChild(minInput);
inputContainer.appendChild(maxInput);
plotDiv.appendChild(inputContainer);
}
var resultSelect = document.getElementById("resultSelect");
if (result_names.length > 1 && !resultSelect) {
resultSelect = document.createElement("select");
resultSelect.id = "resultSelect";
resultSelect.style.marginBottom = "10px";
var sortedResults = [...result_names].sort();
sortedResults.forEach(result => {
var option = document.createElement("option");
option.value = result;
option.textContent = result;
resultSelect.appendChild(option);
});
var selectContainer = document.createElement("div");
selectContainer.style.marginBottom = "10px";
selectContainer.appendChild(resultSelect);
plotDiv.appendChild(selectContainer);
}
minInput.addEventListener("input", updatePlots);
maxInput.addEventListener("input", updatePlots);
if (resultSelect) {
resultSelect.addEventListener("change", updatePlots);
}
updatePlots();
async function updatePlots() {
var minValue = parseFloat(minInput.value);
var maxValue = parseFloat(maxInput.value);
if (isNaN(minValue)) minValue = -Infinity;
if (isNaN(maxValue)) maxValue = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var selectedResult = resultSelect ? resultSelect.value : result_names[0];
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 2) {
console.error("Not enough columns for Scatter-Plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
showlegend: false
};
let subDiv = document.createElement("div");
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
symbol: data.map(d => d.result === null ? 'x' : 'circle'),
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter',
showlegend: false
};
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
$("#plotScatter2d").data("loaded", "true");
}
function plotScatter3d() {
if ($("#plotScatter3d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter3d");
if (!plotDiv) {
console.error("Div element with id 'plotScatter3d' not found");
return;
}
plotDiv.innerHTML = "";
var minInput3d = document.getElementById("minValue3d");
var maxInput3d = document.getElementById("maxValue3d");
if (!minInput3d || !maxInput3d) {
minInput3d = document.createElement("input");
minInput3d.id = "minValue3d";
minInput3d.type = "number";
minInput3d.placeholder = "Min Value";
minInput3d.step = "any";
maxInput3d = document.createElement("input");
maxInput3d.id = "maxValue3d";
maxInput3d.type = "number";
maxInput3d.placeholder = "Max Value";
maxInput3d.step = "any";
var inputContainer3d = document.createElement("div");
inputContainer3d.style.marginBottom = "10px";
inputContainer3d.appendChild(minInput3d);
inputContainer3d.appendChild(maxInput3d);
plotDiv.appendChild(inputContainer3d);
}
var select3d = document.getElementById("select3dScatter");
if (result_names.length > 1 && !select3d) {
if (!select3d) {
select3d = document.createElement("select");
select3d.id = "select3dScatter";
select3d.style.marginBottom = "10px";
select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
select3d.addEventListener("change", updatePlots3d);
plotDiv.appendChild(select3d);
}
}
minInput3d.addEventListener("input", updatePlots3d);
maxInput3d.addEventListener("input", updatePlots3d);
updatePlots3d();
async function updatePlots3d() {
var selectedResult = select3d ? select3d.value : result_names[0];
var minValue3d = parseFloat(minInput3d.value);
var maxValue3d = parseFloat(maxInput3d.value);
if (isNaN(minValue3d)) minValue3d = -Infinity;
if (isNaN(maxValue3d)) maxValue3d = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 3) {
console.error("Not enough columns for 3D scatter plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
for (let k = j + 1; k < numericColumns.length; k++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let zCol = numericColumns[k];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let zIndex = tab_results_headers_json.indexOf(zCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
z: parseFloat(row[zIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
scene: {
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
zaxis: {
title: get_axis_title_data(zCol)
}
},
showlegend: false
};
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
z: data.map(d => d.z),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter3d',
showlegend: false
};
let subDiv = document.createElement("div");
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
}
$("#plotScatter3d").data("loaded", "true");
}
async function load_pareto_graph() {
if($("#tab_pareto_fronts").data("loaded") == "true") {
return;
}
var data = pareto_front_data;
if (!data || typeof data !== "object") {
console.error("Invalid data format for pareto_front_data");
return;
}
if (!Object.keys(data).length) {
console.warn("No data found in pareto_front_data");
return;
}
let categories = Object.keys(data);
let allMetrics = new Set();
function extractMetrics(obj, prefix = "") {
let keys = Object.keys(obj);
for (let key of keys) {
let newPrefix = prefix ? `${prefix} -> ${key}` : key;
if (typeof obj[key] === "object" && !Array.isArray(obj[key])) {
extractMetrics(obj[key], newPrefix);
} else {
if (!newPrefix.includes("param_dicts") && !newPrefix.includes(" -> sems -> ") && !newPrefix.includes("absolute_metrics")) {
allMetrics.add(newPrefix);
}
}
}
}
for (let cat of categories) {
extractMetrics(data[cat]);
}
allMetrics = Array.from(allMetrics);
function extractValues(obj, metricPath, values) {
let parts = metricPath.split(" -> ");
let data = obj;
for (let part of parts) {
if (data && typeof data === "object") {
data = data[part];
} else {
return;
}
}
if (Array.isArray(data)) {
values.push(...data);
}
}
let graphContainer = document.getElementById("pareto_front_graphs_container");
graphContainer.classList.add("invert_in_dark_mode");
graphContainer.innerHTML = "";
var already_plotted = [];
for (let i = 0; i < allMetrics.length; i++) {
for (let j = i + 1; j < allMetrics.length; j++) {
let xMetric = allMetrics[i];
let yMetric = allMetrics[j];
let xValues = [];
let yValues = [];
for (let cat of categories) {
let metricData = data[cat];
extractValues(metricData, xMetric, xValues);
extractValues(metricData, yMetric, yValues);
}
xValues = xValues.filter(v => v !== undefined && v !== null);
yValues = yValues.filter(v => v !== undefined && v !== null);
let cleanXMetric = xMetric.replace(/.* -> /g, "");
let cleanYMetric = yMetric.replace(/.* -> /g, "");
let plot_key = `${cleanXMetric}-${cleanYMetric}`;
if (xValues.length > 0 && yValues.length > 0 && xValues.length === yValues.length && !already_plotted.includes(plot_key)) {
let div = document.createElement("div");
div.id = `pareto_front_graph_${i}_${j}`;
div.style.marginBottom = "20px";
graphContainer.appendChild(div);
let layout = {
title: `${cleanXMetric} vs ${cleanYMetric}`,
xaxis: {
title: get_axis_title_data(cleanXMetric)
},
yaxis: {
title: get_axis_title_data(cleanYMetric)
},
hovermode: "closest"
};
let trace = {
x: xValues,
y: yValues,
mode: "markers",
marker: {
size: get_marker_size(),
},
type: "scatter",
name: `${cleanXMetric} vs ${cleanYMetric}`
};
Plotly.newPlot(div.id, [trace], add_default_layout_data(layout));
already_plotted.push(plot_key);
}
}
}
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
$("#tab_pareto_fronts").data("loaded", "true");
}
async function plot_worker_cpu_ram() {
if($("#worker_cpu_ram_pre").data("loaded") == "true") {
return;
}
const logData = $("#worker_cpu_ram_pre").text();
const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
const hostData = {};
logData.split("\n").forEach(line => {
line = line.trim();
const match = line.match(regex);
if (match) {
const timestamp = new Date(parseInt(match[1]) * 1000);
const hostname = match[2];
const cpu = parseFloat(match[3]);
const ram = parseFloat(match[4]);
if (!hostData[hostname]) {
hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
}
hostData[hostname].timestamps.push(timestamp);
hostData[hostname].cpuUsage.push(cpu);
hostData[hostname].ramUsage.push(ram);
}
});
if (!Object.keys(hostData).length) {
console.log("No valid data found");
return;
}
const container = document.getElementById("cpuRamWorkerChartContainer");
container.innerHTML = "";
var i = 1;
Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
const chartId = `workerChart_${index}`;
const chartDiv = document.createElement("div");
chartDiv.id = chartId;
chartDiv.style.marginBottom = "40px";
container.appendChild(chartDiv);
const cpuTrace = {
x: timestamps,
y: cpuUsage,
mode: "lines+markers",
name: "CPU Usage (%)",
yaxis: "y1",
line: {
color: "red"
}
};
const ramTrace = {
x: timestamps,
y: ramUsage,
mode: "lines+markers",
name: "RAM Usage (MB)",
yaxis: "y2",
line: {
color: "blue"
}
};
const layout = {
title: `Worker CPU and RAM Usage - ${hostname}`,
xaxis: {
title: get_axis_title_data("Timestamp", "date")
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
side: "left",
color: "red"
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
side: "right",
overlaying: "y",
color: "blue"
},
showlegend: true
};
Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
i++;
});
$("#plot_worker_cpu_ram_button").remove();
$("#worker_cpu_ram_pre").data("loaded", "true");
}
function load_log_file(log_nr, filename) {
var pre_id = `single_run_${log_nr}_pre`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
fetch(url)
.then(response => response.json())
.then(data => {
if (data.data) {
$("#" + pre_id).html(data.data);
$("#" + pre_id).data("loaded", true);
} else {
log(`No 'data' key found in response.`);
}
$("#spinner_log_" + log_nr).remove();
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#spinner_log_" + log_nr).remove();
});
}
}
function load_debug_log () {
var pre_id = `here_debuglogs_go`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
fetch(url)
.then(response => response.json())
.then(data => {
$("#debug_log_spinner").remove();
if (data.data) {
try {
$("#" + pre_id).html(data.data);
} catch (err) {
$("#" + pre_id).text(`Error loading data: ${err}`);
}
$("#" + pre_id).data("loaded", true);
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
} else {
log(`No 'data' key found in response.`);
}
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#debug_log_spinner").remove();
});
}
}
function plotBoxplot() {
if ($("#plotBoxplot").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough numeric columns for Boxplot");
return;
}
var resultIndex = tab_results_headers_json.findIndex(function(header) {
return result_names.includes(header.toLowerCase());
});
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
var plotDiv = document.getElementById("plotBoxplot");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'box',
name: col,
boxmean: 'sd',
marker: {
color: 'rgb(0, 255, 0)'
},
};
});
let layout = {
title: 'Boxplot of Numerical Columns',
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Value")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotBoxplot").data("loaded", "true");
}
function plotHeatmap() {
if ($("#plotHeatmap").data("loaded") === "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col => {
if (special_col_names.includes(col) || result_names.includes(col)) {
return false;
}
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.every(row => {
let value = parseFloat(row[index]);
return !isNaN(value) && isFinite(value);
});
});
if (numericColumns.length < 2) {
console.error("Not enough valid numeric columns for Heatmap");
return;
}
var columnData = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.map(row => parseFloat(row[index]));
});
var dataMatrix = numericColumns.map((_, i) =>
numericColumns.map((_, j) => {
let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
return values.reduce((a, b) => a + b, 0) / values.length;
})
);
var trace = {
z: dataMatrix,
x: numericColumns,
y: numericColumns,
colorscale: 'Viridis',
type: 'heatmap'
};
var layout = {
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
var plotDiv = document.getElementById("plotHeatmap");
plotDiv.innerHTML = "";
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotHeatmap").data("loaded", "true");
}
function plotHistogram() {
if ($("#plotHistogram").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Histogram");
return;
}
var plotDiv = document.getElementById("plotHistogram");
plotDiv.innerHTML = "";
const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
let traces = numericColumns.map((col, index) => {
let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
return {
x: data,
type: 'histogram',
name: col,
opacity: 0.7,
marker: {
color: colorPalette[index % colorPalette.length]
},
autobinx: true
};
});
let layout = {
title: 'Histogram of Numerical Columns',
xaxis: {
title: get_axis_title_data("Value")
},
yaxis: {
title: get_axis_title_data("Frequency")
},
showlegend: true,
barmode: 'overlay'
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotHistogram").data("loaded", "true");
}
function plotViolin() {
if ($("#plotViolin").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Violin Plot");
return;
}
var plotDiv = document.getElementById("plotViolin");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'violin',
name: col,
box: {
visible: true
},
line: {
color: 'rgb(0, 255, 0)'
},
marker: {
color: 'rgb(0, 255, 0)'
},
meanline: {
visible: true
},
};
});
let layout = {
title: 'Violin Plot of Numerical Columns',
yaxis: {
title: get_axis_title_data("Value")
},
xaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotViolin").data("loaded", "true");
}
function plotExitCodesPieChart() {
if ($("#plotExitCodesPieChart").data("loaded") == "true") {
return;
}
var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
counts[exitCode] = (counts[exitCode] || 0) + 1;
return counts;
}, {});
var labels = Object.keys(exitCodeCounts);
var values = Object.values(exitCodeCounts);
var plotDiv = document.getElementById("plotExitCodesPieChart");
plotDiv.innerHTML = "";
var trace = {
labels: labels,
values: values,
type: 'pie',
hoverinfo: 'label+percent',
textinfo: 'label+value',
marker: {
colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
}
};
var layout = {
title: 'Exit Code Distribution',
showlegend: true
};
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotExitCodesPieChart").data("loaded", "true");
}
function plotResultEvolution() {
if ($("#plotResultEvolution").data("loaded") == "true") {
return;
}
result_names.forEach(resultName => {
var relevantColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
);
var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
var resultIndex = tab_results_headers_json.indexOf(resultName);
let data = tab_results_csv_json.map(row => ({
x: row[xColumnIndex],
y: parseFloat(row[resultIndex])
}));
data.sort((a, b) => a.x - b.x);
let xData = data.map(item => item.x);
let yData = data.map(item => item.y);
let trace = {
x: xData,
y: yData,
mode: 'lines+markers',
name: resultName,
line: {
shape: 'linear'
},
marker: {
size: get_marker_size()
}
};
let layout = {
title: `Evolution of ${resultName} over time`,
xaxis: {
title: get_axis_title_data("Trial-Index")
},
yaxis: {
title: get_axis_title_data(resultName)
},
showlegend: true
};
let subDiv = document.createElement("div");
document.getElementById("plotResultEvolution").appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
});
$("#plotResultEvolution").data("loaded", "true");
}
function plotResultPairs() {
if ($("#plotResultPairs").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotResultPairs");
plotDiv.innerHTML = "";
for (let i = 0; i < result_names.length; i++) {
for (let j = i + 1; j < result_names.length; j++) {
let xName = result_names[i];
let yName = result_names[j];
let xIndex = tab_results_headers_json.indexOf(xName);
let yIndex = tab_results_headers_json.indexOf(yName);
let data = tab_results_csv_json
.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
status: row[tab_results_headers_json.indexOf("trial_status")]
}));
let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: colors
},
text: data.map(d => `Status: ${d.status}`),
type: 'scatter',
showlegend: false
};
let layout = {
xaxis: {
title: get_axis_title_data(xName)
},
yaxis: {
title: get_axis_title_data(yName)
},
showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
upArrow.style.display = "block";
} else {
upArrow.style.display = "none";
}
if (scrollPosition + windowHeight < pageHeight) {
downArrow.style.display = "block";
} else {
downArrow.style.display = "none";
}
}
window.addEventListener("scroll", checkScrollPosition);
upArrow.addEventListener("click", function () {
window.scrollTo({ top: 0, behavior: 'smooth' });
});
downArrow.addEventListener("click", function () {
window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
});
checkScrollPosition();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
function plotGPUUsage() {
if ($("#tab_gpu_usage").data("loaded") === "true") {
return;
}
Object.keys(gpu_usage).forEach(node => {
const nodeData = gpu_usage[node];
var timestamps = [];
var gpuUtilizations = [];
var temperatures = [];
nodeData.forEach(entry => {
try {
var timestamp = new Date(entry[0]* 1000);
var utilization = parseFloat(entry[1]);
var temperature = parseFloat(entry[2]);
if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
timestamps.push(timestamp);
gpuUtilizations.push(utilization);
temperatures.push(temperature);
} else {
console.warn("Invalid data point:", entry);
}
} catch (error) {
console.error("Error processing GPU data entry:", error, entry);
}
});
var trace1 = {
x: timestamps,
y: gpuUtilizations,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Utilization (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: temperatures,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'GPU Temperature (°C)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'GPU Usage Over Time - ' + node,
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("GPU Utilization (%)"),
overlaying: 'y',
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("GPU Temperature (°C)"),
overlaying: 'y',
side: 'right',
position: 0.85,
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var divId = 'gpu_usage_plot_' + node;
if (!document.getElementById(divId)) {
var div = document.createElement('div');
div.id = divId;
div.className = 'gpu-usage-plot';
document.getElementById('tab_gpu_usage').appendChild(div);
}
var plotData = [trace1, trace2];
Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
});
$("#tab_gpu_usage").data("loaded", "true");
}
function plotResultsDistributionByGenerationMethod() {
if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
return;
}
var res_col = result_names[0];
var gen_method_col = "generation_method";
var data = {};
tab_results_csv_json.forEach(row => {
var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
var result = row[tab_results_headers_json.indexOf(res_col)];
if (!data[gen_method]) {
data[gen_method] = [];
}
data[gen_method].push(result);
});
var traces = Object.keys(data).map(method => {
return {
y: data[method],
type: 'box',
name: method,
boxpoints: 'outliers', // Zeigt nur Ausreißer außerhalb der Whiskers
jitter: 0.5, // Erhöht die Streuung der Punkte für bessere Sichtbarkeit
pointpos: 0 // Position der Punkte innerhalb der Box
};
});
var layout = {
title: 'Distribution of Results by Generation Method',
yaxis: {
title: get_axis_title_data(res_col)
},
xaxis: {
title: "Generation Method"
},
boxmode: 'group' // Gruppiert die Boxplots nach Generation Method
};
Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
}
function plotJobStatusDistribution() {
if ($("#plotJobStatusDistribution").data("loaded") === "true") {
return;
}
var status_col = "trial_status";
var status_counts = {};
tab_results_csv_json.forEach(row => {
var status = row[tab_results_headers_json.indexOf(status_col)];
if (status) {
status_counts[status] = (status_counts[status] || 0) + 1;
}
});
var statuses = Object.keys(status_counts);
var counts = Object.values(status_counts);
var colors = statuses.map((status, i) =>
status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
);
var trace = {
x: statuses,
y: counts,
type: 'bar',
marker: { color: colors }
};
var layout = {
title: 'Distribution of Job Status',
xaxis: { title: 'Trial Status' },
yaxis: { title: 'Nr. of jobs' }
};
Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
$("#plotJobStatusDistribution").data("loaded", "true");
}
function _colorize_table_entries_by_generation_method () {
document.querySelectorAll('[data-column-id="generation_method"]').forEach(el => {
let color = el.textContent.includes("Manual") ? "green" :
el.textContent.includes("Sobol") ? "orange" :
el.textContent.includes("SAASBO") ? "pink" :
el.textContent.includes("Uniform") ? "lightblue" :
el.textContent.includes("Legacy_GPEI") ? "Sienna" :
el.textContent.includes("BO_MIXED") ? "Aqua" :
el.textContent.includes("RANDOMFOREST") ? "DarkSeaGreen" :
el.textContent.includes("EXTERNAL_GENERATOR") ? "Purple" :
el.textContent.includes("BoTorch") ? "yellow" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_trial_status () {
document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
el.textContent.includes("RUNNING") ? "orange" :
el.textContent.includes("FAILED") ? "red" : "";
if (color) el.style.backgroundColor = color;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_run_time() {
let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
if (values.length === 0) return;
let min = Math.min(...values);
let max = Math.max(...values);
let range = max - min || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value)) return;
let ratio = (value - min) / range;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
}
function _colorize_table_entries_by_results() {
result_names.forEach((name, index) => {
let minMax = result_min_max[index];
let selector_query = `[data-column-id="${name}"]`;
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
if (values.length === 0) return;
let logValues = values.map(v => Math.log(v));
let logMin = Math.min(...logValues);
let logMax = Math.max(...logValues);
let logRange = logMax - logMin || 1;
cells.forEach(el => {
let value = parseFloat(el.textContent);
if (isNaN(value) || value <= 0) return;
let logValue = Math.log(value);
let ratio = (logValue - logMin) / logRange;
if (minMax === "max") ratio = 1 - ratio;
let red = Math.round(255 * ratio);
let green = Math.round(255 * (1 - ratio));
el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
el.classList.add("invert_in_dark_mode");
});
});
}
function _colorize_table_entries_by_generation_node_or_hostname() {
["hostname", "generation_node"].forEach(element => {
let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
let cells = [...document.querySelectorAll(selector_query)];
if (cells.length === 0) return;
let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
let colorMap = {};
uniqueValues.forEach((value, index) => {
let hue = Math.round((360 / uniqueValues.length) * index);
colorMap[value] = `hsl(${hue}, 70%, 60%)`;
});
cells.forEach(el => {
let value = el.textContent.trim();
if (colorMap[value]) {
el.style.backgroundColor = colorMap[value];
el.classList.add("invert_in_dark_mode");
}
});
});
}
function colorize_table_entries () {
setTimeout(() => {
if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
_colorize_table_entries_by_trial_status();
_colorize_table_entries_by_results();
_colorize_table_entries_by_run_time();
_colorize_table_entries_by_generation_method();
_colorize_table_entries_by_generation_node_or_hostname();
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
}
}, 300);
}
function add_colorize_to_gridjs_table () {
let searchInput = document.querySelector(".gridjs-search-input");
if (searchInput) {
searchInput.addEventListener("input", colorize_table_entries);
}
}
function updatePreWidths() {
var width = window.innerWidth * 0.95;
var pres = document.getElementsByTagName('pre');
for (var i = 0; i < pres.length; i++) {
pres[i].style.width = width + 'px';
}
}
window.addEventListener('load', updatePreWidths);
window.addEventListener('resize', updatePreWidths);
$(document).ready(function() {
colorize_table_entries();
add_up_down_arrows_for_scrolling();
add_colorize_to_gridjs_table();
});
$(document).ready(function() {
colorize_table_entries();;
plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
plotJobStatusDistribution();;
plotBoxplot();;
plotViolin();;
plotHistogram();;
plotHeatmap();;
plotExitCodesPieChart();
colorize_table_entries();
});
</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0, failed: 244): </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>0.025</td><td>0.084886</td><td>1</td><td>0.210303 </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>244</td>
<td>253</td>
<td>8</td>
<td>505</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,0.368842210552638105625078424055,639,0.010000000000000000208166817117,0.035388970375061036544028780781,4
1,1_0,COMPLETED,Sobol,0.396599149787446814130476013815,769,0.025000000000000001387778780781,0.174874556064605723992855246252,3
2,2_0,COMPLETED,Sobol,0.376094023505876506874301412608,594,0.100000000000000005551115123126,0.088957514055073266812101451251,1
3,3_0,COMPLETED,Sobol,0.398099524881220356853361863614,847,0.001000000000000000020816681712,0.096011908352375038844250809689,3
4,4_0,COMPLETED,Sobol,0.276819204801200258181381741451,180,0.050000000000000002775557561563,0.027782568894326689634688420938,4
5,5_0,COMPLETED,Sobol,0.414853713428357084858077996614,815,0.005000000000000000104083408559,0.168868544511497020721435546875,3
6,6_0,COMPLETED,Sobol,0.380345086271567933700055164081,726,0.005000000000000000104083408559,0.013220926560461521842571031016,4
7,7_0,COMPLETED,Sobol,0.282070517629407380155726059456,237,0.005000000000000000104083408559,0.136544787138700496331722433752,2
8,8_0,COMPLETED,Sobol,0.372343085771442816600540481886,609,0.001000000000000000020816681712,0.142151387408375740051269531250,4
9,9_0,COMPLETED,Sobol,0.403850962740685215379699002369,800,0.010000000000000000208166817117,0.035909005254507068982672279844,2
10,10_0,COMPLETED,Sobol,0.414353588397099237283782713348,804,0.005000000000000000104083408559,0.123794245161116131526135575314,4
11,11_0,COMPLETED,Sobol,0.395098774693673382429892626533,699,0.005000000000000000104083408559,0.198873057216405885183618806877,1
12,12_0,COMPLETED,Sobol,0.399099774943735940979649967630,899,0.001000000000000000020816681712,0.163477747701108455657958984375,1
13,13_0,COMPLETED,Sobol,0.272818204551137810653926862869,224,0.100000000000000005551115123126,0.170623718388378642352165570628,1
14,14_0,COMPLETED,Sobol,0.354588647161790393447233782354,538,0.001000000000000000020816681712,0.079169971495866783839367997189,2
15,15_0,COMPLETED,Sobol,0.351337834458614661770070597413,442,0.001000000000000000020816681712,0.045531598292291169949308482501,1
16,16_0,COMPLETED,Sobol,0.243810952738184538723942296201,138,0.001000000000000000020816681712,0.045034025423228742079917452656,2
17,17_0,COMPLETED,Sobol,0.415103775943985953134074406989,971,0.005000000000000000104083408559,0.032584542781114576859291531719,1
18,18_0,COMPLETED,Sobol,0.371342835708927232474252377870,657,0.025000000000000001387778780781,0.047511684894561770353682561563,4
19,19_0,COMPLETED,Sobol,0.406601650412603099482566904044,876,0.050000000000000002775557561563,0.060447103716433053799406138751,1
20,20_0,COMPLETED,BoTorch,0.266066516629157256978999157582,100,0.005000000000000000104083408559,0.044424322962941846515416699503,2
21,21_0,COMPLETED,BoTorch,0.236309077269317380221025359788,100,0.001000000000000000020816681712,0.037716570669483182043357771818,3
22,22_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.025000000000000001387778780781,0.057179380594893650102683579917,2
23,23_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2
24,24_0,COMPLETED,BoTorch,0.274318579644911242354510250152,100,0.001000000000000000020816681712,0.081359089299967046748918164667,3
25,25_0,COMPLETED,BoTorch,0.237559389847461832623309874180,100,0.001000000000000000020816681712,0.068729863265890103751765138895,1
26,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
27,27_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.006200475445037423143090915545,2
28,28_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.158829322168216713340171963864,3
29,29_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.049678109617522753227447651625,1
30,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
31,31_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.062710458944585670271187893832,3
32,32_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.001000000000000000020816681712,0.092118489706186704180090885075,4
33,33_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.094334945386475285711291860480,2
34,34_0,COMPLETED,BoTorch,0.242810702675668954597654192185,100,0.010000000000000000208166817117,0.056389409209062095473807829649,2
35,35_0,COMPLETED,BoTorch,0.219304826206551672918010353897,100,0.050000000000000002775557561563,0.107980795408812613178639594480,1
36,36_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.044856578283365935999604801054,1
37,37_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
38,38_0,COMPLETED,BoTorch,0.237809452363090811921608747070,100,0.005000000000000000104083408559,0.003420433302942613336405930369,3
39,39_0,COMPLETED,BoTorch,0.229807451862965694822094064875,100,0.005000000000000000104083408559,0.129426832632290045310696768865,4
40,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
41,41_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.061780352951651906767693844813,1
42,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
43,43_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.059729765498412738800038113141,1
44,44_0,COMPLETED,BoTorch,0.272818204551137810653926862869,224,0.100000000000000005551115123126,0.170626964876806758164562438651,1
45,45_0,FAILED,BoTorch,,190,0.001000000000000000020816681712,0.000000000000000000000000000000,3
46,46_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
47,47_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4
48,48_0,COMPLETED,BoTorch,0.289072268067016802106650175119,249,0.250000000000000000000000000000,0.200000000000000011102230246252,4
49,49_0,COMPLETED,BoTorch,0.294573643410852681334688440984,245,0.250000000000000000000000000000,0.129810919441300659515903248575,2
50,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
51,51_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.250000000000000000000000000000,0.006866049574184560078116135173,2
52,52_0,COMPLETED,BoTorch,0.216804201050262546068836400082,100,0.010000000000000000208166817117,0.108355869800822068871326564476,1
53,53_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.200000000000000011102230246252,4
54,54_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.106322634362150864051344001382,1
55,55_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.105717243358629600646914070694,1
56,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
57,57_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.025000000000000001387778780781,0.093779770968284825727323550382,4
58,58_0,COMPLETED,BoTorch,0.292323080770192533783813360060,215,0.010000000000000000208166817117,0.115086596391147585882741566365,4
59,59_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.001000000000000000020816681712,0.200000000000000011102230246252,4
60,60_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.096935564253627565234339158451,4
61,61_0,COMPLETED,BoTorch,0.242560640160039975299355319294,163,0.100000000000000005551115123126,0.014601076067599513819139644966,4
62,62_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.085909866664424711091285757902,1
63,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
64,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
65,65_0,COMPLETED,BoTorch,0.225806451612903247294639186293,100,0.010000000000000000208166817117,0.081216257540240352486016206512,1
66,66_0,FAILED,BoTorch,,266,0.001000000000000000020816681712,0.000000000000000000000000000000,4
67,67_0,COMPLETED,BoTorch,0.256814203550887687477199960995,177,0.005000000000000000104083408559,0.067131592174801385519700147597,4
68,68_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.090734185904579156556337693473,4
69,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
70,70_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.129410088665140005081966023681,4
71,71_0,COMPLETED,BoTorch,0.244811202800700122850230400218,115,0.001000000000000000020816681712,0.043198331606128993753745959339,4
72,72_0,COMPLETED,BoTorch,0.276069017254313542331090047810,180,0.050000000000000002775557561563,0.027794026254146522725285706201,4
73,73_0,COMPLETED,BoTorch,0.232308077019254821671268018690,128,0.250000000000000000000000000000,0.128478886696705901782067371641,3
74,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
75,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
76,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
77,77_0,FAILED,BoTorch,,233,0.250000000000000000000000000000,0.000000000000000000000000000000,3
78,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
79,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
80,80_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.077600927671389985373906483801,1
81,81_0,COMPLETED,BoTorch,0.232558139534883689947264429065,100,0.001000000000000000020816681712,0.147831743427241896204904492151,4
82,82_0,FAILED,BoTorch,,257,0.250000000000000000000000000000,0.000000000000000000000000000000,1
83,83_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
84,84_0,FAILED,BoTorch,,183,0.250000000000000000000000000000,0.000000000000000000000000000000,2
85,85_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
86,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
87,87_0,FAILED,BoTorch,,232,0.100000000000000005551115123126,0.000000000000000000000000000000,2
88,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
89,89_0,FAILED,BoTorch,,191,0.250000000000000000000000000000,0.000000000000000000000000000000,3
90,90_0,COMPLETED,BoTorch,0.310327581895473825213116469968,285,0.100000000000000005551115123126,0.087026870679377157924427876878,4
91,91_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.250000000000000000000000000000,0.006872404276689253466159357231,2
92,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
93,93_0,COMPLETED,BoTorch,0.319579894973743394714915666555,170,0.100000000000000005551115123126,0.035728821249001528614908096415,3
94,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
95,95_0,COMPLETED,BoTorch,0.259314828707176814326373914810,183,0.025000000000000001387778780781,0.050710015386007938065215938650,4
96,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
97,97_0,COMPLETED,BoTorch,0.265316329082270541128707463940,196,0.050000000000000002775557561563,0.032618923786967415900939215589,3
98,98_0,COMPLETED,BoTorch,0.249312328082020528974283024581,154,0.005000000000000000104083408559,0.012694664077119818812455775969,4
99,99_0,FAILED,BoTorch,,250,0.250000000000000000000000000000,0.000000000000000000000000000000,4
100,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
101,101_0,COMPLETED,BoTorch,0.262315578894723677727540689375,193,0.050000000000000002775557561563,0.101669647441252716801862732154,4
102,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
103,85_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
104,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
105,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
106,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
107,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
108,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
109,109_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.050000000000000002775557561563,0.098295268134416524663521386174,4
110,110_0,COMPLETED,BoTorch,0.260065016254063530176665608451,178,0.005000000000000000104083408559,0.021807185621937494324207307272,4
111,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
112,112_0,COMPLETED,BoTorch,0.222555638909727404595173538837,116,0.001000000000000000020816681712,0.048331853330789986689097759154,4
113,113_0,COMPLETED,BoTorch,0.267066766691672952127589724114,169,0.010000000000000000208166817117,0.018648485564222161414704714844,3
114,114_0,COMPLETED,BoTorch,0.287321830457614391107767914946,228,0.010000000000000000208166817117,0.039446095795129983152538244440,4
115,115_0,COMPLETED,BoTorch,0.235558889722430553348431203631,142,0.025000000000000001387778780781,0.087632654575256763163082496249,4
116,116_0,COMPLETED,BoTorch,0.249312328082020528974283024581,154,0.100000000000000005551115123126,0.064897217770104737022407448421,4
117,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
118,118_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.056162085787583908291775713906,4
119,119_0,COMPLETED,BoTorch,0.247561890472618117975400764408,154,0.005000000000000000104083408559,0.012700182286472237389030048860,4
120,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
121,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
122,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
123,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
124,124_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.080916029449961845987360220533,1
125,125_0,COMPLETED,BoTorch,0.292823205801450381358108643326,256,0.010000000000000000208166817117,0.081671389557463253128233304778,3
126,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
127,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
128,128_0,COMPLETED,BoTorch,0.233808452113028253371851405973,132,0.050000000000000002775557561563,0.194664398985279291087735487054,4
129,129_0,COMPLETED,BoTorch,0.249312328082020528974283024581,149,0.050000000000000002775557561563,0.115717772861261017358636138397,4
130,50_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
131,131_0,COMPLETED,BoTorch,0.253313328332082976501737903163,161,0.005000000000000000104083408559,0.072350303959938883080482696641,4
132,132_0,FAILED,BoTorch,,273,0.250000000000000000000000000000,0.000000000000000000000000000000,4
133,133_0,COMPLETED,BoTorch,0.258314578644661119177783348277,115,0.001000000000000000020816681712,0.043232179473651689838309408742,4
134,134_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.081086735412702726222278215573,1
135,135_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.033604323227361482251396296306,4
136,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
137,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
138,138_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,4
139,139_0,COMPLETED,BoTorch,0.280070017504376100880847388908,198,0.010000000000000000208166817117,0.054554901597087306075462009858,4
140,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
141,141_0,COMPLETED,BoTorch,0.223055763940985252169468822103,117,0.025000000000000001387778780781,0.076458015051571945330799451312,1
142,142_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.075794577395037032729874226789,1
143,143_0,COMPLETED,BoTorch,0.218804701175293825343715070630,113,0.010000000000000000208166817117,0.078277427219997086638159089489,1
144,144_0,COMPLETED,BoTorch,0.214553638409602398517961319158,111,0.025000000000000001387778780781,0.076677726639996668378529420806,1
145,145_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.082033249829531917907132765322,1
146,146_0,COMPLETED,BoTorch,0.229307326831707958270101244125,111,0.010000000000000000208166817117,0.077808241936501418289928722061,1
147,147_0,COMPLETED,BoTorch,0.243810952738184538723942296201,112,0.025000000000000001387778780781,0.079934142304802516254547128938,1
148,148_0,COMPLETED,BoTorch,0.229307326831707958270101244125,111,0.010000000000000000208166817117,0.078606658097365778026244242938,1
149,149_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.093976912011139987490615510524,3
150,150_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.250000000000000000000000000000,0.073873975434035066278681824770,1
151,151_0,COMPLETED,BoTorch,0.320580145036259089863506233087,368,0.050000000000000002775557561563,0.066990045419658111880423234652,1
152,152_0,COMPLETED,BoTorch,0.219804951237809409470003174647,110,0.025000000000000001387778780781,0.077671858577057412142963244150,1
153,153_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.078286201019343124030136493730,1
154,154_0,COMPLETED,BoTorch,0.214553638409602398517961319158,111,0.025000000000000001387778780781,0.080940034759783882623018769209,1
155,155_0,COMPLETED,BoTorch,0.237059264816204096071317053429,112,0.010000000000000000208166817117,0.078931929702148517780102565666,1
156,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
157,157_0,COMPLETED,BoTorch,0.243810952738184538723942296201,112,0.025000000000000001387778780781,0.078488616787303983057810796709,1
158,158_0,COMPLETED,BoTorch,0.273818454613653394780214966886,201,0.025000000000000001387778780781,0.048806280926064607439407438960,3
159,159_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.090561233853479583544299202913,1
160,160_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.091265011881889443468018896510,1
161,161_0,FAILED,BoTorch,,153,0.001000000000000000020816681712,0.000000000000000000000000000000,3
162,162_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.088714849464212777729876791000,1
163,161_0,FAILED,BoTorch,,153,0.001000000000000000020816681712,0.000000000000000000000000000000,3
164,164_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.089125128248580320899918660871,1
165,165_0,COMPLETED,BoTorch,0.230807701925481389970684631407,105,0.005000000000000000104083408559,0.033914710209900660042858788756,3
166,166_0,COMPLETED,BoTorch,0.210302575643910971692207567685,100,0.025000000000000001387778780781,0.084885830043585172588471721156,1
167,167_0,COMPLETED,BoTorch,0.258314578644661119177783348277,184,0.001000000000000000020816681712,0.200000000000000011102230246252,4
168,168_0,COMPLETED,BoTorch,0.413353338334583653157494609331,1000,0.250000000000000000000000000000,0.200000000000000011102230246252,4
169,169_0,FAILED,BoTorch,,151,0.001000000000000000020816681712,0.000000000000000000000000000000,3
170,170_0,COMPLETED,BoTorch,0.413353338334583653157494609331,1000,0.025000000000000001387778780781,0.069184708041460846184023125716,3
171,171_0,COMPLETED,BoTorch,0.398849712428107072703653557255,967,0.025000000000000001387778780781,0.000648655421756750443819383722,4
172,172_0,COMPLETED,BoTorch,0.272318079519879963079631579603,168,0.250000000000000000000000000000,0.139266240608920038868134838594,4
173,173_0,COMPLETED,BoTorch,0.262815703925981525301835972641,184,0.001000000000000000020816681712,0.200000000000000011102230246252,3
174,174_0,COMPLETED,BoTorch,0.305076269067266814261074614478,297,0.050000000000000002775557561563,0.086755174611452920419019108067,3
175,175_0,COMPLETED,BoTorch,0.376594148537134243426294233359,605,0.005000000000000000104083408559,0.058045951165739989585645730585,2
176,176_0,COMPLETED,BoTorch,0.267066766691672952127589724114,169,0.100000000000000005551115123126,0.200000000000000011102230246252,3
177,177_0,COMPLETED,BoTorch,0.307326831707926961811949695402,288,0.100000000000000005551115123126,0.200000000000000011102230246252,4
178,178_0,COMPLETED,BoTorch,0.266066516629157256978999157582,167,0.250000000000000000000000000000,0.163251171271723283240362434299,4
179,179_0,RUNNING,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,3
180,180_0,COMPLETED,BoTorch,0.242560640160039975299355319294,163,0.050000000000000002775557561563,0.200000000000000011102230246252,4
181,181_0,COMPLETED,BoTorch,0.263815953988497109428124076658,166,0.250000000000000000000000000000,0.200000000000000011102230246252,4
182,182_0,COMPLETED,BoTorch,0.266566641660415104553294440848,172,0.025000000000000001387778780781,0.200000000000000011102230246252,3
183,183_0,COMPLETED,BoTorch,0.308327081770442656960540261935,160,0.010000000000000000208166817117,0.077079054102868360676126258113,3
184,184_0,COMPLETED,BoTorch,0.313578394598649667912582117424,363,0.001000000000000000020816681712,0.200000000000000011102230246252,4
185,185_0,COMPLETED,BoTorch,0.289072268067016802106650175119,249,0.025000000000000001387778780781,0.200000000000000011102230246252,3
186,186_0,COMPLETED,BoTorch,0.324831207801950516689259984560,305,0.050000000000000002775557561563,0.092948440917075603184827059522,3
187,187_0,COMPLETED,BoTorch,0.305826456614153530111366308120,299,0.010000000000000000208166817117,0.196773036907419873742242089065,3
188,188_0,COMPLETED,BoTorch,0.279569892473118253306552105641,219,0.010000000000000000208166817117,0.177131950551116412739816041721,3
189,189_0,COMPLETED,BoTorch,0.263815953988497109428124076658,166,0.250000000000000000000000000000,0.200000000000000011102230246252,3
190,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
191,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
192,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
193,193_0,FAILED,BoTorch,,156,0.001000000000000000020816681712,0.000000000000000000000000000000,3
194,194_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.101876220952681062481559592925,1
195,193_0,FAILED,BoTorch,,156,0.001000000000000000020816681712,0.000000000000000000000000000000,3
196,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
197,197_0,FAILED,BoTorch,,138,0.001000000000000000020816681712,0.000000000000000000000000000000,3
198,198_0,COMPLETED,BoTorch,0.225556389097274267996340313402,100,0.001000000000000000020816681712,0.037994293868149099646647215422,4
199,199_0,FAILED,BoTorch,,147,0.001000000000000000020816681712,0.000000000000000000000000000000,3
200,200_0,COMPLETED,BoTorch,0.223805951487871968019760515745,124,0.100000000000000005551115123126,0.047017241312899360483612554162,3
201,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
202,202_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.091924450890514430856370609035,3
203,203_0,COMPLETED,BoTorch,0.224806201550387552146048619761,115,0.001000000000000000020816681712,0.041915728710977984139418595078,3
204,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
205,205_0,COMPLETED,BoTorch,0.216304076019004698494541116816,113,0.001000000000000000020816681712,0.042060731604721214582642829782,4
206,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
207,207_0,COMPLETED,BoTorch,0.231807951987996974096972735424,126,0.005000000000000000104083408559,0.089296087155725720196919326099,4
208,208_0,COMPLETED,BoTorch,0.255063765941485387500620163337,148,0.005000000000000000104083408559,0.106549246578776984906156144461,4
209,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
210,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
211,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
212,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
213,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
214,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
215,215_0,COMPLETED,BoTorch,0.229557389347336826546097654500,100,0.005000000000000000104083408559,0.002774243316007354853702793207,4
216,216_0,COMPLETED,BoTorch,0.405101275318829667781983516761,873,0.001000000000000000020816681712,0.065203717354405318906707123006,4
217,217_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,3
218,218_0,COMPLETED,BoTorch,0.233058264566141537521559712332,106,0.250000000000000000000000000000,0.028573333614170373651042211804,2
219,219_0,COMPLETED,BoTorch,0.307826956739184809386244978668,140,0.005000000000000000104083408559,0.125793879863307866973087811857,4
220,220_0,FAILED,BoTorch,,120,0.010000000000000000208166817117,0.000000000000000000000000000000,3
221,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
222,222_0,COMPLETED,BoTorch,0.354588647161790393447233782354,516,0.005000000000000000104083408559,0.200000000000000011102230246252,4
223,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
224,224_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.085149133521580211425572315420,2
225,225_0,COMPLETED,BoTorch,0.233808452113028253371851405973,132,0.010000000000000000208166817117,0.188744813439255088027834972308,3
226,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
227,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
228,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
229,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
230,230_0,COMPLETED,BoTorch,0.282320580145036248431722469832,100,0.001000000000000000020816681712,0.038138711481821963023008947857,4
231,231_0,COMPLETED,BoTorch,0.225556389097274267996340313402,112,0.001000000000000000020816681712,0.058898656224075411624863107818,3
232,232_0,COMPLETED,BoTorch,0.303075768942235534986195943929,298,0.025000000000000001387778780781,0.181126345411895595116646973111,3
233,233_0,COMPLETED,BoTorch,0.256814203550887687477199960995,186,0.250000000000000000000000000000,0.200000000000000011102230246252,4
234,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
235,235_0,COMPLETED,BoTorch,0.254563640910227539926324880071,207,0.010000000000000000208166817117,0.076815116231982533134825530396,4
236,236_0,COMPLETED,BoTorch,0.282320580145036248431722469832,204,0.010000000000000000208166817117,0.192900806479272907134614456481,3
237,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
238,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
239,239_0,COMPLETED,BoTorch,0.249562390597649397250279434957,125,0.025000000000000001387778780781,0.200000000000000011102230246252,4
240,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
241,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
242,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
243,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
244,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
245,245_0,COMPLETED,BoTorch,0.322330582645661389840086030745,314,0.010000000000000000208166817117,0.177513676964164202054519137164,1
246,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
247,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
248,248_0,COMPLETED,BoTorch,0.222805701425356383893472411728,107,0.001000000000000000020816681712,0.063553068942243190475593905830,4
249,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
250,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
251,251_0,COMPLETED,BoTorch,0.216054013503375830218544706440,113,0.001000000000000000020816681712,0.042065755358342678260630265186,4
252,252_0,COMPLETED,BoTorch,0.254313578394598671650328469696,164,0.025000000000000001387778780781,0.158831795514028983884458057219,3
253,253_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.107406421025384835044036435647,4
254,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
255,255_0,COMPLETED,BoTorch,0.368092023005751389774786730413,555,0.005000000000000000104083408559,0.105177191352630539089574313039,1
256,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
257,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
258,258_0,COMPLETED,BoTorch,0.281820455113778400857427186565,120,0.250000000000000000000000000000,0.017551693754480384573879447885,2
259,259_0,FAILED,BoTorch,,103,0.001000000000000000020816681712,0.000000000000000000000000000000,4
260,260_0,FAILED,BoTorch,,210,0.250000000000000000000000000000,0.000000000000000000000000000000,1
261,261_0,FAILED,BoTorch,,154,0.001000000000000000020816681712,0.000000000000000000000000000000,4
262,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
263,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
264,264_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.097343657728073004764546283241,1
265,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
266,266_0,FAILED,BoTorch,,115,0.001000000000000000020816681712,0.000000000000000000000000000000,3
267,267_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.100000000000000005551115123126,0.080269216156161171671357124069,3
268,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
269,269_0,COMPLETED,BoTorch,0.319829957489372374013214539445,295,0.001000000000000000020816681712,0.108620321475835807101262275864,4
270,270_0,COMPLETED,BoTorch,0.260065016254063530176665608451,178,0.100000000000000005551115123126,0.186825317231154058861264388725,2
271,271_0,FAILED,BoTorch,,133,0.250000000000000000000000000000,0.000000000000000000000000000000,4
272,272_0,FAILED,BoTorch,,101,0.005000000000000000104083408559,0.000000000000000000000000000000,3
273,273_0,COMPLETED,BoTorch,0.222805701425356383893472411728,114,0.250000000000000000000000000000,0.000866549584994571867914425756,2
274,30_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,3
275,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
276,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
277,277_0,COMPLETED,BoTorch,0.301825456364090971561608967022,180,0.050000000000000002775557561563,0.200000000000000011102230246252,4
278,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
279,279_0,COMPLETED,BoTorch,0.294323580895223813058692030609,264,0.250000000000000000000000000000,0.200000000000000011102230246252,3
280,280_0,COMPLETED,BoTorch,0.235558889722430553348431203631,102,0.001000000000000000020816681712,0.104551787020007405648591713998,2
281,281_0,FAILED,BoTorch,,104,0.001000000000000000020816681712,0.000000000000000000000000000000,3
282,282_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.098937027884926059817516375006,1
283,283_0,FAILED,BoTorch,,116,0.001000000000000000020816681712,0.000000000000000000000000000000,3
284,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
285,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
286,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
287,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
288,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
289,289_0,COMPLETED,BoTorch,0.263315828957239261853828793392,100,0.001000000000000000020816681712,0.037732041844681492304136583016,3
290,290_0,FAILED,BoTorch,,131,0.001000000000000000020816681712,0.000000000000000000000000000000,3
291,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
292,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
293,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
294,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
295,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
296,296_0,COMPLETED,BoTorch,0.224806201550387552146048619761,100,0.001000000000000000020816681712,0.038209858080888031706123797449,4
297,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
298,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
299,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
300,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
301,301_0,COMPLETED,BoTorch,0.281570392598149532581430776190,227,0.025000000000000001387778780781,0.074601440920845557558394034459,4
302,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
303,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
304,304_0,FAILED,BoTorch,,442,0.005000000000000000104083408559,0.000000000000000000000000000000,1
305,305_0,COMPLETED,BoTorch,0.396099024756189077578483193065,698,0.025000000000000001387778780781,0.075019253222113863044384629575,4
306,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
307,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
308,308_0,COMPLETED,BoTorch,0.234558639659914969222143099614,100,0.001000000000000000020816681712,0.038009937163653993719147905495,4
309,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
310,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
311,311_0,COMPLETED,BoTorch,0.350837709427356814195775314147,492,0.005000000000000000104083408559,0.110338863498554096143067226876,1
312,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
313,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
314,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
315,315_0,COMPLETED,BoTorch,0.394848712178044514153896216158,708,0.025000000000000001387778780781,0.076822910841062769238263285843,4
316,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
317,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
318,23_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2
319,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
320,320_0,COMPLETED,BoTorch,0.307826956739184809386244978668,309,0.001000000000000000020816681712,0.200000000000000011102230246252,2
321,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
322,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
323,323_0,COMPLETED,BoTorch,0.338334583645911513016812932619,438,0.250000000000000000000000000000,0.089149457290131350895023842895,2
324,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
325,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
326,326_0,COMPLETED,BoTorch,0.403600900225056236081400129478,822,0.025000000000000001387778780781,0.200000000000000011102230246252,3
327,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
328,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
329,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
330,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
331,331_0,FAILED,BoTorch,,242,0.001000000000000000020816681712,0.000000000000000000000000000000,2
332,332_0,COMPLETED,BoTorch,0.396849212303075793428774886706,757,0.025000000000000001387778780781,0.199959252023311218060541705199,1
333,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
334,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
335,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
336,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
337,337_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.003644233271601659759908464764,4
338,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
339,339_0,COMPLETED,BoTorch,0.214303575893973530241964908782,123,0.001000000000000000020816681712,0.049161433686879987825513182997,4
340,26_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,4
341,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
342,342_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.005000000000000000104083408559,0.002785043614937771343925687617,4
343,343_0,COMPLETED,BoTorch,0.298824706176544108160442192457,253,0.025000000000000001387778780781,0.103692612360672800631000711746,4
344,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
345,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
346,346_0,FAILED,BoTorch,,127,0.001000000000000000020816681712,0.000000000000000000000000000000,4
347,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
348,348_0,FAILED,BoTorch,,128,0.001000000000000000020816681712,0.000000000000000000000000000000,4
349,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
350,350_0,COMPLETED,BoTorch,0.225556389097274267996340313402,100,0.001000000000000000020816681712,0.038014962924231454621804005001,4
351,351_0,FAILED,BoTorch,,126,0.001000000000000000020816681712,0.000000000000000000000000000000,4
352,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
353,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
354,354_0,COMPLETED,BoTorch,0.294323580895223813058692030609,205,0.050000000000000002775557561563,0.200000000000000011102230246252,4
355,355_0,COMPLETED,BoTorch,0.226056514128532115570635596669,113,0.001000000000000000020816681712,0.029293654769387195146990165995,4
356,356_0,FAILED,BoTorch,,212,0.250000000000000000000000000000,0.000000000000000000000000000000,4
357,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
358,348_0,FAILED,BoTorch,,128,0.001000000000000000020816681712,0.000000000000000000000000000000,4
359,359_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.102077777838947142408088097909,1
360,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
361,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
362,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
363,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
364,364_0,COMPLETED,BoTorch,0.244061015253813406999938706576,138,0.001000000000000000020816681712,0.045118079302409623554392936740,2
365,365_0,COMPLETED,BoTorch,0.233558389597399385095854995598,137,0.250000000000000000000000000000,0.102080727218184383331411879681,4
366,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4
367,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4
368,368_0,COMPLETED,BoTorch,0.328582145536384095940718452766,250,0.025000000000000001387778780781,0.200000000000000011102230246252,4
369,369_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.005000000000000000104083408559,0.059094712025506257457863057425,4
370,370_0,FAILED,BoTorch,,169,0.001000000000000000020816681712,0.000000000000000000000000000000,1
371,371_0,RUNNING,BoTorch,,520,0.005000000000000000104083408559,0.115862528376004414454314428440,1
372,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
373,373_0,COMPLETED,BoTorch,0.287821955488872238682063198212,226,0.010000000000000000208166817117,0.187689834724264648091462959201,2
374,374_0,COMPLETED,BoTorch,0.356089022255563936170119632152,514,0.005000000000000000104083408559,0.117203552200532523652753980059,1
375,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
376,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4
377,377_0,COMPLETED,BoTorch,0.222805701425356383893472411728,104,0.005000000000000000104083408559,0.059090670981739881750804954663,4
378,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
379,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
380,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
381,381_0,FAILED,BoTorch,,135,0.001000000000000000020816681712,0.000000000000000000000000000000,4
382,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
383,383_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,4
384,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
385,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
386,386_0,COMPLETED,BoTorch,0.235808952238059532646730076522,136,0.005000000000000000104083408559,0.047075446065609448387245805634,4
387,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
388,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
389,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
390,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
391,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4
392,392_0,COMPLETED,BoTorch,0.234808702175543837498139509989,138,0.050000000000000002775557561563,0.137539257229414318972615660641,4
393,393_0,FAILED,BoTorch,,143,0.001000000000000000020816681712,0.000000000000000000000000000000,4
394,393_0,FAILED,BoTorch,,143,0.001000000000000000020816681712,0.000000000000000000000000000000,4
395,395_0,COMPLETED,BoTorch,0.270317579394848683804752909055,105,0.005000000000000000104083408559,0.174345284829028612794132868657,3
396,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
397,397_0,COMPLETED,BoTorch,0.397099274818704661704771297082,680,0.005000000000000000104083408559,0.128880008789946209901700058253,3
398,398_0,COMPLETED,BoTorch,0.232808202050512669245563301956,133,0.005000000000000000104083408559,0.006174432960647995710656843471,3
399,399_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,4
400,400_0,COMPLETED,BoTorch,0.377094273568392091000589516625,633,0.001000000000000000020816681712,0.164812799567015444424100678589,3
401,23_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2
402,402_0,FAILED,BoTorch,,146,0.001000000000000000020816681712,0.000000000000000000000000000000,4
403,403_0,COMPLETED,BoTorch,0.294073518379594944782695620233,301,0.050000000000000002775557561563,0.154179740280119992323903943543,3
404,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
405,405_0,COMPLETED,BoTorch,0.375343835958989791024009718967,656,0.005000000000000000104083408559,0.110598722412879477139391326546,3
406,406_0,FAILED,BoTorch,,233,0.005000000000000000104083408559,0.000000000000000000000000000000,3
407,407_0,FAILED,BoTorch,,230,0.005000000000000000104083408559,0.000000000000000000000000000000,4
408,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4
409,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
410,410_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.100734995186227138663781488503,1
411,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
412,412_0,FAILED,BoTorch,,144,0.001000000000000000020816681712,0.000000000000000000000000000000,4
413,383_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,4
414,414_0,COMPLETED,BoTorch,0.285821455363840959407184527663,233,0.005000000000000000104083408559,0.016954031106625421648770313254,4
415,415_0,RUNNING,BoTorch,,113,0.001000000000000000020816681712,0.029316033192138924584613235425,4
416,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
417,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
418,418_0,FAILED,BoTorch,,145,0.001000000000000000020816681712,0.000000000000000000000000000000,4
419,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
420,420_0,COMPLETED,BoTorch,0.255063765941485387500620163337,148,0.100000000000000005551115123126,0.021914020900722232243484910441,3
421,74_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
422,399_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,4
423,402_0,FAILED,BoTorch,,146,0.001000000000000000020816681712,0.000000000000000000000000000000,4
424,424_0,COMPLETED,BoTorch,0.418104526131532927557543644070,798,0.001000000000000000020816681712,0.145136112966548158631496789894,2
425,425_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.050000000000000002775557561563,0.010529777222508399439626636251,3
426,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
427,427_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,3
428,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
429,429_0,COMPLETED,BoTorch,0.253313328332082976501737903163,161,0.100000000000000005551115123126,0.200000000000000011102230246252,4
430,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4
431,431_0,COMPLETED,BoTorch,0.253563390847711955800036776054,146,0.005000000000000000104083408559,0.043630832122724706734206989722,3
432,366_0,FAILED,BoTorch,,139,0.001000000000000000020816681712,0.000000000000000000000000000000,4
433,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
434,434_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,3
435,435_0,FAILED,BoTorch,,165,0.010000000000000000208166817117,0.000000000000000000000000000000,4
436,399_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,4
437,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4
438,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
439,381_0,FAILED,BoTorch,,135,0.001000000000000000020816681712,0.000000000000000000000000000000,4
440,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
441,441_0,COMPLETED,BoTorch,0.263315828957239261853828793392,159,0.250000000000000000000000000000,0.200000000000000011102230246252,4
442,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
443,443_0,COMPLETED,BoTorch,0.249812453113278265526275845332,115,0.010000000000000000208166817117,0.200000000000000011102230246252,4
444,444_0,FAILED,BoTorch,,149,0.001000000000000000020816681712,0.000000000000000000000000000000,2
445,362_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,4
446,446_0,COMPLETED,BoTorch,0.237559389847461832623309874180,108,0.010000000000000000208166817117,0.055165423534351457068858337607,4
447,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4
448,448_0,COMPLETED,BoTorch,0.409852463115778942182032551500,828,0.050000000000000002775557561563,0.104987838324140700385633806491,3
449,408_0,FAILED,BoTorch,,137,0.001000000000000000020816681712,0.000000000000000000000000000000,4
450,450_0,COMPLETED,BoTorch,0.218054513628407109493423376989,111,0.010000000000000000208166817117,0.184924891557410431275343398738,4
451,451_0,COMPLETED,BoTorch,0.219804951237809409470003174647,116,0.001000000000000000020816681712,0.056557617161534082250717858642,4
452,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
453,453_0,COMPLETED,BoTorch,0.231307826956739237544979914674,123,0.001000000000000000020816681712,0.006998235909252846845274298460,4
454,454_0,COMPLETED,BoTorch,0.252063015753938524099453388772,213,0.010000000000000000208166817117,0.092263341324696171441566150406,4
455,455_0,COMPLETED,BoTorch,0.358589647411852951996991123451,454,0.250000000000000000000000000000,0.129329853696432212073474943281,4
456,456_0,COMPLETED,BoTorch,0.252563140785196260651446209522,144,0.025000000000000001387778780781,0.086246433845858061495448509959,4
457,457_0,COMPLETED,BoTorch,0.218054513628407109493423376989,111,0.025000000000000001387778780781,0.099771724707667874820771203304,4
458,458_0,FAILED,BoTorch,,141,0.001000000000000000020816681712,0.000000000000000000000000000000,3
459,459_0,COMPLETED,BoTorch,0.358339584896224083720994713076,484,0.005000000000000000104083408559,0.103100456426519868080582398306,1
460,460_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.099660230246399750253516458542,1
461,197_0,FAILED,BoTorch,,138,0.001000000000000000020816681712,0.000000000000000000000000000000,3
462,462_0,FAILED,BoTorch,,132,0.001000000000000000020816681712,0.000000000000000000000000000000,4
463,463_0,COMPLETED,BoTorch,0.233558389597399385095854995598,137,0.005000000000000000104083408559,0.087213788633888456036657998993,4
464,464_0,RUNNING,BoTorch,,100,0.025000000000000001387778780781,0.106803489014117319877428258224,1
465,465_0,FAILED,BoTorch,,130,0.001000000000000000020816681712,0.000000000000000000000000000000,4
466,197_0,FAILED,BoTorch,,138,0.001000000000000000020816681712,0.000000000000000000000000000000,3
467,467_0,COMPLETED,BoTorch,0.214803700925231266793957729533,100,0.005000000000000000104083408559,0.113063467936606987240821808882,2
468,468_0,FAILED,BoTorch,,118,0.001000000000000000020816681712,0.000000000000000000000000000000,4
469,469_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.097970582964692493055380850819,3
470,470_0,COMPLETED,BoTorch,0.240310077519379827748480238370,138,0.001000000000000000020816681712,0.001068359383450949440241828370,4
471,471_0,FAILED,BoTorch,,144,0.001000000000000000020816681712,0.000000000000000000000000000000,3
472,427_0,FAILED,BoTorch,,142,0.001000000000000000020816681712,0.000000000000000000000000000000,3
473,473_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.097955631970960743704068818261,3
474,474_0,COMPLETED,BoTorch,0.369342335583895953199373707321,582,0.050000000000000002775557561563,0.200000000000000011102230246252,2
475,475_0,COMPLETED,BoTorch,0.349337334333583382495191926864,479,0.005000000000000000104083408559,0.110874967982368621832733879273,1
476,476_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.109587519917548503745052812519,1
477,477_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.108314321565190885277019106070,1
478,478_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.112717130664233902703763590125,1
479,479_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.106477629309613797126132794801,1
480,480_0,COMPLETED,BoTorch,0.257814453613403382625790527527,135,0.001000000000000000020816681712,0.034580827181856177432450749620,4
481,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
482,482_0,COMPLETED,BoTorch,0.376094023505876506874301412608,594,0.010000000000000000208166817117,0.071391435460074689767218103498,4
483,483_0,COMPLETED,BoTorch,0.229307326831707958270101244125,133,0.001000000000000000020816681712,0.040022040532536731771706683958,4
484,484_0,COMPLETED,BoTorch,0.234058514628657121647847816348,128,0.001000000000000000020816681712,0.042236986856669260503860385825,4
485,485_0,COMPLETED,BoTorch,0.364841210302575658097623545473,545,0.005000000000000000104083408559,0.179887075852265265751839251607,3
486,486_0,COMPLETED,BoTorch,0.357339334833708388572404146544,476,0.025000000000000001387778780781,0.097981242677061333723464997547,4
487,487_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.108006905580991977022797811969,1
488,488_0,COMPLETED,BoTorch,0.228057014253563394845514267217,137,0.001000000000000000020816681712,0.043458904588374654143212438839,4
489,489_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.109970037435713943740900333523,1
490,490_0,COMPLETED,BoTorch,0.243310827706926691149647012935,100,0.001000000000000000020816681712,0.002400710837877469572970712264,1
491,491_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.113228570859403743220639171341,1
492,492_0,COMPLETED,BoTorch,0.330582645661415375215597123315,404,0.100000000000000005551115123126,0.105164585147430836298276801699,1
493,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
494,494_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.113494349156980783854820060697,1
495,434_0,FAILED,BoTorch,,140,0.001000000000000000020816681712,0.000000000000000000000000000000,3
496,496_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.111564450867507616860230257316,1
497,497_0,COMPLETED,BoTorch,0.281570392598149532581430776190,100,0.010000000000000000208166817117,0.159922395950273243947492574080,3
498,79_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
499,499_0,COMPLETED,BoTorch,0.215053763440860246092256602424,100,0.025000000000000001387778780781,0.112127652541063327351622547212,1
500,500_0,COMPLETED,BoTorch,0.245311327831957970424525683484,161,0.005000000000000000104083408559,0.000485305960241321464321284651,2
501,501_0,RUNNING,BoTorch,,100,0.025000000000000001387778780781,0.113949093451803262766475199896,1
502,502_0,RUNNING,BoTorch,,100,0.010000000000000000208166817117,0.104977585740612180953412746476,3
503,23_0,RUNNING,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2
504,79_0,RUNNING,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
</pre>
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<script>
createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
<h1> Errors</h1>
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<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_OutdoorObjects/0/single_runs/2252001/2252001_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252004/2252004_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252008/2252008_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252015/2252015_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252018/2252018_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252020/2252020_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252023/2252023_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 190 confidence 0.001 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=(190, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252024/2252024_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 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_OutdoorObjects/0/single_runs/2252028/2252028_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252034/2252034_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252041/2252041_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252042/2252042_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252047/2252047_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252044/2252044_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 266 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=(266, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252052/2252052_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252053/2252053_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252054/2252054_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252055/2252055_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 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=(233, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252056/2252056_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252057/2252057_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252060/2252060_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 257 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=(257, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252061/2252061_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252062/2252062_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.25 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=(183, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252064/2252064_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252065/2252065_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 232 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=(232, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252066/2252066_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252067/2252067_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 191 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=(191, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252070/2252070_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252072/2252072_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252074/2252074_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252077/2252077_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 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=(250, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252078/2252078_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252080/2252080_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252082/2252082_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252083/2252083_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252084/2252084_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252085/2252085_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252086/2252086_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252089/2252089_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252095/2252095_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252098/2252098_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252099/2252099_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252100/2252100_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252101/2252101_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252104/2252104_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252106/2252106_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252109/2252109_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252111/2252111_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 273 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=(273, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252115/2252115_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252116/2252116_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252117/2252117_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 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_OutdoorObjects/0/single_runs/2252119/2252119_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252135/2252135_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252140/2252140_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.001 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=(153, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252142/2252142_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.001 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=(153, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252148/2252148_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.001 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=(151, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252171/2252171_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252172/2252172_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252173/2252173_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252174/2252174_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.001 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=(156, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252176/2252176_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.001 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=(156, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252177/2252177_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252178/2252178_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 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=(138, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252180/2252180_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 147 confidence 0.001 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=(147, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252182/2252182_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252185/2252185_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252188/2252188_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252191/2252191_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252192/2252192_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252193/2252193_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252194/2252194_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252195/2252195_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252196/2252196_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252200/2252200_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 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_OutdoorObjects/0/single_runs/2252203/2252203_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 120 confidence 0.01 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=(120, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252204/2252204_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252206/2252206_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252209/2252209_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252210/2252210_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252211/2252211_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252212/2252212_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252217/2252217_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252220/2252220_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252221/2252221_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252318/2252318_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252333/2252333_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252341/2252341_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252349/2252349_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252357/2252357_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252380/2252380_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252388/2252388_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252405/2252405_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252413/2252413_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252453/2252453_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252462/2252462_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252470/2252470_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252630/2252630_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 103 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=(103, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252643/2252643_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 210 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=(210, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252652/2252652_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 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=(154, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252655/2252655_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2252668/2252668_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252687/2252687_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252695/2252695_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.001 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=(115, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252711/2252711_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252735/2252735_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 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=(133, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252743/2252743_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 101 confidence 0.005 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=(101, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252766/2252766_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2252916/2252916_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252924/2252924_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252947/2252947_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252971/2252971_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.001 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=(104, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2252994/2252994_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.001 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=(116, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253009/2253009_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253024/2253024_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253032/2253032_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253047/2253047_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253055/2253055_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253078/2253078_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 131 confidence 0.001 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=(131, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253086/2253086_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253221/2253221_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253236/2253236_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253244/2253244_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253259/2253259_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253282/2253282_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253283/2253283_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253298/2253298_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253313/2253313_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253329/2253329_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253337/2253337_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253352/2253352_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 442 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=(442, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253375/2253375_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253383/2253383_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253406/2253406_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253542/2253542_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253572/2253572_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253580/2253580_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253588/2253588_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253618/2253618_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253626/2253626_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253634/2253634_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2253642/2253642_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253672/2253672_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253680/2253680_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253703/2253703_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253718/2253718_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253798/2253798_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253813/2253813_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253828/2253828_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253836/2253836_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253851/2253851_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 242 confidence 0.001 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=(242, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253874/2253874_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253882/2253882_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253890/2253890_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253905/2253905_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253935/2253935_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253951/2253951_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2253966/2253966_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254144/2254144_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254159/2254159_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254174/2254174_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 127 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=(127, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254189/2254189_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254197/2254197_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 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=(128, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254212/2254212_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254235/2254235_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 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=(126, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254249/2254249_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254258/2254258_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254296/2254296_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 212 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=(212, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254309/2254309_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254326/2254326_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 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=(128, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254349/2254349_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254519/2254519_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254528/2254528_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254543/2254543_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254575/2254575_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254589/2254589_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254625/2254625_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 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=(169, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254657/2254657_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254689/2254689_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254697/2254697_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254727/2254727_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254869/2254869_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254891/2254891_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254899/2254899_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 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=(135, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254907/2254907_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254929/2254929_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254937/2254937_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2254952/2254952_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254982/2254982_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2254990/2254990_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255005/2255005_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255020/2255020_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255035/2255035_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255058/2255058_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 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=(143, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255073/2255073_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 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=(143, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255264/2255264_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255302/2255302_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255332/2255332_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 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_OutdoorObjects/0/single_runs/2255340/2255340_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 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=(146, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255370/2255370_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255400/2255400_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 233 confidence 0.005 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=(233, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255415/2255415_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.005 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=(230, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255430/2255430_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255445/2255445_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255475/2255475_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255639/2255639_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 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=(144, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255655/2255655_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255689/2255689_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255705/2255705_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255720/2255720_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 145 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=(145, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255737/2255737_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2255761/2255761_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 OutdoorObjects 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_OutdoorObjects/0/single_runs/2255777/2255777_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255793/2255793_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 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=(146, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255832/2255832_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255846/2255846_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2255862/2255862_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256048/2256048_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256078/2256078_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 139 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=(139, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256093/2256093_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256108/2256108_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256130/2256130_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 165 confidence 0.01 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=(165, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256145/2256145_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256153/2256153_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256175/2256175_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256190/2256190_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 135 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=(135, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256205/2256205_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256235/2256235_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256272/2256272_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 149 confidence 0.001 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=(149, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256462/2256462_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256492/2256492_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256515/2256515_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 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=(137, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256567/2256567_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256657/2256657_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.001 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=(141, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256871/2256871_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 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=(138, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256886/2256886_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 132 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=(132, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256931/2256931_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 130 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=(130, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256946/2256946_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.001 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=(138, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2256983/2256983_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 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=(118, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257028/2257028_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.001 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=(144, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257043/2257043_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 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=(142, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257381/2257381_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257764/2257764_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257794/2257794_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 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=(140, 0)) while a minimum of 1
Out file /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_OutdoorObjects/0/single_runs/2257825/2257825_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 OutdoorObjects 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 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=(100, 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
1727442452,476.59375,49.7
1727442452,476.59375,50.0
1727442452,476.59375,49.7
1727442452,476.59375,57.1
1727442452,476.59375,40.0
1727442452,476.59375,49.9
1727442452,476.59375,40.6
1727442498,485.51953125,49.8
1727442498,485.51953125,39.4
1727442498,485.51953125,50.2
1727442498,485.51953125,37.5
1727442504,486.4453125,49.7
1727442504,486.4453125,53.1
1727442504,486.4453125,48.8
1727442504,486.4453125,48.6
1727442506,486.49609375,49.9
1727442506,486.49609375,55.6
1727442506,486.49609375,51.2
1727442506,486.49609375,40.6
1727442508,486.49609375,49.9
1727442508,486.49609375,54.2
1727442508,486.49609375,48.5
1727442508,486.49609375,47.5
1727442511,486.5234375,49.9
1727442511,486.5234375,38.2
1727442511,486.5234375,52.5
1727442511,486.5234375,36.4
1727442513,486.52734375,49.9
1727442513,486.52734375,40.5
1727442513,486.52734375,52.8
1727442513,486.52734375,37.1
1727442515,486.52734375,49.9
1727442515,486.52734375,50.0
1727442515,486.52734375,48.5
1727442515,486.52734375,37.5
1727442518,486.53125,49.9
1727442518,486.53125,55.3
1727442518,486.53125,46.7
1727442518,486.53125,57.8
1727442520,486.53125,49.9
1727442520,486.53125,56.5
1727442520,486.53125,48.1
1727442520,486.53125,57.8
1727442522,486.53125,49.8
1727442522,486.53125,53.2
1727442522,486.53125,48.1
1727442522,486.53125,56.8
1727442525,486.53125,49.9
1727442525,486.53125,38.2
1727442525,486.53125,52.9
1727442525,486.53125,38.7
1727442527,486.53125,49.9
1727442527,486.53125,54.3
1727442527,486.53125,47.3
1727442527,486.53125,55.6
1727442529,486.53125,49.9
1727442529,486.53125,50.0
1727442529,486.53125,48.3
1727442529,486.53125,56.8
1727442532,486.53125,49.8
1727442532,486.53125,57.4
1727442532,486.53125,45.5
1727442532,486.53125,55.6
1727442534,486.53125,49.9
1727442534,486.53125,54.3
1727442534,486.53125,47.3
1727442534,486.53125,55.6
1727442536,486.53125,49.9
1727442536,486.53125,53.2
1727442536,486.53125,46.4
1727442536,486.53125,56.3
1727442538,486.53125,49.9
1727442538,486.53125,54.3
1727442538,486.53125,46.8
1727442538,486.53125,56.5
1727442541,486.53125,49.9
1727442541,486.53125,38.2
1727442541,486.53125,53.3
1727442541,486.53125,39.4
1727442543,486.53125,49.9
1727442543,486.53125,51.2
1727442543,486.53125,52.8
1727442543,486.53125,39.4
1727442545,486.53125,49.9
1727442545,486.53125,38.2
1727442545,486.53125,52.5
1727442545,486.53125,40.6
1727442547,486.53125,49.9
1727442547,486.53125,51.2
1727442547,486.53125,48.6
1727442547,486.53125,40.6
1727442550,486.53125,49.9
1727442550,486.53125,51.0
1727442550,486.53125,48.1
1727442550,486.53125,56.5
1727442552,486.53125,49.9
1727442552,486.53125,53.2
1727442552,486.53125,46.4
1727442552,486.53125,57.8
1727442554,486.53125,49.9
1727442554,486.53125,52.1
1727442554,486.53125,51.6
1727442554,486.53125,40.6
1727442557,486.53125,49.9
1727442557,486.53125,54.2
1727442557,486.53125,45.9
1727442557,486.53125,56.5
1727442559,486.53125,49.9
1727442559,486.53125,53.2
1727442559,486.53125,50.4
1727442559,486.53125,38.7
1727442561,486.53125,49.9
1727442561,486.53125,55.6
1727442561,486.53125,46.8
1727442561,486.53125,56.8
1727442564,486.625,49.9
1727442564,486.625,54.3
1727442564,486.625,50.4
1727442564,486.625,40.6
1727442566,486.625,49.9
1727442566,486.625,55.3
1727442566,486.625,51.2
1727442566,486.625,37.5
1727442568,486.625,49.9
1727442568,486.625,43.2
1727442568,486.625,51.2
1727442568,486.625,42.9
1727442572,486.66015625,49.9
1727442572,486.66015625,54.3
1727442572,486.66015625,51.6
1727442572,486.66015625,37.5
1727442575,486.66796875,49.9
1727442575,486.66796875,55.3
1727442575,486.66796875,45.6
1727442575,486.66796875,56.8
1727442578,486.7265625,49.9
1727442578,486.7265625,55.1
1727442578,486.7265625,45.5
1727442578,486.7265625,54.3
1727442581,486.7265625,49.9
1727442581,486.7265625,55.3
1727442581,486.7265625,46.9
1727442581,486.7265625,54.5
1727442738,523.0859375,50.2
1727442738,523.0859375,50.0
1727442738,523.0859375,50.8
1727442738,523.0859375,39.4
1727442848,526.52734375,50.2
1727442848,526.52734375,38.2
1727442848,526.52734375,52.1
1727442848,526.52734375,40.6
1727442962,530.90625,50.2
1727442962,530.90625,39.4
1727442962,530.90625,51.3
1727442962,530.90625,55.6
1727443109,529.83203125,50.2
1727443109,529.83203125,40.0
1727443109,529.83203125,50.3
1727443109,529.83203125,55.6
1727443293,543.5859375,50.2
1727443293,543.5859375,45.9
1727443293,543.5859375,49.4
1727443293,543.5859375,58.7
1727443542,543.3359375,50.2
1727443542,543.3359375,37.1
1727443542,543.3359375,50.3
1727443542,543.3359375,57.8
1727443795,543.109375,50.2
1727443795,543.109375,38.2
1727443795,543.109375,50.9
1727443795,543.109375,53.7
1727444060,544.45703125,50.2
1727444060,544.45703125,53.2
1727444060,544.45703125,48.6
1727444060,544.45703125,56.5
1727444339,546.2734375,50.2
1727444339,546.2734375,41.2
1727444339,546.2734375,50.7
1727444339,546.2734375,55.6
1727444625,555.33984375,50.2
1727444625,555.33984375,35.3
1727444625,555.33984375,51.0
1727444625,555.33984375,54.2
1727444905,555.9296875,50.2
1727444905,555.9296875,55.3
1727444905,555.9296875,49.8
1727444905,555.9296875,48.7
1727445248,557.9296875,50.2
1727445248,557.9296875,38.2
1727445248,557.9296875,51.8
1727445248,557.9296875,37.5
1727445630,559.58203125,50.2
1727445630,559.58203125,55.3
1727445630,559.58203125,48.6
1727445630,559.58203125,55.6
1727446042,558.453125,50.2
1727446042,558.453125,54.3
1727446042,558.453125,49.8
1727446042,558.453125,45.9
1727446508,437.05859375,50.2
1727446508,437.05859375,52.1
1727446508,437.05859375,49.1
1727446508,437.05859375,56.5
1727446972,453.19140625,50.2
1727446972,453.19140625,55.6
1727446972,453.19140625,50.8
1727446972,453.19140625,40.6
1727447440,420.7109375,50.2
1727447440,420.7109375,40.0
1727447440,420.7109375,50.0
1727447440,420.7109375,56.8
1727447930,426.375,50.1
1727447930,426.375,39.4
1727447930,426.375,49.6
1727447930,426.375,56.5
1727448411,427.48828125,50.1
1727448411,427.48828125,54.2
1727448411,427.48828125,50.4
1727448411,427.48828125,42.4
1727448941,426.51953125,50.1
1727448941,426.51953125,55.3
1727448941,426.51953125,50.0
1727448941,426.51953125,42.4
1727449487,429.109375,50.1
1727449487,429.109375,53.2
1727449487,429.109375,49.1
1727449487,429.109375,57.8
1727450097,432.19921875,50.2
1727450097,432.19921875,39.4
1727450098,432.19921875,50.5
1727450098,432.19921875,56.5
1727450640,423.74609375,50.1
1727450640,423.74609375,52.0
1727450640,423.74609375,49.0
1727450640,423.74609375,53.2
1727451246,437.44140625,50.1
1727451246,437.44140625,38.9
1727451246,437.44140625,51.9
1727451246,437.44140625,38.7
1727451878,437.8359375,50.1
1727451878,437.8359375,54.3
1727451878,437.8359375,49.5
1727451878,437.8359375,55.6
1727452475,438.39453125,50.1
1727452475,438.39453125,35.3
1727452475,438.39453125,50.3
1727452475,438.39453125,57.8
1727453140,439.2578125,50.1
1727453140,439.2578125,55.3
1727453140,439.2578125,49.0
1727453140,439.2578125,57.8
1727453799,441.2109375,50.2
1727453799,441.2109375,39.4
1727453799,441.2109375,49.7
1727453799,441.2109375,56.5
1727454454,436.921875,50.2
1727454454,436.921875,48.1
1727454454,436.921875,50.9
1727454454,436.921875,40.6
1727455061,446.953125,50.2
1727455061,446.953125,35.1
1727455090,446.9609375,49.8
1727455090,446.9609375,55.6
</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>