| n_samples | confidence | feature_proportion | n_clusters | result |
|---|---|---|---|---|
| 100 | 0.1 | 0.034026 | 2 | 0.273213 |
| 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 |
|---|---|---|---|
| 287 | 211 | 9 | 507 |
trial_index,arm_name,trial_status,generation_method,result,n_samples,confidence,feature_proportion,n_clusters 0,0_0,COMPLETED,Sobol,0.303062011234717432195395758754,963,0.050000000000000002775557561563,0.090525108575820925627120061563,1 1,1_0,COMPLETED,Sobol,0.288688181517788300389781852573,410,0.250000000000000000000000000000,0.167912307754158995898308148753,1 2,2_0,COMPLETED,Sobol,0.288963542240334825272896068782,519,0.250000000000000000000000000000,0.104168479330837726593017578125,4 3,3_0,COMPLETED,Sobol,0.292047582332855992781617260334,619,0.250000000000000000000000000000,0.022104877047240734100341796875,4 4,4_0,COMPLETED,Sobol,0.298766383962991488587590538373,654,0.100000000000000005551115123126,0.195973979309201240539550781250,3 5,5_0,COMPLETED,Sobol,0.282795462055292379233151223161,112,0.025000000000000001387778780781,0.124943611398339274320967717813,4 6,6_0,COMPLETED,Sobol,0.279766494107280494496592382347,178,0.001000000000000000020816681712,0.058544055186212064223472140156,2 7,7_0,COMPLETED,Sobol,0.294415684546756262207622967253,992,0.010000000000000000208166817117,0.016774291545152666266238483672,3 8,8_0,COMPLETED,Sobol,0.282134596321180763922598089266,210,0.005000000000000000104083408559,0.161182905361056338922054465002,2 9,9_0,COMPLETED,Sobol,0.293589602379116687558280318626,880,0.025000000000000001387778780781,0.016868462413549424605552218281,4 10,10_0,COMPLETED,Sobol,0.299262033263575255581656620052,810,0.001000000000000000020816681712,0.194715055823326127493189119377,4 11,11_0,COMPLETED,Sobol,0.285438924991739173542271146289,296,0.250000000000000000000000000000,0.134585285000503068753019420001,1 12,12_0,COMPLETED,Sobol,0.295627271725961038306706996082,651,0.005000000000000000104083408559,0.027049892954528333838259968047,2 13,13_0,COMPLETED,Sobol,0.296288137460072653617260129977,860,0.250000000000000000000000000000,0.148602385446429258175626841876,2 14,14_0,COMPLETED,Sobol,0.294140323824209737324508751044,529,0.250000000000000000000000000000,0.112633097544312485438489090939,4 15,15_0,COMPLETED,Sobol,0.294030179535191060757881587051,998,0.005000000000000000104083408559,0.043478224985301495986167452656,3 16,16_0,COMPLETED,Sobol,0.290340345853067560710769612342,835,0.050000000000000002775557561563,0.098502206616103649139404296875,4 17,17_0,COMPLETED,Sobol,0.296783786760656420611326211656,325,0.001000000000000000020816681712,0.144428371451795101165771484375,1 18,18_0,COMPLETED,Sobol,0.292267870910893234892569125805,798,0.100000000000000005551115123126,0.066104659624397751893631891562,3 19,19_0,COMPLETED,Sobol,0.297334508205749581399857106589,653,0.250000000000000000000000000000,0.191614809818565851040617076251,3 20,20_0,COMPLETED,BoTorch,0.282575173477255248144501820207,130,0.005000000000000000104083408559,0.097383231306390108383830295224,3 21,21_0,COMPLETED,BoTorch,0.281473730587069037589742492855,127,0.010000000000000000208166817117,0.098496977213928568750667125187,2 22,22_0,COMPLETED,BoTorch,0.282354884899218006033549954736,200,0.025000000000000001387778780781,0.083681939960258869271036985538,3 23,23_0,COMPLETED,BoTorch,0.286265007159378748191613794916,100,0.001000000000000000020816681712,0.113182872816724233722140979808,3 24,24_0,COMPLETED,BoTorch,0.279325916951206121296991113923,114,0.025000000000000001387778780781,0.168159959532115599323276455834,3 25,25_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.005000000000000000104083408559,0.028910586306439074039120740167,3 26,26_0,COMPLETED,BoTorch,0.293314241656570051652863639902,215,0.001000000000000000020816681712,0.081342478114249897047649540127,3 27,27_0,COMPLETED,BoTorch,0.280537504130410786373772680236,100,0.010000000000000000208166817117,0.040441863507982622749636902881,2 28,28_0,COMPLETED,BoTorch,0.282685317766273813688826521684,120,0.050000000000000002775557561563,0.124401711174049953156917069919,2 29,29_0,COMPLETED,BoTorch,0.285053419980174083114832228603,100,0.001000000000000000020816681712,0.106279604584790132726368483418,2 30,30_0,COMPLETED,BoTorch,0.286265007159378748191613794916,235,0.010000000000000000208166817117,0.049882784523490091010966551721,2 31,31_0,COMPLETED,BoTorch,0.282024452032162087355970925273,104,0.005000000000000000104083408559,0.194003465969583455130731408644,3 32,32_0,COMPLETED,BoTorch,0.282740389910783096460988872423,100,0.001000000000000000020816681712,0.018468888734889537389660674194,2 33,33_0,COMPLETED,BoTorch,0.282905606344311055799778387154,140,0.001000000000000000020816681712,0.144121023614028093140504438452,3 34,34_0,FAILED,BoTorch,,199,0.001000000000000000020816681712,0.000000000000000000000000000000,3 35,35_0,COMPLETED,BoTorch,0.280757792708448028484724545706,189,0.005000000000000000104083408559,0.142858111407180632390634400508,3 36,36_0,COMPLETED,BoTorch,0.289238902962881350156010284991,250,0.005000000000000000104083408559,0.112690125891816705916426144540,3 37,37_0,COMPLETED,BoTorch,0.284888203546646123776042713871,166,0.050000000000000002775557561563,0.039532969656030508831534575620,3 38,38_0,COMPLETED,BoTorch,0.282299812754708723261387603998,136,0.100000000000000005551115123126,0.111431966640026580694922131443,3 39,39_0,COMPLETED,BoTorch,0.284392554246062356781976632192,100,0.010000000000000000208166817117,0.167280718658282007149651349209,2 40,40_0,COMPLETED,BoTorch,0.277453464037889618865051488683,180,0.025000000000000001387778780781,0.197395279598435141599210851382,3 41,41_0,COMPLETED,BoTorch,0.279711421962771211724430031609,100,0.025000000000000001387778780781,0.123391138167767241973216130191,3 42,42_0,COMPLETED,BoTorch,0.281253442009031795478790627385,100,0.050000000000000002775557561563,0.200000000000000011102230246252,3 43,43_0,COMPLETED,BoTorch,0.280647648419429462940399844229,164,0.025000000000000001387778780781,0.200000000000000011102230246252,2 44,44_0,COMPLETED,BoTorch,0.278389690494547870081021301303,100,0.025000000000000001387778780781,0.200000000000000011102230246252,4 45,45_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.016772286104730034506093971913,1 46,46_0,COMPLETED,BoTorch,0.280041854829827019379706598556,100,0.050000000000000002775557561563,0.088872069637165179711857376788,3 47,47_0,COMPLETED,BoTorch,0.283511399933913388338169170311,206,0.050000000000000002775557561563,0.189321426289413508037284827878,3 48,48_0,COMPLETED,BoTorch,0.279270844806696727502526300668,100,0.025000000000000001387778780781,0.200000000000000011102230246252,2 49,49_0,COMPLETED,BoTorch,0.279160700517678161958201599191,100,0.025000000000000001387778780781,0.075672156407027513225216353021,1 50,50_0,COMPLETED,BoTorch,0.277783896904945426520328055631,100,0.050000000000000002775557561563,0.131572762476871607528750018901,3 51,51_0,COMPLETED,BoTorch,0.279656349818261928952267680870,100,0.100000000000000005551115123126,0.200000000000000011102230246252,4 52,52_0,COMPLETED,BoTorch,0.280317215552373655285123277281,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1 53,53_0,COMPLETED,BoTorch,0.281198369864522512706628276646,155,0.005000000000000000104083408559,0.015263594869020234365275534572,1 54,54_0,COMPLETED,BoTorch,0.281473730587069037589742492855,157,0.025000000000000001387778780781,0.200000000000000011102230246252,4 55,55_0,COMPLETED,BoTorch,0.279380989095715404069153464661,161,0.025000000000000001387778780781,0.167245117074233701215391079131,3 56,56_0,COMPLETED,BoTorch,0.284282409957043680215349468199,160,0.010000000000000000208166817117,0.200000000000000011102230246252,3 57,57_0,COMPLETED,BoTorch,0.289293975107390632928172635729,203,0.100000000000000005551115123126,0.200000000000000011102230246252,4 58,58_0,FAILED,BoTorch,,131,0.025000000000000001387778780781,0.000000000000000000000000000000,1 59,59_0,FAILED,BoTorch,,147,0.001000000000000000020816681712,0.000000000000000000000000000000,1 60,60_0,COMPLETED,BoTorch,0.285053419980174083114832228603,150,0.005000000000000000104083408559,0.055951993657619131239400189770,1 61,61_0,COMPLETED,BoTorch,0.282795462055292379233151223161,174,0.010000000000000000208166817117,0.196432733221418964753013369773,3 62,62_0,COMPLETED,BoTorch,0.281749091309615562472856709064,186,0.025000000000000001387778780781,0.161379139920321101886457881847,3 63,63_0,COMPLETED,BoTorch,0.276847670448287286326660705527,158,0.010000000000000000208166817117,0.200000000000000011102230246252,4 64,64_0,COMPLETED,BoTorch,0.285714285714285698425385362498,154,0.025000000000000001387778780781,0.200000000000000011102230246252,3 65,65_0,COMPLETED,BoTorch,0.289899768696993076488865881402,254,0.100000000000000005551115123126,0.200000000000000011102230246252,3 66,66_0,FAILED,BoTorch,,143,0.010000000000000000208166817117,0.000000000000000000000000000000,1 67,67_0,COMPLETED,BoTorch,0.280096926974336413174171411811,100,0.100000000000000005551115123126,0.008081171800640817373673208124,1 68,68_0,COMPLETED,BoTorch,0.281363586298050472045417791378,100,0.250000000000000000000000000000,0.098285548983866308225820773714,1 69,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 70,70_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.200000000000000011102230246252,2 71,71_0,COMPLETED,BoTorch,0.279711421962771211724430031609,100,0.250000000000000000000000000000,0.200000000000000011102230246252,3 72,72_0,COMPLETED,BoTorch,0.281638947020596996928532007587,144,0.050000000000000002775557561563,0.200000000000000011102230246252,1 73,73_0,COMPLETED,BoTorch,0.279931710540808453835381897079,100,0.050000000000000002775557561563,0.146667877415456449075037426155,1 74,74_0,COMPLETED,BoTorch,0.278279546205529193514394137310,100,0.250000000000000000000000000000,0.087106881242826963984704491395,2 75,75_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.148886028762708788608648546870,2 76,76_0,COMPLETED,BoTorch,0.286485295737415990302565660386,172,0.250000000000000000000000000000,0.200000000000000011102230246252,1 77,77_0,COMPLETED,BoTorch,0.279325916951206121296991113923,100,0.050000000000000002775557561563,0.067082055787048136541450560344,1 78,78_0,COMPLETED,BoTorch,0.278720123361603677736297868250,137,0.100000000000000005551115123126,0.163905464382652882315838382965,1 79,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 80,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 81,81_0,COMPLETED,BoTorch,0.278279546205529193514394137310,100,0.250000000000000000000000000000,0.119808151095350090553637301127,2 82,82_0,COMPLETED,BoTorch,0.281363586298050472045417791378,100,0.250000000000000000000000000000,0.054810520901026262008404188464,1 83,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 84,84_0,COMPLETED,BoTorch,0.281583874876087714156369656848,157,0.050000000000000002775557561563,0.056729940933681249903841603555,1 85,85_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 86,86_0,FAILED,BoTorch,,151,0.250000000000000000000000000000,0.000000000000000000000000000000,1 87,87_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.129240514166755054992208329168,4 88,88_0,COMPLETED,BoTorch,0.278665051217094394964135517512,100,0.100000000000000005551115123126,0.154183314505685897799480699177,2 89,89_0,FAILED,BoTorch,,208,0.250000000000000000000000000000,0.000000000000000000000000000000,1 90,90_0,COMPLETED,BoTorch,0.280096926974336413174171411811,172,0.100000000000000005551115123126,0.019043071984322468975792119750,1 91,91_0,COMPLETED,BoTorch,0.277343319748871053320726787206,100,0.050000000000000002775557561563,0.084601905107450525722612155732,2 92,92_0,COMPLETED,BoTorch,0.277178103315343093981937272474,158,0.250000000000000000000000000000,0.071380868764979774065970730135,1 93,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 94,94_0,COMPLETED,BoTorch,0.279546205529243363407942979393,100,0.250000000000000000000000000000,0.126063321334941941254115249649,3 95,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 96,96_0,COMPLETED,BoTorch,0.282244740610199329466922790743,162,0.100000000000000005551115123126,0.082406085220461888773968439637,1 97,97_0,COMPLETED,BoTorch,0.277783896904945426520328055631,100,0.050000000000000002775557561563,0.052815648415997534792509782164,2 98,98_0,COMPLETED,BoTorch,0.279711421962771211724430031609,104,0.050000000000000002775557561563,0.040917674834797496119520587854,2 99,99_0,COMPLETED,BoTorch,0.283015750633329621344103088632,175,0.100000000000000005551115123126,0.059508679931502175830537737511,1 100,100_0,COMPLETED,BoTorch,0.280317215552373655285123277281,100,0.250000000000000000000000000000,0.132175142840226106377699011318,1 101,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 102,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 103,103_0,FAILED,BoTorch,,209,0.250000000000000000000000000000,0.000000000000000000000000000000,1 104,104_0,COMPLETED,BoTorch,0.279160700517678161958201599191,100,0.100000000000000005551115123126,0.147701455916858542805059073544,3 105,105_0,COMPLETED,BoTorch,0.281694019165106279700694358326,100,0.250000000000000000000000000000,0.015408072592119799459897677707,4 106,106_0,FAILED,BoTorch,,133,0.250000000000000000000000000000,0.000000000000000000000000000000,1 107,107_0,COMPLETED,BoTorch,0.280427359841392220829447978758,100,0.010000000000000000208166817117,0.200000000000000011102230246252,4 108,108_0,COMPLETED,BoTorch,0.274038991078312643701053730183,100,0.100000000000000005551115123126,0.107651776383508049339532419708,2 109,85_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3 110,110_0,FAILED,BoTorch,,246,0.250000000000000000000000000000,0.000000000000000000000000000000,1 111,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 112,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 113,113_0,FAILED,BoTorch,,181,0.100000000000000005551115123126,0.000000000000000000000000000000,1 114,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 115,115_0,COMPLETED,BoTorch,0.280372287696882938057285628020,100,0.250000000000000000000000000000,0.058837232069032657788554274703,3 116,116_0,COMPLETED,BoTorch,0.280372287696882938057285628020,100,0.250000000000000000000000000000,0.019451621923215790810335334982,3 117,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 118,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 119,119_0,COMPLETED,BoTorch,0.278499834783566435625346002780,100,0.100000000000000005551115123126,0.071280474695899459502967943081,2 120,120_0,COMPLETED,BoTorch,0.277453464037889618865051488683,100,0.250000000000000000000000000000,0.027479106722264542983502622064,2 121,121_0,COMPLETED,BoTorch,0.280096926974336413174171411811,100,0.100000000000000005551115123126,0.107526642964011473480034908334,1 122,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 123,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 124,124_0,COMPLETED,BoTorch,0.280207071263354978718496113288,127,0.250000000000000000000000000000,0.080205005851055805043969826329,2 125,125_0,FAILED,BoTorch,,229,0.250000000000000000000000000000,0.000000000000000000000000000000,1 126,126_0,FAILED,BoTorch,,311,0.250000000000000000000000000000,0.000000000000000000000000000000,1 127,127_0,COMPLETED,BoTorch,0.280096926974336413174171411811,100,0.100000000000000005551115123126,0.063816599022502473737006312149,1 128,128_0,FAILED,BoTorch,,203,0.250000000000000000000000000000,0.000000000000000000000000000000,3 129,129_0,COMPLETED,BoTorch,0.273212908910672958029408619041,100,0.100000000000000005551115123126,0.034025703431758771988491218963,2 130,130_0,FAILED,BoTorch,,376,0.250000000000000000000000000000,0.000000000000000000000000000000,3 131,131_0,FAILED,BoTorch,,252,0.250000000000000000000000000000,0.000000000000000000000000000000,3 132,132_0,COMPLETED,BoTorch,0.278499834783566435625346002780,172,0.001000000000000000020816681712,0.200000000000000011102230246252,4 133,133_0,COMPLETED,BoTorch,0.276792598303778003554498354788,100,0.250000000000000000000000000000,0.051157768335299030892926452907,2 134,134_0,FAILED,BoTorch,,301,0.100000000000000005551115123126,0.000000000000000000000000000000,1 135,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 136,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 137,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 138,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 139,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 140,140_0,FAILED,BoTorch,,166,0.250000000000000000000000000000,0.000000000000000000000000000000,1 141,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 142,142_0,FAILED,BoTorch,,143,0.250000000000000000000000000000,0.000000000000000000000000000000,2 143,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 144,144_0,COMPLETED,BoTorch,0.286705584315453232413517525856,142,0.001000000000000000020816681712,0.200000000000000011102230246252,4 145,145_0,COMPLETED,BoTorch,0.279325916951206121296991113923,100,0.050000000000000002775557561563,0.031364224861028959512321279135,1 146,146_0,COMPLETED,BoTorch,0.277067959026324528437612570997,100,0.100000000000000005551115123126,0.050877720506665717603578258377,2 147,147_0,FAILED,BoTorch,,230,0.250000000000000000000000000000,0.000000000000000000000000000000,1 148,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 149,149_0,FAILED,BoTorch,,288,0.250000000000000000000000000000,0.000000000000000000000000000000,2 150,150_0,FAILED,BoTorch,,235,0.250000000000000000000000000000,0.000000000000000000000000000000,2 151,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 152,152_0,FAILED,BoTorch,,372,0.250000000000000000000000000000,0.000000000000000000000000000000,1 153,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 154,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 155,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 156,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 157,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 158,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 159,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 160,160_0,FAILED,BoTorch,,258,0.250000000000000000000000000000,0.000000000000000000000000000000,1 161,142_0,FAILED,BoTorch,,143,0.250000000000000000000000000000,0.000000000000000000000000000000,2 162,162_0,FAILED,BoTorch,,246,0.250000000000000000000000000000,0.000000000000000000000000000000,3 163,163_0,FAILED,BoTorch,,189,0.250000000000000000000000000000,0.000000000000000000000000000000,1 164,164_0,FAILED,BoTorch,,252,0.250000000000000000000000000000,0.000000000000000000000000000000,2 165,165_0,COMPLETED,BoTorch,0.282024452032162087355970925273,130,0.100000000000000005551115123126,0.014940955056160890729310963820,1 166,166_0,COMPLETED,BoTorch,0.282740389910783096460988872423,241,0.250000000000000000000000000000,0.004611359294566957402194962157,2 167,167_0,FAILED,BoTorch,,138,0.250000000000000000000000000000,0.000000000000000000000000000000,1 168,168_0,COMPLETED,BoTorch,0.285493997136248456314433497027,100,0.050000000000000002775557561563,0.033234922768231221767987193516,4 169,169_0,FAILED,BoTorch,,420,0.250000000000000000000000000000,0.000000000000000000000000000000,2 170,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 171,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 172,172_0,FAILED,BoTorch,,342,0.250000000000000000000000000000,0.000000000000000000000000000000,1 173,173_0,COMPLETED,BoTorch,0.288688181517788300389781852573,379,0.250000000000000000000000000000,0.055260351827480319597540869836,2 174,174_0,FAILED,BoTorch,,423,0.250000000000000000000000000000,0.000000000000000000000000000000,1 175,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 176,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 177,177_0,COMPLETED,BoTorch,0.279546205529243363407942979393,100,0.100000000000000005551115123126,0.091784815734130054121209241202,2 178,178_0,FAILED,BoTorch,,141,0.250000000000000000000000000000,0.000000000000000000000000000000,2 179,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 180,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 181,181_0,COMPLETED,BoTorch,0.283456327789404105566006819572,371,0.250000000000000000000000000000,0.035268170977396595677788582179,2 182,182_0,COMPLETED,BoTorch,0.288798325806806865934106554050,240,0.250000000000000000000000000000,0.006550836244915510600539398922,2 183,183_0,FAILED,BoTorch,,128,0.100000000000000005551115123126,0.000000000000000000000000000000,2 184,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 185,185_0,COMPLETED,BoTorch,0.283346183500385540021682118095,371,0.250000000000000000000000000000,0.012474694277423533511628406245,2 186,186_0,COMPLETED,BoTorch,0.282575173477255248144501820207,138,0.010000000000000000208166817117,0.105835394462641854684825659660,1 187,187_0,FAILED,BoTorch,,312,0.100000000000000005551115123126,0.000000000000000000000000000000,2 188,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 189,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 190,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 191,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 192,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 193,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 194,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 195,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 196,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 197,197_0,COMPLETED,BoTorch,0.284282409957043680215349468199,121,0.100000000000000005551115123126,0.064490437360056454552115212664,2 198,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 199,199_0,COMPLETED,BoTorch,0.280482431985901503601610329497,100,0.025000000000000001387778780781,0.200000000000000011102230246252,1 200,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 201,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 202,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 203,203_0,FAILED,BoTorch,,475,0.250000000000000000000000000000,0.000000000000000000000000000000,1 204,204_0,COMPLETED,BoTorch,0.275030289679480066666883431026,100,0.050000000000000002775557561563,0.200000000000000011102230246252,4 205,205_0,COMPLETED,BoTorch,0.280427359841392220829447978758,100,0.250000000000000000000000000000,0.179974964553872268657741528841,4 206,206_0,FAILED,BoTorch,,376,0.250000000000000000000000000000,0.000000000000000000000000000000,4 207,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 208,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 209,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 210,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 211,211_0,FAILED,BoTorch,,144,0.250000000000000000000000000000,0.000000000000000000000000000000,1 212,212_0,FAILED,BoTorch,,100,0.005000000000000000104083408559,0.000000000000000000000000000000,1 213,213_0,FAILED,BoTorch,,160,0.010000000000000000208166817117,0.000000000000000000000000000000,1 214,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 215,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 216,216_0,COMPLETED,BoTorch,0.280757792708448028484724545706,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4 217,217_0,FAILED,BoTorch,,145,0.001000000000000000020816681712,0.000000000000000000000000000000,1 218,218_0,COMPLETED,BoTorch,0.280427359841392220829447978758,100,0.050000000000000002775557561563,0.124746793075073622580895005285,2 219,219_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.016780906584811027676407135800,1 220,220_0,FAILED,BoTorch,,328,0.250000000000000000000000000000,0.000000000000000000000000000000,1 221,221_0,FAILED,BoTorch,,438,0.250000000000000000000000000000,0.000000000000000000000000000000,1 222,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 223,223_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.016828327482921595636966571874,1 224,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 225,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 226,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 227,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 228,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 229,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 230,230_0,COMPLETED,BoTorch,0.276792598303778003554498354788,100,0.250000000000000000000000000000,0.040516131459451071306077096779,2 231,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 232,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 233,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 234,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 235,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 236,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 237,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 238,238_0,FAILED,BoTorch,,447,0.250000000000000000000000000000,0.000000000000000000000000000000,1 239,239_0,FAILED,BoTorch,,494,0.250000000000000000000000000000,0.000000000000000000000000000000,1 240,240_0,COMPLETED,BoTorch,0.295847560303998280417658861552,584,0.025000000000000001387778780781,0.012052556872367859233663445195,4 241,241_0,COMPLETED,BoTorch,0.280372287696882938057285628020,100,0.100000000000000005551115123126,0.200000000000000011102230246252,3 242,242_0,FAILED,BoTorch,,385,0.250000000000000000000000000000,0.000000000000000000000000000000,4 243,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 244,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 245,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 246,246_0,FAILED,BoTorch,,525,0.100000000000000005551115123126,0.000000000000000000000000000000,4 247,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 248,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 249,249_0,COMPLETED,BoTorch,0.284943275691155406548205064610,270,0.100000000000000005551115123126,0.062503175008246217303486957917,4 250,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 251,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 252,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 253,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 254,254_0,COMPLETED,BoTorch,0.289349047251900026722637448984,246,0.250000000000000000000000000000,0.037427697376058485789762642071,4 255,255_0,FAILED,BoTorch,,461,0.250000000000000000000000000000,0.000000000000000000000000000000,3 256,256_0,COMPLETED,BoTorch,0.275525938980063833660949512705,100,0.100000000000000005551115123126,0.097796768657165744631498682793,3 257,257_0,COMPLETED,BoTorch,0.287641810772111483629487338476,300,0.250000000000000000000000000000,0.017493468304008136238181236877,3 258,258_0,FAILED,BoTorch,,434,0.250000000000000000000000000000,0.000000000000000000000000000000,1 259,259_0,FAILED,BoTorch,,434,0.250000000000000000000000000000,0.000000000000000000000000000000,4 260,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 261,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 262,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 263,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 264,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 265,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 266,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 267,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 268,268_0,RUNNING,BoTorch,,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1 269,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 270,270_0,FAILED,BoTorch,,527,0.250000000000000000000000000000,0.000000000000000000000000000000,1 271,271_0,COMPLETED,BoTorch,0.284172265668025114671024766722,100,0.001000000000000000020816681712,0.148762731647136131618580634495,1 272,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 273,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 274,274_0,FAILED,BoTorch,,523,0.250000000000000000000000000000,0.000000000000000000000000000000,1 275,275_0,COMPLETED,BoTorch,0.293093953078532920564214236947,392,0.250000000000000000000000000000,0.008157396350348457628176568335,1 276,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 277,277_0,COMPLETED,BoTorch,0.278720123361603677736297868250,100,0.005000000000000000104083408559,0.162367116239565456581317448581,1 278,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 279,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 280,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 281,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 282,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 283,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 284,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 285,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 286,286_0,RUNNING,BoTorch,,418,0.001000000000000000020816681712,0.200000000000000011102230246252,4 287,287_0,RUNNING,BoTorch,,210,0.005000000000000000104083408559,0.195229531824729851674504743642,4 288,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 289,289_0,FAILED,BoTorch,,399,0.250000000000000000000000000000,0.000000000000000000000000000000,4 290,290_0,COMPLETED,BoTorch,0.288522965084260341050992337841,391,0.250000000000000000000000000000,0.200000000000000011102230246252,4 291,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 292,292_0,COMPLETED,BoTorch,0.286430223592906707530403309647,398,0.250000000000000000000000000000,0.124213997212494514643665866060,4 293,293_0,COMPLETED,BoTorch,0.290835995153651327704835694021,265,0.005000000000000000104083408559,0.195706359202674429198509642447,4 294,294_0,COMPLETED,BoTorch,0.295186694569886554084803265141,379,0.005000000000000000104083408559,0.138826016297458193493596922963,4 295,295_0,COMPLETED,BoTorch,0.293204097367551486108538938424,398,0.250000000000000000000000000000,0.116092535794456830355869669802,4 296,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 297,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 298,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 299,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 300,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 301,301_0,COMPLETED,BoTorch,0.285549069280757739086595847766,164,0.001000000000000000020816681712,0.200000000000000011102230246252,1 302,302_0,COMPLETED,BoTorch,0.279215772662187444730363949930,116,0.001000000000000000020816681712,0.200000000000000011102230246252,1 303,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 304,304_0,COMPLETED,BoTorch,0.282354884899218006033549954736,126,0.001000000000000000020816681712,0.200000000000000011102230246252,1 305,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 306,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 307,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 308,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 309,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 310,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 311,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 312,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 313,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 314,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 315,315_0,COMPLETED,BoTorch,0.279931710540808453835381897079,100,0.050000000000000002775557561563,0.200000000000000011102230246252,1 316,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 317,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 318,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 319,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 320,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 321,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 322,322_0,COMPLETED,BoTorch,0.276792598303778003554498354788,100,0.250000000000000000000000000000,0.065092367586101540499754491975,2 323,323_0,COMPLETED,BoTorch,0.276902742592796569098823056265,100,0.250000000000000000000000000000,0.086519324341300926639597435042,3 324,324_0,COMPLETED,BoTorch,0.276737526159268609760033541534,100,0.025000000000000001387778780781,0.148885438950722565065731828327,4 325,325_0,COMPLETED,BoTorch,0.277949113338473385859117570362,100,0.010000000000000000208166817117,0.114237107451868713514642195150,1 326,326_0,COMPLETED,BoTorch,0.278554906928075829419810816034,100,0.050000000000000002775557561563,0.162751877759727187067184672742,4 327,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 328,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 329,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 330,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 331,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 332,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 333,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 334,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 335,335_0,COMPLETED,BoTorch,0.280482431985901503601610329497,100,0.025000000000000001387778780781,0.166185298806437647956002479077,1 336,336_0,COMPLETED,BoTorch,0.279160700517678161958201599191,100,0.025000000000000001387778780781,0.118538379812509614419369086136,1 337,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 338,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 339,339_0,COMPLETED,BoTorch,0.279050556228659596413876897714,100,0.250000000000000000000000000000,0.058328797540965919565802977331,2 340,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 341,341_0,COMPLETED,BoTorch,0.281804163454124956267321522319,100,0.010000000000000000208166817117,0.200000000000000011102230246252,1 342,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 343,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 344,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 345,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 346,346_0,COMPLETED,BoTorch,0.278279546205529193514394137310,100,0.250000000000000000000000000000,0.067999752960812329116713215171,2 347,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 348,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 349,349_0,COMPLETED,BoTorch,0.279931710540808453835381897079,100,0.050000000000000002775557561563,0.157042400667316200957301930430,1 350,350_0,COMPLETED,BoTorch,0.282024452032162087355970925273,104,0.050000000000000002775557561563,0.040941970392548272805743891922,2 351,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 352,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 353,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 354,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 355,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 356,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 357,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 358,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 359,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 360,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 361,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 362,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 363,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 364,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 365,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 366,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 367,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 368,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 369,369_0,COMPLETED,BoTorch,0.286870800748981191752307040588,156,0.010000000000000000208166817117,0.147898331283112555167846835502,1 370,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1 371,371_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2 372,372_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4 373,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 374,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 375,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 376,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 377,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 378,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 379,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 380,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 381,381_0,COMPLETED,BoTorch,0.279436061240224686841315815400,100,0.250000000000000000000000000000,0.077617748101248329462009678537,4 382,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 383,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 384,384_0,COMPLETED,BoTorch,0.277288247604361659526261973951,100,0.100000000000000005551115123126,0.186089965302586257678285619477,1 385,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 386,386_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.062498399400783159751693318640,1 387,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 388,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 389,389_0,COMPLETED,BoTorch,0.278665051217094394964135517512,100,0.100000000000000005551115123126,0.144907860933490367782994212575,3 390,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 391,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 392,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 393,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 394,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 395,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 396,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 397,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 398,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 399,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 400,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 401,401_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.173224199560479408255275757256,4 402,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 403,403_0,COMPLETED,BoTorch,0.281363586298050472045417791378,100,0.250000000000000000000000000000,0.045459298479500663547892003180,1 404,404_0,COMPLETED,BoTorch,0.285273708558211214203481631557,171,0.010000000000000000208166817117,0.100038032419979577847257701251,2 405,405_0,COMPLETED,BoTorch,0.279491133384733969613478166139,100,0.050000000000000002775557561563,0.163307962482790558489398335951,3 406,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2 407,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 408,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1 409,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 410,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 411,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 412,268_0,COMPLETED,BoTorch,0.284172265668025114671024766722,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1 413,413_0,COMPLETED,BoTorch,0.277288247604361659526261973951,100,0.100000000000000005551115123126,0.158134305030585536888665387778,1 414,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 415,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 416,416_0,COMPLETED,BoTorch,0.284172265668025114671024766722,100,0.001000000000000000020816681712,0.151038369651452092368160151636,1 417,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1 418,418_0,COMPLETED,BoTorch,0.277453464037889618865051488683,100,0.250000000000000000000000000000,0.089778478806331427075626550049,2 419,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 420,420_0,COMPLETED,BoTorch,0.279656349818261928952267680870,100,0.050000000000000002775557561563,0.147690880372298688483212458777,3 421,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2 422,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1 423,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 424,424_0,FAILED,BoTorch,,133,0.250000000000000000000000000000,0.000000000000000000000000000000,2 425,425_0,COMPLETED,BoTorch,0.281528802731578320361904843594,172,0.010000000000000000208166817117,0.200000000000000011102230246252,4 426,426_0,FAILED,BoTorch,,153,0.250000000000000000000000000000,0.000000000000000000000000000000,1 427,427_0,COMPLETED,BoTorch,0.280537504130410786373772680236,129,0.250000000000000000000000000000,0.200000000000000011102230246252,1 428,428_0,COMPLETED,BoTorch,0.284062121379006549126700065244,146,0.250000000000000000000000000000,0.072270933389340472063899767363,1 429,429_0,FAILED,BoTorch,,191,0.005000000000000000104083408559,0.000000000000000000000000000000,1 430,430_0,COMPLETED,BoTorch,0.279986782685317736607544247818,169,0.005000000000000000104083408559,0.144232418191124950324066844587,4 431,431_0,COMPLETED,BoTorch,0.279105628373168879186039248452,119,0.050000000000000002775557561563,0.200000000000000011102230246252,1 432,432_0,COMPLETED,BoTorch,0.286540367881925273074728011125,205,0.025000000000000001387778780781,0.118217182327579162226527387247,1 433,433_0,COMPLETED,BoTorch,0.283786760656459913221283386520,169,0.001000000000000000020816681712,0.086063488076073779708963229496,4 434,434_0,COMPLETED,BoTorch,0.279325916951206121296991113923,195,0.005000000000000000104083408559,0.200000000000000011102230246252,1 435,435_0,COMPLETED,BoTorch,0.280207071263354978718496113288,199,0.005000000000000000104083408559,0.098712345832683456858802628631,1 436,436_0,COMPLETED,BoTorch,0.279270844806696727502526300668,115,0.005000000000000000104083408559,0.019650920318037799761068384896,1 437,437_0,COMPLETED,BoTorch,0.283676616367441347676958685042,144,0.250000000000000000000000000000,0.089353878213655715812002711118,1 438,438_0,COMPLETED,BoTorch,0.278499834783566435625346002780,114,0.005000000000000000104083408559,0.090201890089163697106222628008,1 439,439_0,COMPLETED,BoTorch,0.284007049234497155332235251990,199,0.010000000000000000208166817117,0.053078853834053155158478887188,1 440,440_0,COMPLETED,BoTorch,0.279050556228659596413876897714,118,0.050000000000000002775557561563,0.200000000000000011102230246252,1 441,441_0,FAILED,BoTorch,,205,0.100000000000000005551115123126,0.000000000000000000000000000000,1 442,442_0,COMPLETED,BoTorch,0.277067959026324528437612570997,110,0.010000000000000000208166817117,0.069722779905971490888028085919,1 443,443_0,COMPLETED,BoTorch,0.281473730587069037589742492855,106,0.050000000000000002775557561563,0.200000000000000011102230246252,4 444,444_0,FAILED,BoTorch,,342,0.250000000000000000000000000000,0.000000000000000000000000000000,2 445,445_0,COMPLETED,BoTorch,0.280482431985901503601610329497,190,0.001000000000000000020816681712,0.200000000000000011102230246252,2 446,446_0,COMPLETED,BoTorch,0.285714285714285698425385362498,200,0.001000000000000000020816681712,0.200000000000000011102230246252,1 447,447_0,COMPLETED,BoTorch,0.280427359841392220829447978758,131,0.250000000000000000000000000000,0.200000000000000011102230246252,4 448,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1 449,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1 450,450_0,COMPLETED,BoTorch,0.279380989095715404069153464661,185,0.001000000000000000020816681712,0.200000000000000011102230246252,3 451,451_0,COMPLETED,BoTorch,0.282740389910783096460988872423,126,0.250000000000000000000000000000,0.200000000000000011102230246252,3 452,452_0,FAILED,BoTorch,,350,0.250000000000000000000000000000,0.000000000000000000000000000000,4 453,453_0,COMPLETED,BoTorch,0.281253442009031795478790627385,117,0.100000000000000005551115123126,0.200000000000000011102230246252,4 454,454_0,FAILED,BoTorch,,110,0.010000000000000000208166817117,0.000000000000000000000000000000,1 455,455_0,FAILED,BoTorch,,340,0.250000000000000000000000000000,0.000000000000000000000000000000,1 456,456_0,COMPLETED,BoTorch,0.291882365899328144465130208118,345,0.250000000000000000000000000000,0.154313144062262819211639452988,4 457,457_0,COMPLETED,BoTorch,0.277728824760436143748165704892,142,0.250000000000000000000000000000,0.200000000000000011102230246252,4 458,458_0,COMPLETED,BoTorch,0.285604141425267132881060661020,154,0.250000000000000000000000000000,0.139474740217836762345982037914,4 459,459_0,COMPLETED,BoTorch,0.290285273708558166916304799088,465,0.250000000000000000000000000000,0.200000000000000011102230246252,4 460,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2 461,461_0,COMPLETED,BoTorch,0.280647648419429462940399844229,114,0.010000000000000000208166817117,0.108764820140075157972603392409,1 462,462_0,COMPLETED,BoTorch,0.283731688511950630449121035781,163,0.100000000000000005551115123126,0.200000000000000011102230246252,4 463,463_0,COMPLETED,BoTorch,0.277067959026324528437612570997,110,0.010000000000000000208166817117,0.044969895511009175259609094155,1 464,464_0,FAILED,BoTorch,,109,0.010000000000000000208166817117,0.000000000000000000000000000000,1 465,465_0,FAILED,BoTorch,,180,0.001000000000000000020816681712,0.000000000000000000000000000000,1 466,466_0,COMPLETED,BoTorch,0.292267870910893234892569125805,475,0.250000000000000000000000000000,0.200000000000000011102230246252,1 467,467_0,COMPLETED,BoTorch,0.294911333847340029201689048932,468,0.250000000000000000000000000000,0.200000000000000011102230246252,3 468,468_0,COMPLETED,BoTorch,0.299206961119065972809494269313,470,0.250000000000000000000000000000,0.021224734671334202079640007810,4 469,469_0,FAILED,BoTorch,,341,0.250000000000000000000000000000,0.000000000000000000000000000000,1 470,470_0,COMPLETED,BoTorch,0.286870800748981191752307040588,190,0.010000000000000000208166817117,0.200000000000000011102230246252,2 471,471_0,COMPLETED,BoTorch,0.294580900980284221546412481985,346,0.250000000000000000000000000000,0.098074682697481499471514609922,1 472,472_0,COMPLETED,BoTorch,0.294635973124793504318574832723,466,0.250000000000000000000000000000,0.148270767925898189210087707579,4 473,473_0,FAILED,BoTorch,,271,0.250000000000000000000000000000,0.000000000000000000000000000000,1 474,474_0,FAILED,BoTorch,,110,0.005000000000000000104083408559,0.000000000000000000000000000000,1 475,475_0,COMPLETED,BoTorch,0.277067959026324528437612570997,110,0.010000000000000000208166817117,0.000140524550070662655873107716,1 476,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1 477,477_0,COMPLETED,BoTorch,0.276186804714175559993805109116,186,0.001000000000000000020816681712,0.078044541724856641984331417916,1 478,478_0,FAILED,BoTorch,,182,0.001000000000000000020816681712,0.000000000000000000000000000000,1 479,479_0,FAILED,BoTorch,,113,0.001000000000000000020816681712,0.000000000000000000000000000000,1 480,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1 481,481_0,COMPLETED,BoTorch,0.282354884899218006033549954736,106,0.010000000000000000208166817117,0.007445980293814238176741682196,1 482,482_0,FAILED,BoTorch,,164,0.250000000000000000000000000000,0.000000000000000000000000000000,1 483,483_0,COMPLETED,BoTorch,0.280041854829827019379706598556,178,0.001000000000000000020816681712,0.058670282113353955377732518173,2 484,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2 485,485_0,FAILED,BoTorch,,738,0.250000000000000000000000000000,0.000000000000000000000000000000,1 486,486_0,FAILED,BoTorch,,104,0.010000000000000000208166817117,0.000000000000000000000000000000,1 487,487_0,COMPLETED,BoTorch,0.278334618350038587308858950564,184,0.001000000000000000020816681712,0.200000000000000011102230246252,4 488,488_0,FAILED,BoTorch,,267,0.025000000000000001387778780781,0.000000000000000000000000000000,1 489,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1 490,490_0,FAILED,BoTorch,,269,0.100000000000000005551115123126,0.000000000000000000000000000000,1 491,478_0,FAILED,BoTorch,,182,0.001000000000000000020816681712,0.000000000000000000000000000000,1 492,492_0,FAILED,BoTorch,,266,0.005000000000000000104083408559,0.000000000000000000000000000000,1 493,493_0,COMPLETED,BoTorch,0.286870800748981191752307040588,182,0.001000000000000000020816681712,0.009659092574850258738905495193,1 494,494_0,FAILED,BoTorch,,117,0.001000000000000000020816681712,0.000000000000000000000000000000,1 495,465_0,FAILED,BoTorch,,180,0.001000000000000000020816681712,0.000000000000000000000000000000,1 496,496_0,FAILED,BoTorch,,284,0.010000000000000000208166817117,0.000000000000000000000000000000,1 497,497_0,COMPLETED,BoTorch,0.278940411939640919847249733721,183,0.001000000000000000020816681712,0.024836256220531940930307257531,1 498,498_0,FAILED,BoTorch,,272,0.050000000000000002775557561563,0.000000000000000000000000000000,1 499,499_0,COMPLETED,BoTorch,0.289459191540918592266962150461,224,0.010000000000000000208166817117,0.147249993213455082630147785494,4 500,500_0,COMPLETED,BoTorch,0.282630245621764530916664170945,270,0.050000000000000002775557561563,0.000008729249569113619228221193,1 501,501_0,RUNNING,BoTorch,,161,0.250000000000000000000000000000,0.000000000000000000000000000000,1 502,502_0,RUNNING,BoTorch,,178,0.005000000000000000104083408559,0.000000000000000000000000000000,1 503,503_0,RUNNING,BoTorch,,271,0.050000000000000002775557561563,0.000000000000000000000000000000,1 504,504_0,RUNNING,BoTorch,,254,0.025000000000000001387778780781,0.029757720669419746722939024153,1 505,478_0,RUNNING,BoTorch,,182,0.001000000000000000020816681712,0.000000000000000000000000000000,1 506,506_0,RUNNING,BoTorch,,118,0.001000000000000000020816681712,0.000000000000000000000000000000,1
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 1727277352,1727277431,79,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 963 confidence 0.05 feature_proportion 0.09052510857582093 n_clusters 1,963,0.05,0.09052510857582093,1,0.30306201123471743,0,None,i7182,9,0.0018530156858425726 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 1727277371,1727277431,60,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 519 confidence 0.25 feature_proportion 0.10416847933083773 n_clusters 4,519,0.25,0.10416847933083773,4,0.2889635422403348,0,None,i7182,9,0.0013818101713244345 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 1727277371,1727277431,60,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 619 confidence 0.25 feature_proportion 0.022104877047240734 n_clusters 4,619,0.25,0.022104877047240734,4,0.292047582332856,0,None,i7182,9,0.0015184176986137562 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 1727277371,1727277431,60,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 654 confidence 0.1 feature_proportion 0.19597397930920124 n_clusters 3,654,0.1,0.19597397930920124,3,0.2987663839629915,0,None,i7182,9,0.00137680361273268 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 1727277370,1727277431,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 410 confidence 0.25 feature_proportion 0.167912307754159 n_clusters 1,410,0.25,0.167912307754159,1,0.2886881815177883,0,None,i7182,9,0.001092264199434592 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 1727277371,1727277431,60,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 178 confidence 0.001 feature_proportion 0.058544055186212064 n_clusters 2,178,0.001,0.058544055186212064,2,0.2797664941072805,0,None,i7182,9,0.0005512777293810448 1727277371,1727277432,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 112 confidence 0.025 feature_proportion 0.12494361139833927 n_clusters 4,112,0.025,0.12494361139833927,4,0.2827954620552924,0,None,i7182,11,0.0003391284688204707 1727277390,1727277432,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 860 confidence 0.25 feature_proportion 0.14860238544642926 n_clusters 2,860,0.25,0.14860238544642926,2,0.29628813746007265,0,None,i7178,11,0.0019137570216984257 1727277390,1727277432,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 529 confidence 0.25 feature_proportion 0.11263309754431249 n_clusters 4,529,0.25,0.11263309754431249,4,0.29414032382420974,0,None,i7178,11,0.001224938002116104 1727277390,1727277432,42,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 296 confidence 0.25 feature_proportion 0.13458528500050307 n_clusters 1,296,0.25,0.13458528500050307,1,0.2854389249917392,0,None,i7178,11,0.0008187392150383665 1727277390,1727277433,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 210 confidence 0.005 feature_proportion 0.16118290536105634 n_clusters 2,210,0.005,0.16118290536105634,2,0.28213459632118076,0,None,i7178,11,0.0006142163881743901 1727277371,1727277433,62,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 992 confidence 0.01 feature_proportion 0.016774291545152666 n_clusters 3,992,0.01,0.016774291545152666,3,0.29441568454675626,0,None,i7182,12,0.0025092245842053065 1727277390,1727277440,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 880 confidence 0.025 feature_proportion 0.016868462413549425 n_clusters 4,880,0.025,0.016868462413549425,4,0.2935896023791167,0,None,i7178,18,0.0024102162067602636 1727277390,1727277440,50,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 998 confidence 0.005 feature_proportion 0.043478224985301496 n_clusters 3,998,0.005,0.043478224985301496,3,0.29403017953519106,0,None,i7178,19,0.002895221311346436 1727277390,1727277442,52,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 651 confidence 0.005 feature_proportion 0.027049892954528334 n_clusters 2,651,0.005,0.027049892954528334,2,0.29562727172596104,0,None,i7178,21,0.0020492634825305333 1727277390,1727277451,61,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 810 confidence 0.001 feature_proportion 0.19471505582332613 n_clusters 4,810,0.001,0.19471505582332613,4,0.29926203326357526,0,None,i7178,30,0.0022063277894041194 1727277449,1727277662,213,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 653 confidence 0.25 feature_proportion 0.19161480981856585 n_clusters 3,653,0.25,0.19161480981856585,3,0.2973345082057496,0,None,i7176,166,0.001431875757241984 1727277449,1727277683,234,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 798 confidence 0.1 feature_proportion 0.06610465962439775 n_clusters 3,798,0.1,0.06610465962439775,3,0.29226787091089323,0,None,i7176,187,0.0020140669991975204 1727277449,1727277718,269,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 835 confidence 0.05 feature_proportion 0.09850220661610365 n_clusters 4,835,0.05,0.09850220661610365,4,0.29034034585306756,0,None,i7176,222,0.002327522738998611 1727277449,1727277812,363,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 325 confidence 0.001 feature_proportion 0.1444283714517951 n_clusters 1,325,0.001,0.1444283714517951,1,0.2967837867606564,0,None,i7176,316,0.0009941971350890724 1727277911,1727277924,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 114 confidence 0.025 feature_proportion 0.1681599595321156 n_clusters 3,114,0.025,0.1681599595321156,3,0.2793259169512061,0,None,i7182,9,0.0003504272125146357 1727277911,1727277924,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 200 confidence 0.025 feature_proportion 0.08368193996025887 n_clusters 3,200,0.025,0.08368193996025887,3,0.282354884899218,0,None,i7182,10,0.0005932771931229905 1727277911,1727277924,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 127 confidence 0.01 feature_proportion 0.09849697721392857 n_clusters 2,127,0.01,0.09849697721392857,2,0.28147373058706904,0,None,i7182,10,0.0003803543793448553 1727277911,1727277925,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.028910586306439074 n_clusters 3,100,0.005,0.028910586306439074,3,0.2854389249917392,0,None,i7182,11,0.000294602797134125 1727277911,1727277925,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 130 confidence 0.005 feature_proportion 0.09738323130639011 n_clusters 3,130,0.005,0.09738323130639011,3,0.28257517347725525,0,None,i7182,11,0.00041085568125991015 1727277911,1727277926,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.11318287281672423 n_clusters 3,100,0.001,0.11318287281672423,3,0.28626500715937875,0,None,i7182,12,0.0003121946893287349 1727277923,1727277936,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 120 confidence 0.05 feature_proportion 0.12440171117404995 n_clusters 2,120,0.05,0.12440171117404995,2,0.2826853177662738,0,None,i7182,10,0.00034669578221295375 1727277923,1727277937,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.04044186350798262 n_clusters 2,100,0.01,0.04044186350798262,2,0.2805375041304108,0,None,i7182,10,0.0003074599153462465 1727277923,1727277938,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 215 confidence 0.001 feature_proportion 0.0813424781142499 n_clusters 3,215,0.001,0.0813424781142499,3,0.29331424165657005,0,None,i7182,12,0.0005544425359382956 1727277924,1727277940,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.10627960458479013 n_clusters 2,100,0.001,0.10627960458479013,2,0.2850534199801741,0,None,i7178,12,0.000298724662641393 1727277931,1727277943,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 235 confidence 0.01 feature_proportion 0.04988278452349009 n_clusters 2,235,0.01,0.04988278452349009,2,0.28626500715937875,0,None,i7182,9,0.0006439769431288323 1727277931,1727277944,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.005 feature_proportion 0.19400346596958346 n_clusters 3,104,0.005,0.19400346596958346,3,0.2820244520321621,0,None,i7182,10,0.00030775610166965784 1727277931,1727277944,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.018468888734889537 n_clusters 2,100,0.001,0.018468888734889537,2,0.2827403899107831,0,None,i7182,10,0.0003053408248829635 1727277951,1727277955,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 199 confidence 0.001 feature_proportion 0 n_clusters 3,199,0.001,0,3,None,1,None,i7182 1727277951,1727277965,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 136 confidence 0.1 feature_proportion 0.11143196664002658 n_clusters 3,136,0.1,0.11143196664002658,3,0.2822998127547087,0,None,i7182,10,0.0003972761569564519 1727277951,1727277965,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 166 confidence 0.05 feature_proportion 0.03953296965603051 n_clusters 3,166,0.05,0.03953296965603051,3,0.2848882035466461,0,None,i7182,10,0.0004709979597081695 1727277952,1727277966,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 189 confidence 0.005 feature_proportion 0.14285811140718063 n_clusters 3,189,0.005,0.14285811140718063,3,0.28075779270844803,0,None,i7182,11,0.0005761849097586655 1727277951,1727277966,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 250 confidence 0.005 feature_proportion 0.1126901258918167 n_clusters 3,250,0.005,0.1126901258918167,3,0.28923890296288135,0,None,i7182,12,0.0006867329535024215 1727277951,1727277967,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 140 confidence 0.001 feature_proportion 0.1441210236140281 n_clusters 3,140,0.001,0.1441210236140281,3,0.28290560634431106,0,None,i7182,12,0.00041819010546091765 1727277953,1727277967,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.167280718658282 n_clusters 2,100,0.01,0.167280718658282,2,0.28439255424606236,0,None,i7182,11,0.00028062042174124477 1727278031,1727278045,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 180 confidence 0.025 feature_proportion 0.19739527959843514 n_clusters 3,180,0.025,0.19739527959843514,3,0.2774534640378896,0,None,i7182,10,0.0005746416694759021 1727278031,1727278045,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.2 n_clusters 3,100,0.05,0.2,3,0.2812534420090318,0,None,i7182,11,0.0002949419294831785 1727278031,1727278046,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.12339113816776724 n_clusters 3,100,0.025,0.12339113816776724,3,0.2797114219627712,0,None,i7182,12,0.0003139745250185788 1727278044,1727278058,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 164 confidence 0.025 feature_proportion 0.2 n_clusters 2,164,0.025,0.2,2,0.28064764841942946,0,None,i7182,10,0.0004926178063905914 1727278044,1727278058,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.016772286104730035 n_clusters 1,100,0.005,0.016772286104730035,1,0.27888533979513164,0,None,i7182,11,0.00031155982876894544 1727278044,1727278058,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.08887206963716518 n_clusters 3,100,0.05,0.08887206963716518,3,0.280041854829827,0,None,i7182,11,0.0003085291732169142 1727278045,1727278059,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.2 n_clusters 4,100,0.025,0.2,4,0.27838969049454787,0,None,i7182,11,0.0003143443754014385 1727278051,1727278065,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 206 confidence 0.05 feature_proportion 0.1893214262894135 n_clusters 3,206,0.05,0.1893214262894135,3,0.2835113999339134,0,None,i7182,10,0.0005842711423228797 1727278051,1727278065,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.2 n_clusters 2,100,0.025,0.2,2,0.2792708448066967,0,None,i7182,11,0.00030766561178383907 1727278071,1727278085,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 1,100,0.25,0.2,1,0.28031721555237366,0,None,i7182,10,0.00030014318757572373 1727278071,1727278085,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.07567215640702751 n_clusters 1,100,0.025,0.07567215640702751,1,0.27916070051767816,0,None,i7182,10,0.0003082809430074065 1727278071,1727278085,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.1315727624768716 n_clusters 3,100,0.05,0.1315727624768716,3,0.2777838969049454,0,None,i7182,10,0.0003142171800614361 1727278072,1727278086,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 4,100,0.1,0.2,4,0.27965634981826193,0,None,i7182,10,0.00030381466387634443 1727278164,1727278177,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 161 confidence 0.025 feature_proportion 0.1672451170742337 n_clusters 3,161,0.025,0.1672451170742337,3,0.2793809890957154,0,None,i7182,10,0.0004951531551377344 1727278164,1727278177,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 155 confidence 0.005 feature_proportion 0.015263594869020234 n_clusters 1,155,0.005,0.015263594869020234,1,0.2811983698645225,0,None,i7182,10,0.00046618087238141587 1727278164,1727278177,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 157 confidence 0.025 feature_proportion 0.2 n_clusters 4,157,0.025,0.2,4,0.28147373058706904,0,None,i7182,10,0.00046376542744679724 1727278171,1727278184,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 203 confidence 0.1 feature_proportion 0.2 n_clusters 4,203,0.1,0.2,4,0.28929397510739063,0,None,i7182,10,0.000514423895302847 1727278171,1727278185,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 160 confidence 0.01 feature_proportion 0.2 n_clusters 3,160,0.01,0.2,3,0.2842824099570437,0,None,i7182,10,0.00044696945856214504 1727278191,1727278195,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 131 confidence 0.025 feature_proportion 0 n_clusters 1,131,0.025,0,1,None,1,None,i7182 1727278191,1727278195,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 147 confidence 0.001 feature_proportion 0 n_clusters 1,147,0.001,0,1,None,1,None,i7182 1727278191,1727278205,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 186 confidence 0.025 feature_proportion 0.1613791399203211 n_clusters 3,186,0.025,0.1613791399203211,3,0.28174909130961556,0,None,i7182,10,0.0005478531042332121 1727278191,1727278205,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 174 confidence 0.01 feature_proportion 0.19643273322141896 n_clusters 3,174,0.01,0.19643273322141896,3,0.2827954620552924,0,None,i7182,10,0.0005053679143207015 1727278191,1727278205,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 150 confidence 0.005 feature_proportion 0.05595199365761913 n_clusters 1,150,0.005,0.05595199365761913,1,0.2850534199801741,0,None,i7182,10,0.0004177082147104224 1727278211,1727278214,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 confidence 0.01 feature_proportion 0 n_clusters 1,143,0.01,0,1,None,1,None,i7182 1727278211,1727278224,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 confidence 0.025 feature_proportion 0.2 n_clusters 3,154,0.025,0.2,3,0.2857142857142857,0,None,i7182,10,0.0004192129620837778 1727278211,1727278225,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 158 confidence 0.01 feature_proportion 0.2 n_clusters 4,158,0.01,0.2,4,0.2768476704482873,0,None,i7182,10,0.0005133510613188985 1727278212,1727278225,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 254 confidence 0.1 feature_proportion 0.2 n_clusters 3,254,0.1,0.2,3,0.2898997686969931,0,None,i7182,10,0.0006380501313864012 1727278291,1727278304,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.008081171800640817 n_clusters 1,100,0.1,0.008081171800640817,1,0.2800969269743364,0,None,i7182,10,0.00030136701300926394 1727278311,1727278314,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727278311,1727278325,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.09828554898386631 n_clusters 1,100,0.25,0.09828554898386631,1,0.28136358629805047,0,None,i7182,10,0.00029433001676640807 1727278311,1727278325,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 2,100,0.25,0.2,2,0.2801519991188457,0,None,i7182,10,0.00030106105665087907 1727278311,1727278325,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 3,100,0.25,0.2,3,0.2797114219627712,0,None,i7182,10,0.0003035087075179595 1727278314,1727278327,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.05 feature_proportion 0.2 n_clusters 1,144,0.05,0.2,1,0.281638947020597,0,None,i7182,10,0.0004216323383632554 1727278331,1727278345,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.14666787741545645 n_clusters 1,100,0.05,0.14666787741545645,1,0.27993171054080845,0,None,i7182,10,0.0003022848820844193 1727278331,1727278345,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1488860287627088 n_clusters 2,100,0.25,0.1488860287627088,2,0.2801519991188457,0,None,i7182,10,0.00030106105665087907 1727278331,1727278345,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.08710688124282696 n_clusters 2,100,0.25,0.08710688124282696,2,0.2782795462055292,0,None,i7182,10,0.0003114635728359707 1727278351,1727278354,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7182 1727278352,1727278355,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727278344,1727278357,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 137 confidence 0.1 feature_proportion 0.16390546438265288 n_clusters 1,137,0.1,0.16390546438265288,1,0.2787201233616037,0,None,i7182,10,0.0004246020301862614 1727278344,1727278358,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 172 confidence 0.25 feature_proportion 0.2 n_clusters 1,172,0.25,0.2,1,0.286485295737416,0,None,i7182,10,0.00046017013056334554 1727278344,1727278358,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.06708205578704814 n_clusters 1,100,0.05,0.06708205578704814,1,0.2793259169512061,0,None,i7182,10,0.0003073579461720548 1727278371,1727278384,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05481052090102626 n_clusters 1,100,0.25,0.05481052090102626,1,0.28136358629805047,0,None,i7182,10,0.00029433001676640807 1727278371,1727278384,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.11980815109535009 n_clusters 2,100,0.25,0.11980815109535009,2,0.2782795462055292,0,None,i7182,10,0.0003114635728359707 1727278464,1727278467,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727278471,1727278484,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 157 confidence 0.05 feature_proportion 0.05672994093368125 n_clusters 1,157,0.05,0.05672994093368125,1,0.2815838748760877,0,None,i7182,10,0.0004627992494729492 1727278491,1727278494,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7182 1727278491,1727278494,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 151 confidence 0.25 feature_proportion 0 n_clusters 1,151,0.25,0,1,None,1,None,i7182 1727278494,1727278497,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 208 confidence 0.25 feature_proportion 0 n_clusters 1,208,0.25,0,1,None,1,None,i7182 1727278491,1727278505,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.1541833145056859 n_clusters 2,100,0.1,0.1541833145056859,2,0.2786650512170944,0,None,i7182,10,0.00030932187832727516 1727278491,1727278505,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12924051416675505 n_clusters 4,100,0.25,0.12924051416675505,4,0.2801519991188457,0,None,i7182,10,0.00030106105665087907 1727278511,1727278524,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 158 confidence 0.25 feature_proportion 0.07138086876497977 n_clusters 1,158,0.25,0.07138086876497977,1,0.2771781033153431,0,None,i7182,10,0.0005058839469085029 1727278511,1727278524,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 172 confidence 0.1 feature_proportion 0.01904307198432247 n_clusters 1,172,0.1,0.01904307198432247,1,0.2800969269743364,0,None,i7182,10,0.0005266607994336652 1727278511,1727278525,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.08460190510745053 n_clusters 2,100,0.05,0.08460190510745053,2,0.27734331974887105,0,None,i7182,10,0.0003202228627367015 1727278524,1727278528,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7178 1727278524,1727278528,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7178 1727278524,1727278539,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.12606332133494194 n_clusters 3,100,0.25,0.12606332133494194,3,0.27954620552924336,0,None,i7178,11,0.00030442657659311424 1727278531,1727278544,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 162 confidence 0.1 feature_proportion 0.08240608522046189 n_clusters 1,162,0.1,0.08240608522046189,1,0.28224474061019933,0,None,i7182,9,0.00047362044278004175 1727278552,1727278565,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1321751428402261 n_clusters 1,100,0.25,0.1321751428402261,1,0.28031721555237366,0,None,i7182,10,0.00030014318757572373 1727278552,1727278566,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.05 feature_proportion 0.040917674834797496 n_clusters 2,104,0.05,0.040917674834797496,2,0.2797114219627712,0,None,i7182,10,0.00031948285001890477 1727278552,1727278566,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 175 confidence 0.1 feature_proportion 0.059508679931502176 n_clusters 1,175,0.1,0.059508679931502176,1,0.2830157506333296,0,None,i7182,10,0.0005032082223791598 1727278552,1727278566,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.052815648415997535 n_clusters 2,100,0.05,0.052815648415997535,2,0.2777838969049454,0,None,i7182,11,0.00031774771017448596 1727278671,1727278674,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727278692,1727278694,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727278692,1727278694,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 209 confidence 0.25 feature_proportion 0 n_clusters 1,209,0.25,0,1,None,1,None,i7182 1727278692,1727278705,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.14770145591685854 n_clusters 3,100,0.1,0.14770145591685854,3,0.27916070051767816,0,None,i7182,10,0.0003065682711018098 1727278704,1727278707,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 confidence 0.25 feature_proportion 0 n_clusters 1,133,0.25,0,1,None,1,None,i7182 1727278704,1727278718,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.0154080725921198 n_clusters 4,100,0.25,0.0154080725921198,4,0.2816940191651063,0,None,i7182,10,0.00029249427861609804 1727278711,1727278725,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.2 n_clusters 4,100,0.01,0.2,4,0.2804273598413922,0,None,i7182,11,0.00030633880383302105 1727278732,1727278735,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 3,100,0.25,0,3,None,1,None,i7182 1727278732,1727278735,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7182 1727278732,1727278735,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 246 confidence 0.25 feature_proportion 0 n_clusters 1,246,0.25,0,1,None,1,None,i7182 1727278732,1727278746,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.10765177638350805 n_clusters 2,100,0.1,0.10765177638350805,2,0.27403899107831264,0,None,i7182,10,0.00033689384490330325 1727278751,1727278754,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727278751,1727278754,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727278751,1727278754,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.1 feature_proportion 0 n_clusters 1,181,0.1,0,1,None,1,None,i7182 1727278764,1727278778,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.01945162192321579 n_clusters 3,100,0.25,0.01945162192321579,3,0.28037228769688294,0,None,i7182,10,0.0003015122995481619 1727278764,1727278778,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05883723206903266 n_clusters 3,100,0.25,0.05883723206903266,3,0.28037228769688294,0,None,i7182,10,0.0003015122995481619 1727278885,1727278887,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727278885,1727278887,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727278891,1727278904,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.07128047469589946 n_clusters 2,100,0.1,0.07128047469589946,2,0.27849983478356644,0,None,i7182,9,0.0003119729303488128 1727278911,1727278914,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727278914,1727278917,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727278911,1727278924,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.10752664296401147 n_clusters 1,100,0.1,0.10752664296401147,1,0.2800969269743364,0,None,i7182,9,0.00030136701300926394 1727278911,1727278924,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.027479106722264543 n_clusters 2,100,0.25,0.027479106722264543,2,0.2774534640378896,0,None,i7182,9,0.00031605291821174615 1727278931,1727278934,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 229 confidence 0.25 feature_proportion 0 n_clusters 1,229,0.25,0,1,None,1,None,i7182 1727278931,1727278943,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 127 confidence 0.25 feature_proportion 0.0802050058510558 n_clusters 2,127,0.25,0.0802050058510558,2,0.280207071263355,0,None,i7182,9,0.0003839426812244606 1727278945,1727278947,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 311 confidence 0.25 feature_proportion 0 n_clusters 1,311,0.25,0,1,None,1,None,i7182 1727278951,1727278954,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 203 confidence 0.25 feature_proportion 0 n_clusters 3,203,0.25,0,3,None,1,None,i7182 1727278944,1727278957,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.06381659902250247 n_clusters 1,100,0.1,0.06381659902250247,1,0.2800969269743364,0,None,i7182,9,0.00030136701300926394 1727278971,1727278974,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 252 confidence 0.25 feature_proportion 0 n_clusters 3,252,0.25,0,3,None,1,None,i7182 1727278971,1727278974,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 376 confidence 0.25 feature_proportion 0 n_clusters 3,376,0.25,0,3,None,1,None,i7182 1727278971,1727278983,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.03402570343175877 n_clusters 2,100,0.1,0.03402570343175877,2,0.27321290891067296,0,None,i7182,9,0.00034150882908006125 1727278971,1727278985,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 172 confidence 0.001 feature_proportion 0.2 n_clusters 4,172,0.001,0.2,4,0.27849983478356644,0,None,i7182,10,0.0005640722680044191 1727278991,1727278994,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 301 confidence 0.1 feature_proportion 0 n_clusters 1,301,0.1,0,1,None,1,None,i7182 1727278991,1727279003,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05115776833529903 n_clusters 2,100,0.25,0.05115776833529903,2,0.276792598303778,0,None,i7182,9,0.0003197243945123662 1727279111,1727279114,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727279125,1727279128,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727279131,1727279134,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7182 1727279151,1727279154,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727279151,1727279154,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727279152,1727279155,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 166 confidence 0.25 feature_proportion 0 n_clusters 1,166,0.25,0,1,None,1,None,i7182 1727279155,1727279158,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727279172,1727279174,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727279172,1727279174,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 confidence 0.25 feature_proportion 0 n_clusters 2,143,0.25,0,2,None,1,None,i7182 1727279185,1727279197,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.03136422486102896 n_clusters 1,100,0.05,0.03136422486102896,1,0.2793259169512061,0,None,i7182,9,0.0003073579461720548 1727279185,1727279200,15,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.001 feature_proportion 0.2 n_clusters 4,142,0.001,0.2,4,0.28670558431545323,0,None,i7182,12,0.00041066728448750596 1727279191,1727279204,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.05087772050666572 n_clusters 2,100,0.1,0.05087772050666572,2,0.27706795902632453,0,None,i7182,9,0.00031997223625519213 1727279211,1727279214,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727279211,1727279214,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 230 confidence 0.25 feature_proportion 0 n_clusters 1,230,0.25,0,1,None,1,None,i7182 1727279211,1727279214,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 288 confidence 0.25 feature_proportion 0 n_clusters 2,288,0.25,0,2,None,1,None,i7182 1727279232,1727279234,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727279232,1727279235,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 235 confidence 0.25 feature_proportion 0 n_clusters 2,235,0.25,0,2,None,1,None,i7182 1727279232,1727279235,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 372 confidence 0.25 feature_proportion 0 n_clusters 1,372,0.25,0,1,None,1,None,i7182 1727279432,1727279434,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 246 confidence 0.25 feature_proportion 0 n_clusters 3,246,0.25,0,3,None,1,None,i7182 1727279410,1727279456,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7184 1727279410,1727279456,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7184 1727279413,1727279456,43,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7184 1727279412,1727279456,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7184 1727279412,1727279456,44,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7184 1727279425,1727279456,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 258 confidence 0.25 feature_proportion 0 n_clusters 1,258,0.25,0,1,None,1,None,i7184 1727279452,1727279456,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 189 confidence 0.25 feature_proportion 0 n_clusters 1,189,0.25,0,1,None,1,None,i7184 1727279452,1727279456,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 252 confidence 0.25 feature_proportion 0 n_clusters 2,252,0.25,0,2,None,1,None,i7184 1727279425,1727279456,31,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7184 1727279410,1727279456,46,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7184 1727279432,1727279456,24,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 143 confidence 0.25 feature_proportion 0 n_clusters 2,143,0.25,0,2,None,1,None,i7184 1727279453,1727279465,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 130 confidence 0.1 feature_proportion 0.01494095505616089 n_clusters 1,130,0.1,0.01494095505616089,1,0.2820244520321621,0,None,i7182,9,0.0003818871334586995 1727279472,1727279475,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.25 feature_proportion 0 n_clusters 1,138,0.25,0,1,None,1,None,i7184 1727279472,1727279486,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 241 confidence 0.25 feature_proportion 0.004611359294566957 n_clusters 2,241,0.25,0.004611359294566957,2,0.2827403899107831,0,None,i7184,11,0.0006973324243948761 1727279492,1727279496,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 420 confidence 0.25 feature_proportion 0 n_clusters 2,420,0.25,0,2,None,1,None,i7184 1727279485,1727279501,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.03323492276823122 n_clusters 4,100,0.05,0.03323492276823122,4,0.28549399713624846,0,None,i7184,12,0.00027913709817003123 1727279666,1727279669,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727279672,1727279675,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727279692,1727279695,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 342 confidence 0.25 feature_proportion 0 n_clusters 1,342,0.25,0,1,None,1,None,i7182 1727279696,1727279699,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 423 confidence 0.25 feature_proportion 0 n_clusters 1,423,0.25,0,1,None,1,None,i7182 1727279692,1727279705,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 379 confidence 0.25 feature_proportion 0.05526035182748032 n_clusters 2,379,0.25,0.05526035182748032,2,0.2886881815177883,0,None,i7182,10,0.0009972847038315841 1727279712,1727279715,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727279726,1727279729,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727279732,1727279735,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 141 confidence 0.25 feature_proportion 0 n_clusters 2,141,0.25,0,2,None,1,None,i7182 1727279726,1727279739,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.09178481573413005 n_clusters 2,100,0.1,0.09178481573413005,2,0.27954620552924336,0,None,i7182,10,0.0003061272837249193 1727279752,1727279755,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727279752,1727279755,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727279756,1727279770,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 371 confidence 0.25 feature_proportion 0.035268170977396596 n_clusters 2,371,0.25,0.035268170977396596,2,0.2834563277894041,0,None,i7182,11,0.001135710002325268 1727279772,1727279775,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 128 confidence 0.1 feature_proportion 0 n_clusters 2,128,0.1,0,2,None,1,None,i7182 1727279772,1727279785,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 240 confidence 0.25 feature_proportion 0.006550836244915511 n_clusters 2,240,0.25,0.006550836244915511,2,0.28879832580680687,0,None,i7182,10,0.0006184452984761392 1727279786,1727279789,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727279786,1727279799,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 371 confidence 0.25 feature_proportion 0.012474694277423534 n_clusters 2,371,0.25,0.012474694277423534,2,0.28334618350038554,0,None,i7182,10,0.0011381576531923473 1727279792,1727279804,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 138 confidence 0.01 feature_proportion 0.10583539446264185 n_clusters 1,138,0.01,0.10583539446264185,1,0.28257517347725525,0,None,i7182,9,0.0004013008979747959 1727279830,1727279838,8,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 312 confidence 0.1 feature_proportion 0 n_clusters 2,312,0.1,0,2,None,1,None,i7185 1727279936,1727279939,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7185 1727279952,1727279954,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7185 1727279966,1727279968,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7185 1727279966,1727279968,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7185 1727279972,1727279974,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7185 1727279992,1727279994,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7185 1727279992,1727279995,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7185 1727279996,1727279998,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7185 1727280012,1727280015,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7185 1727280012,1727280024,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 121 confidence 0.1 feature_proportion 0.06449043736005645 n_clusters 2,121,0.1,0.06449043736005645,2,0.2842824099570437,0,None,i7185,9,0.00033824715783081245 1727280026,1727280028,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7185 1727280032,1727280043,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.2 n_clusters 1,100,0.025,0.2,1,0.2804824319859015,0,None,i7185,9,0.00030089696832459454 1727280052,1727280055,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7185 1727280052,1727280055,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7185 1727280056,1727280058,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7185 1727280072,1727280075,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 475 confidence 0.25 feature_proportion 0 n_clusters 1,475,0.25,0,1,None,1,None,i7185 1727280092,1727280095,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 376 confidence 0.25 feature_proportion 0 n_clusters 4,376,0.25,0,4,None,1,None,i7185 1727280086,1727280098,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.2 n_clusters 4,100,0.05,0.2,4,0.27503028967948007,0,None,i7185,9,0.0003313558638911947 1727280086,1727280098,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.17997496455387227 n_clusters 4,100,0.25,0.17997496455387227,4,0.2804273598413922,0,None,i7185,9,0.0002995312748589539 1727280236,1727280239,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727280252,1727280254,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727280266,1727280269,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727280266,1727280269,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727280292,1727280295,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0 n_clusters 1,100,0.005,0,1,None,1,None,i7182 1727280292,1727280295,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.25 feature_proportion 0 n_clusters 1,144,0.25,0,1,None,1,None,i7182 1727280296,1727280299,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 160 confidence 0.01 feature_proportion 0 n_clusters 1,160,0.01,0,1,None,1,None,i7182 1727280313,1727280316,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727280326,1727280329,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727280332,1727280335,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 145 confidence 0.001 feature_proportion 0 n_clusters 1,145,0.001,0,1,None,1,None,i7182 1727280326,1727280338,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.2 n_clusters 4,100,0.25,0.2,4,0.28075779270844803,0,None,i7182,9,0.0002976955367086439 1727280352,1727280364,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.016780906584811028 n_clusters 1,100,0.005,0.016780906584811028,1,0.27888533979513164,0,None,i7182,9,0.00031155982876894544 1727280352,1727280364,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.12474679307507362 n_clusters 2,100,0.05,0.12474679307507362,2,0.2804273598413922,0,None,i7182,9,0.00030289679480118936 1727280373,1727280376,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 328 confidence 0.25 feature_proportion 0 n_clusters 1,328,0.25,0,1,None,1,None,i7184 1727280373,1727280376,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 438 confidence 0.25 feature_proportion 0 n_clusters 1,438,0.25,0,1,None,1,None,i7184 1727280387,1727280390,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7184 1727280393,1727280407,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.016828327482921596 n_clusters 1,100,0.005,0.016828327482921596,1,0.27888533979513164,0,None,i7184,10,0.00031155982876894544 1727280552,1727280555,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727280567,1727280569,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727280568,1727280570,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727280572,1727280575,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727280592,1727280595,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727280597,1727280599,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727280612,1727280624,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.04051613145945107 n_clusters 2,100,0.25,0.04051613145945107,2,0.276792598303778,0,None,i7182,9,0.0003197243945123662 1727280627,1727280630,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727280627,1727280630,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727280632,1727280635,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727280652,1727280655,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727280675,1727280686,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7183 1727280675,1727280686,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7183 1727280687,1727280690,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7183 1727280687,1727280690,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 447 confidence 0.25 feature_proportion 0 n_clusters 1,447,0.25,0,1,None,1,None,i7183 1727280692,1727280695,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 494 confidence 0.25 feature_proportion 0 n_clusters 1,494,0.25,0,1,None,1,None,i7183 1727280717,1727280731,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.2 n_clusters 3,100,0.1,0.2,3,0.28037228769688294,0,None,i7183,11,0.0002998372312173388 1727280714,1727280733,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 584 confidence 0.025 feature_proportion 0.01205255687236786 n_clusters 4,584,0.025,0.01205255687236786,4,0.2958475603039983,0,None,i7183,16,0.0014890660611554956 1727280733,1727280736,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 385 confidence 0.25 feature_proportion 0 n_clusters 4,385,0.25,0,4,None,1,None,i7184 1727280913,1727280917,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7186 1727280910,1727280917,7,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727280913,1727280917,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727280928,1727280931,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 525 confidence 0.1 feature_proportion 0 n_clusters 4,525,0.1,0,4,None,1,None,i7186 1727280953,1727280956,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7186 1727280954,1727280957,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7186 1727280957,1727280970,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 270 confidence 0.1 feature_proportion 0.06250317500824622 n_clusters 4,270,0.1,0.06250317500824622,4,0.2849432756911554,0,None,i7186,10,0.0007518182151952388 1727280973,1727280976,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727280987,1727280990,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727280993,1727280996,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727281013,1727281015,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727281013,1727281025,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 246 confidence 0.25 feature_proportion 0.037427697376058486 n_clusters 4,246,0.25,0.037427697376058486,4,0.2893490472519,0,None,i7186,9,0.0006249158620014431 1727281033,1727281036,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 461 confidence 0.25 feature_proportion 0 n_clusters 3,461,0.25,0,3,None,1,None,i7186 1727281033,1727281046,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.09779676865716574 n_clusters 3,100,0.1,0.09779676865716574,3,0.27552593898006383,0,None,i7186,10,0.00032858687338514016 1727281047,1727281060,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 300 confidence 0.25 feature_proportion 0.017493468304008136 n_clusters 3,300,0.25,0.017493468304008136,3,0.2876418107721115,0,None,i7186,9,0.0007952791037615201 1727281073,1727281075,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 434 confidence 0.25 feature_proportion 0 n_clusters 1,434,0.25,0,1,None,1,None,i7186 1727281073,1727281075,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 434 confidence 0.25 feature_proportion 0 n_clusters 4,434,0.25,0,4,None,1,None,i7186 1727281254,1727281257,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727281257,1727281260,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727281273,1727281275,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727281288,1727281291,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727281293,1727281295,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727281313,1727281316,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727281318,1727281320,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727281333,1727281335,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727281353,1727281355,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727281348,1727281360,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 1,100,0.001,0.2,1,0.2841722656680251,0,None,i7182,9,0.00028345041609027576 1727281373,1727281375,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 527 confidence 0.25 feature_proportion 0 n_clusters 1,527,0.25,0,1,None,1,None,i7182 1727281373,1727281385,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.14876273164713613 n_clusters 1,100,0.001,0.14876273164713613,1,0.2841722656680251,0,None,i7182,9,0.00028345041609027576 1727281393,1727281395,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727281408,1727281411,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727281408,1727281411,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 523 confidence 0.25 feature_proportion 0 n_clusters 1,523,0.25,0,1,None,1,None,i7182 1727281433,1727281435,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727281433,1727281445,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 392 confidence 0.25 feature_proportion 0.008157396350348458 n_clusters 1,392,0.25,0.008157396350348458,1,0.2930939530785329,0,None,i7182,9,0.0009644029026862383 1727281453,1727281465,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.16236711623956546 n_clusters 1,100,0.005,0.16236711623956546,1,0.2787201233616037,0,None,i7182,9,0.0003107422679016774 1727281613,1727281616,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727281633,1727281635,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727281648,1727281651,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727281653,1727281656,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727281673,1727281676,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7186 1727281678,1727281681,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727281693,1727281696,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727281708,1727281712,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727281738,1727281741,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7186 1727281733,1727281747,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 210 confidence 0.005 feature_proportion 0.19522953182472985 n_clusters 4,210,0.005,0.19522953182472985,4,0.28742152219407424,0,None,i7186,11,0.0005722130136820693 1727281753,1727281756,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 399 confidence 0.25 feature_proportion 0 n_clusters 4,399,0.25,0,4,None,1,None,i7186 1727281733,1727281761,28,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 418 confidence 0.001 feature_proportion 0.2 n_clusters 4,418,0.001,0.2,4,0.2874765943385835,0,None,i7186,25,0.0015189252759825046 1727281768,1727281781,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 391 confidence 0.25 feature_proportion 0.2 n_clusters 4,391,0.25,0.2,4,0.28852296508426034,0,None,i7186,9,0.0010231180624395739 1727281793,1727281797,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727281793,1727281807,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 398 confidence 0.25 feature_proportion 0.12421399721249451 n_clusters 4,398,0.25,0.12421399721249451,4,0.2864302235929067,0,None,i7186,10,0.0010939330522985104 1727281798,1727281815,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 265 confidence 0.005 feature_proportion 0.19570635920267443 n_clusters 4,265,0.005,0.19570635920267443,4,0.2908359951536513,0,None,i7186,13,0.0006940838530220609 1727281813,1727281835,22,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 379 confidence 0.005 feature_proportion 0.1388260162974582 n_clusters 4,379,0.005,0.1388260162974582,4,0.29518669456988655,0,None,i7186,18,0.0009844145831038654 1727281828,1727281842,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 398 confidence 0.25 feature_proportion 0.11609253579445683 n_clusters 4,398,0.25,0.11609253579445683,4,0.2932040973675515,0,None,i7186,10,0.0009399813756020382 1727281973,1727281977,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727281993,1727281996,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7186 1727282009,1727282012,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727282013,1727282016,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727282033,1727282036,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7186 1727282039,1727282051,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 164 confidence 0.001 feature_proportion 0.2 n_clusters 1,164,0.001,0.2,1,0.28554906928075774,0,None,i7186,9,0.00045601794425463723 1727282053,1727282067,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 116 confidence 0.001 feature_proportion 0.2 n_clusters 1,116,0.001,0.2,1,0.27921577266218744,0,None,i7186,10,0.00036267905693300315 1727282069,1727282072,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7186 1727282073,1727282087,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 confidence 0.001 feature_proportion 0.2 n_clusters 1,126,0.001,0.2,1,0.282354884899218,0,None,i7186,10,0.00037134360297704226 1727282093,1727282096,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727282113,1727282116,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727282129,1727282132,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7186 1727282130,1727282133,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727282153,1727282156,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727282159,1727282162,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727282309,1727282312,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727282313,1727282316,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727282333,1727282336,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727282353,1727282365,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.2 n_clusters 1,100,0.05,0.2,1,0.27993171054080845,0,None,i7182,9,0.0003022848820844193 1727282369,1727282372,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727282356,1727282375,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7119 1727282373,1727282376,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727282393,1727282396,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727282414,1727282417,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7185 1727282429,1727282432,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7185 1727282429,1727282432,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7185 1727282453,1727282465,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06509236758610154 n_clusters 2,100,0.25,0.06509236758610154,2,0.276792598303778,0,None,i7186,9,0.0003197243945123662 1727282459,1727282471,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.08651932434130093 n_clusters 3,100,0.25,0.08651932434130093,3,0.27690274259279657,0,None,i7186,9,0.0003191124817955964 1727282473,1727282485,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.14888543895072257 n_clusters 4,100,0.025,0.14888543895072257,4,0.2767375261592686,0,None,i7186,9,0.00032545459410584924 1727282492,1727282504,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.11423710745186871 n_clusters 1,100,0.01,0.11423710745186871,1,0.2779491133384734,0,None,i7185,9,0.0003168195279636547 1727282513,1727282525,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.1627518777597272 n_clusters 4,100,0.05,0.1627518777597272,4,0.27855490692807583,0,None,i7186,9,0.00031166526473702845 1727282653,1727282656,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727282669,1727282672,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727282673,1727282676,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727282693,1727282696,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727282699,1727282702,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727282713,1727282716,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727282729,1727282732,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727282753,1727282756,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727282759,1727282772,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.16618529880643765 n_clusters 1,100,0.025,0.16618529880643765,1,0.2804824319859015,0,None,i7182,9,0.00030089696832459454 1727282773,1727282785,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0.11853837981250961 n_clusters 1,100,0.025,0.11853837981250961,1,0.27916070051767816,0,None,i7182,9,0.0003082809430074065 1727282791,1727282795,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727282793,1727282796,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727282814,1727282831,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.05832879754096592 n_clusters 2,100,0.25,0.05832879754096592,2,0.2790505562286596,0,None,i7182,13,0.0003071801838185796 1727282833,1727282836,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727282850,1727282853,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727282850,1727282862,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0.2 n_clusters 1,100,0.01,0.2,1,0.28180416345412496,0,None,i7186,9,0.0002947436560889174 1727283014,1727283016,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727283030,1727283033,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7186 1727283054,1727283056,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727283054,1727283067,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.06799975296081233 n_clusters 2,100,0.25,0.06799975296081233,2,0.2782795462055292,0,None,i7186,10,0.0003114635728359707 1727283074,1727283077,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727283090,1727283093,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727283094,1727283106,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.1570424006673162 n_clusters 1,100,0.05,0.1570424006673162,1,0.27993171054080845,0,None,i7186,9,0.0003022848820844193 1727283120,1727283123,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7186 1727283114,1727283128,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.05 feature_proportion 0.04094197039254827 n_clusters 2,104,0.05,0.04094197039254827,2,0.2820244520321621,0,None,i7186,10,0.00030417754234791765 1727283151,1727283155,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727283154,1727283156,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727283175,1727283179,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727283195,1727283198,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727283210,1727283213,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727283211,1727283215,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727283236,1727283240,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727283240,1727283243,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727283414,1727283416,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727283415,1727283418,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727283435,1727283438,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727283451,1727283454,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727283474,1727283477,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727283474,1727283477,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7186 1727283494,1727283497,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7186 1727283511,1727283514,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7186 1727283534,1727283537,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727283534,1727283546,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 156 confidence 0.01 feature_proportion 0.14789833128311256 n_clusters 1,156,0.01,0.14789833128311256,1,0.2868708007489812,0,None,i7186,9,0.0004164227067282696 1727283554,1727283557,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 1,100,0.05,0,1,None,1,None,i7186 1727283571,1727283574,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 2,100,0.001,0,2,None,1,None,i7186 1727283594,1727283597,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727283594,1727283597,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 4,100,0.25,0,4,None,1,None,i7186 1727283614,1727283617,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7186 1727283631,1727283634,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727283654,1727283657,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727283811,1727283814,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727283814,1727283817,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727283834,1727283837,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7186 1727283854,1727283856,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727283871,1727283885,14,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.07761774810124833 n_clusters 4,100,0.25,0.07761774810124833,4,0.2794360612402247,0,None,i7185,9,0.00030503848930988465 1727283894,1727283897,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727283894,1727283897,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7186 1727283914,1727283926,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.18608996530258626 n_clusters 1,100,0.1,0.18608996530258626,1,0.27728824760436166,0,None,i7186,9,0.0003169707872869015 1727283931,1727283934,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727283961,1727283964,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727283954,1727283967,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.005 feature_proportion 0.06249839940078316 n_clusters 1,100,0.005,0.06249839940078316,1,0.27888533979513164,0,None,i7186,9,0.00031155982876894544 1727283974,1727283977,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7186 1727283991,1727284003,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.14490786093349037 n_clusters 3,100,0.1,0.14490786093349037,3,0.2786650512170944,0,None,i7186,9,0.00030932187832727516 1727284014,1727284017,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727284021,1727284024,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727284051,1727284054,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7186 1727284194,1727284197,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727284214,1727284217,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727284231,1727284234,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727284254,1727284257,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727284254,1727284257,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727284274,1727284277,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727284292,1727284294,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727284314,1727284317,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727284322,1727284334,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.1732241995604794 n_clusters 4,100,0.25,0.1732241995604794,4,0.2801519991188457,0,None,i7182,9,0.00030106105665087907 1727284334,1727284337,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727284352,1727284364,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.045459298479500664 n_clusters 1,100,0.25,0.045459298479500664,1,0.28136358629805047,0,None,i7182,9,0.00029433001676640807 1727284374,1727284386,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 171 confidence 0.01 feature_proportion 0.10003803241997958 n_clusters 2,171,0.01,0.10003803241997958,2,0.2852737085582112,0,None,i7182,9,0.0004810713799783599 1727284394,1727284406,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.16330796248279056 n_clusters 3,100,0.05,0.16330796248279056,3,0.27949113338473397,0,None,i7182,9,0.00030473253295149977 1727284412,1727284414,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0 n_clusters 2,100,0.05,0,2,None,1,None,i7182 1727284414,1727284417,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727284442,1727284444,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 1,100,0.25,0,1,None,1,None,i7182 1727284592,1727284595,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727284614,1727284617,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727284634,1727284637,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727284652,1727284664,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.2 n_clusters 1,100,0.001,0.2,1,0.2841722656680251,0,None,i7182,9,0.00028345041609027576 1727284674,1727284677,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727284674,1727284686,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0.15813430503058554 n_clusters 1,100,0.1,0.15813430503058554,1,0.27728824760436166,0,None,i7182,9,0.0003169707872869015 1727284694,1727284697,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727284712,1727284724,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0.1510383696514521 n_clusters 1,100,0.001,0.1510383696514521,1,0.2841722656680251,0,None,i7182,9,0.00028345041609027576 1727284734,1727284737,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.001 feature_proportion 0 n_clusters 1,100,0.001,0,1,None,1,None,i7182 1727284744,1727284761,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0.08977847880633143 n_clusters 2,100,0.25,0.08977847880633143,2,0.2774534640378896,0,None,i7182,13,0.00031605291821174615 1727284774,1727284777,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727284773,1727284785,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.05 feature_proportion 0.1476908803722987 n_clusters 3,100,0.05,0.1476908803722987,3,0.27965634981826193,0,None,i7182,9,0.00030551195250135194 1727284795,1727284799,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 2,100,0.025,0,2,None,1,None,i7182 1727284816,1727284819,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.025 feature_proportion 0 n_clusters 1,100,0.025,0,1,None,1,None,i7182 1727284834,1727284837,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7186 1727284855,1727284858,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 133 confidence 0.25 feature_proportion 0 n_clusters 2,133,0.25,0,2,None,1,None,i7186 1727285163,1727285165,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 153 confidence 0.25 feature_proportion 0 n_clusters 1,153,0.25,0,1,None,1,None,i7182 1727285156,1727285173,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 172 confidence 0.01 feature_proportion 0.2 n_clusters 4,172,0.01,0.2,4,0.2815288027315783,0,None,i7182,13,0.0005078287171579385 1727285175,1727285186,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 129 confidence 0.25 feature_proportion 0.2 n_clusters 1,129,0.25,0.2,1,0.2805375041304108,0,None,i7182,9,0.0003870898214790873 1727285196,1727285212,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 146 confidence 0.25 feature_proportion 0.07227093338934047 n_clusters 1,146,0.25,0.07227093338934047,1,0.28406212137900655,0,None,i7182,12,0.0004087875442032307 1727285215,1727285217,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 191 confidence 0.005 feature_proportion 0 n_clusters 1,191,0.005,0,1,None,1,None,i7182 1727285236,1727285254,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.005 feature_proportion 0.14423241819112495 n_clusters 4,169,0.005,0.14423241819112495,4,0.27998678268531774,0,None,i7182,15,0.0005277301614629727 1727285253,1727285265,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 119 confidence 0.05 feature_proportion 0.2 n_clusters 1,119,0.05,0.2,1,0.2791056283731689,0,None,i7182,9,0.0003658103373697685 1727285276,1727285292,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 205 confidence 0.025 feature_proportion 0.11821718232757916 n_clusters 1,205,0.025,0.11821718232757916,1,0.2865403678819253,0,None,i7182,12,0.0005494554187825132 1727285298,1727285314,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 169 confidence 0.001 feature_proportion 0.08606348807607378 n_clusters 4,169,0.001,0.08606348807607378,4,0.2837867606564599,0,None,i7185,12,0.000549524224560261 1727285313,1727285325,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 195 confidence 0.005 feature_proportion 0.2 n_clusters 1,195,0.005,0.2,1,0.2793259169512061,0,None,i7185,9,0.0006045832127999759 1727285315,1727285327,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 199 confidence 0.005 feature_proportion 0.09871234583268346 n_clusters 1,199,0.005,0.09871234583268346,1,0.280207071263355,0,None,i7185,9,0.0006082687421645949 1727285335,1727285347,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 115 confidence 0.005 feature_proportion 0.0196509203180378 n_clusters 1,115,0.005,0.0196509203180378,1,0.2792708448066967,0,None,i7185,9,0.0003576113279825143 1727285355,1727285366,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 144 confidence 0.25 feature_proportion 0.08935387821365572 n_clusters 1,144,0.25,0.08935387821365572,1,0.28367661636744135,0,None,i7185,9,0.0004053309835885006 1727285373,1727285385,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 114 confidence 0.005 feature_proportion 0.0902018900891637 n_clusters 1,114,0.005,0.0902018900891637,1,0.27849983478356644,0,None,i7185,9,0.0003602784163383064 1727285395,1727285406,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 199 confidence 0.01 feature_proportion 0.053078853834053155 n_clusters 1,199,0.01,0.053078853834053155,1,0.28400704923449716,0,None,i7185,8,0.0005680475130285844 1727285415,1727285426,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 118 confidence 0.05 feature_proportion 0.2 n_clusters 1,118,0.05,0.2,1,0.2790505562286596,0,None,i7185,8,0.0003637660071535811 1727285433,1727285436,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 205 confidence 0.1 feature_proportion 0 n_clusters 1,205,0.1,0,1,None,1,None,i7185 1727285696,1727285713,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 110 confidence 0.01 feature_proportion 0.06972277990597149 n_clusters 1,110,0.01,0.06972277990597149,1,0.27706795902632453,0,None,i7182,12,0.000353549569689379 1727285715,1727285726,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 106 confidence 0.05 feature_proportion 0.2 n_clusters 4,106,0.05,0.2,4,0.28147373058706904,0,None,i7182,9,0.0003128358504670703 1727285735,1727285738,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 342 confidence 0.25 feature_proportion 0 n_clusters 2,342,0.25,0,2,None,1,None,i7182 1727285756,1727285772,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 190 confidence 0.001 feature_proportion 0.2 n_clusters 2,190,0.001,0.2,2,0.2804824319859015,0,None,i7182,12,0.0005791457777430367 1727285764,1727285775,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 200 confidence 0.001 feature_proportion 0.2 n_clusters 1,200,0.001,0.2,1,0.2857142857142857,0,None,i7182,8,0.0005525989045649798 1727285776,1727285787,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 131 confidence 0.25 feature_proportion 0.2 n_clusters 4,131,0.25,0.2,4,0.2804273598413922,0,None,i7182,8,0.0003935447406905964 1727285815,1727285818,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.01 feature_proportion 0 n_clusters 1,100,0.01,0,1,None,1,None,i7182 1727285816,1727285820,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.001 feature_proportion 0 n_clusters 1,181,0.001,0,1,None,1,None,i7182 1727285835,1727285847,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 185 confidence 0.001 feature_proportion 0.2 n_clusters 3,185,0.001,0.2,3,0.2793809890957154,0,None,i7182,9,0.0005725208356280055 1727285854,1727285866,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 126 confidence 0.25 feature_proportion 0.2 n_clusters 3,126,0.25,0.2,3,0.2827403899107831,0,None,i7182,9,0.00036085733849804775 1727285875,1727285878,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 350 confidence 0.25 feature_proportion 0 n_clusters 4,350,0.25,0,4,None,1,None,i7182 1727285884,1727285897,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 117 confidence 0.1 feature_proportion 0.2 n_clusters 4,117,0.1,0.2,4,0.2812534420090318,0,None,i7182,9,0.0003447373201751437 1727285914,1727285917,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 110 confidence 0.01 feature_proportion 0 n_clusters 1,110,0.01,0,1,None,1,None,i7182 1727285935,1727285938,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 340 confidence 0.25 feature_proportion 0 n_clusters 1,340,0.25,0,1,None,1,None,i7182 1727285944,1727285956,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 345 confidence 0.25 feature_proportion 0.15431314406226282 n_clusters 4,345,0.25,0.15431314406226282,4,0.29188236589932814,0,None,i7182,8,0.0008368806273473142 1727285974,1727285986,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 142 confidence 0.25 feature_proportion 0.2 n_clusters 4,142,0.25,0.2,4,0.27772882476043614,0,None,i7182,9,0.00044931876631403 1727286295,1727286307,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 154 confidence 0.25 feature_proportion 0.13947474021783676 n_clusters 4,154,0.25,0.13947474021783676,4,0.28560414142526713,0,None,i7182,9,0.0004201624818166965 1727286305,1727286316,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 465 confidence 0.25 feature_proportion 0.2 n_clusters 4,465,0.25,0.2,4,0.29028527370855817,0,None,i7182,8,0.001165210636460079 1727286315,1727286318,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.25 feature_proportion 0 n_clusters 2,100,0.25,0,2,None,1,None,i7182 1727286335,1727286347,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 114 confidence 0.01 feature_proportion 0.10876482014007516 n_clusters 1,114,0.01,0.10876482014007516,1,0.28064764841942946,0,None,i7182,9,0.0003442009031831696 1727286355,1727286367,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 163 confidence 0.1 feature_proportion 0.2 n_clusters 4,163,0.1,0.2,4,0.28373168851195063,0,None,i7182,9,0.0004601027345823027 1727286375,1727286387,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 110 confidence 0.01 feature_proportion 0.044969895511009175 n_clusters 1,110,0.01,0.044969895511009175,1,0.27706795902632453,0,None,i7182,9,0.000353549569689379 1727286397,1727286400,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 109 confidence 0.01 feature_proportion 0 n_clusters 1,109,0.01,0,1,None,1,None,i7182 1727286415,1727286418,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 180 confidence 0.001 feature_proportion 0 n_clusters 1,180,0.001,0,1,None,1,None,i7182 1727286435,1727286447,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 475 confidence 0.25 feature_proportion 0.2 n_clusters 1,475,0.25,0.2,1,0.29226787091089323,0,None,i7182,8,0.0011431191076526467 1727286455,1727286466,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 468 confidence 0.25 feature_proportion 0.2 n_clusters 3,468,0.25,0.2,3,0.29491133384734003,0,None,i7182,8,0.001071674163424355 1727286475,1727286487,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 470 confidence 0.25 feature_proportion 0.021224734671334202 n_clusters 4,470,0.25,0.021224734671334202,4,0.299206961119066,0,None,i7182,8,0.0009555761290533836 1727286495,1727286498,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 341 confidence 0.25 feature_proportion 0 n_clusters 1,341,0.25,0,1,None,1,None,i7182 1727286515,1727286527,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 190 confidence 0.01 feature_proportion 0.2 n_clusters 2,190,0.01,0.2,2,0.2868708007489812,0,None,i7182,9,0.00051163798769937 1727286535,1727286548,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 346 confidence 0.25 feature_proportion 0.0980746826974815 n_clusters 1,346,0.25,0.0980746826974815,1,0.2945809009802842,0,None,i7182,10,0.0008506888705054669 1727286545,1727286557,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 466 confidence 0.25 feature_proportion 0.1482707679258982 n_clusters 4,466,0.25,0.1482707679258982,4,0.2946359731247935,0,None,i7182,8,0.0010791163451148017 1727286575,1727286578,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 271 confidence 0.25 feature_proportion 0 n_clusters 1,271,0.25,0,1,None,1,None,i7182 1727286896,1727286898,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 110 confidence 0.005 feature_proportion 0 n_clusters 1,110,0.005,0,1,None,1,None,i7182 1727286916,1727286928,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 110 confidence 0.01 feature_proportion 0.00014052455007066266 n_clusters 1,110,0.01,0.00014052455007066266,1,0.27706795902632453,0,None,i7182,9,0.000353549569689379 1727286936,1727286938,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.001 feature_proportion 0 n_clusters 1,181,0.001,0,1,None,1,None,i7182 1727286956,1727286968,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 186 confidence 0.001 feature_proportion 0.07804454172485664 n_clusters 1,186,0.001,0.07804454172485664,1,0.27618680471417556,0,None,i7182,9,0.0006057935896023788 1727286966,1727286968,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 182 confidence 0.001 feature_proportion 0 n_clusters 1,182,0.001,0,1,None,1,None,i7182 1727286996,1727286998,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 113 confidence 0.001 feature_proportion 0 n_clusters 1,113,0.001,0,1,None,1,None,i7182 1727287017,1727287021,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.001 feature_proportion 0 n_clusters 1,181,0.001,0,1,None,1,None,i7182 1727287036,1727287038,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 164 confidence 0.25 feature_proportion 0 n_clusters 1,164,0.25,0,1,None,1,None,i7182 1727287027,1727287043,16,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 106 confidence 0.01 feature_proportion 0.007445980293814238 n_clusters 1,106,0.01,0.007445980293814238,1,0.282354884899218,0,None,i7182,12,0.00031130601447177194 1727287057,1727287074,17,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 178 confidence 0.001 feature_proportion 0.058670282113353955 n_clusters 2,178,0.001,0.058670282113353955,2,0.280041854829827,0,None,i7182,13,0.0005484963079411809 1727287087,1727287090,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 100 confidence 0.1 feature_proportion 0 n_clusters 2,100,0.1,0,2,None,1,None,i7182 1727287097,1727287101,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 738 confidence 0.25 feature_proportion 0 n_clusters 1,738,0.25,0,1,None,1,None,i7182 1727287117,1727287121,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 104 confidence 0.01 feature_proportion 0 n_clusters 1,104,0.01,0,1,None,1,None,i7182 1727287137,1727287155,18,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 184 confidence 0.001 feature_proportion 0.2 n_clusters 4,184,0.001,0.2,4,0.2783346183500386,0,None,i7182,14,0.0005895617996417404 1727287156,1727287159,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 267 confidence 0.025 feature_proportion 0 n_clusters 1,267,0.025,0,1,None,1,None,i7182 1727287177,1727287181,4,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 181 confidence 0.001 feature_proportion 0 n_clusters 1,181,0.001,0,1,None,1,None,i7182 1727287556,1727287559,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 269 confidence 0.1 feature_proportion 0 n_clusters 1,269,0.1,0,1,None,1,None,i7182 1727287567,1727287569,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 182 confidence 0.001 feature_proportion 0 n_clusters 1,182,0.001,0,1,None,1,None,i7182 1727287596,1727287599,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 266 confidence 0.005 feature_proportion 0 n_clusters 1,266,0.005,0,1,None,1,None,i7182 1727287616,1727287628,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 182 confidence 0.001 feature_proportion 0.009659092574850259 n_clusters 1,182,0.001,0.009659092574850259,1,0.2868708007489812,0,None,i7182,9,0.0004916750221140204 1727287627,1727287629,2,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 117 confidence 0.001 feature_proportion 0 n_clusters 1,117,0.001,0,1,None,1,None,i7182 1727287656,1727287659,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 180 confidence 0.001 feature_proportion 0 n_clusters 1,180,0.001,0,1,None,1,None,i7182 1727287676,1727287679,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 284 confidence 0.01 feature_proportion 0 n_clusters 1,284,0.01,0,1,None,1,None,i7182 1727287687,1727287698,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 183 confidence 0.001 feature_proportion 0.02483625622053194 n_clusters 1,183,0.001,0.02483625622053194,1,0.2789404119396409,0,None,i7182,9,0.0005711605915088969 1727287716,1727287719,3,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 272 confidence 0.05 feature_proportion 0 n_clusters 1,272,0.05,0,1,None,1,None,i7182 1727287736,1727287748,12,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 224 confidence 0.01 feature_proportion 0.14724999321345508 n_clusters 4,224,0.01,0.14724999321345508,4,0.2894591915409186,0,None,i7182,9,0.0005754333048087863 1727287747,1727287758,11,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 270 confidence 0.05 feature_proportion 8.72924956911362e-06 n_clusters 1,270,0.05,0.000008729249569114,1,0.28263024562176453,0,None,i7182,8,0.0007868641253375248
timestamp,ram_usage_mb,cpu_usage_percent 1727277346,476.44921875,28.5 1727277346,476.44921875,29.8 1727277346,476.44921875,28.6 1727277346,476.44921875,36.8 1727277346,476.44921875,25.0 1727277346,476.44921875,28.3 1727277346,476.44921875,29.4 1727277389,481.49609375,29.2 1727277389,481.49609375,20.6 1727277389,481.49609375,28.4 1727277389,481.49609375,25.0 1727277391,481.49609375,28.4 1727277391,481.49609375,24.2 1727277391,481.49609375,29.8 1727277391,481.49609375,22.6 1727277392,481.49609375,28.4 1727277392,481.49609375,21.9 1727277392,481.49609375,30.2 1727277392,481.49609375,25.0 1727277393,481.49609375,28.5 1727277393,481.49609375,26.5 1727277393,481.49609375,28.7 1727277393,481.49609375,27.3 1727277395,481.49609375,28.4 1727277395,481.49609375,35.9 1727277395,481.49609375,25.6 1727277395,481.49609375,33.3 1727277396,481.49609375,28.5 1727277396,481.49609375,38.1 1727277396,481.49609375,25.3 1727277396,481.49609375,34.3 1727277397,481.5078125,28.4 1727277397,481.5078125,26.5 1727277397,481.5078125,27.8 1727277397,481.5078125,31.4 1727277399,481.5078125,28.4 1727277399,481.5078125,21.9 1727277399,481.5078125,30.2 1727277399,481.5078125,27.3 1727277400,481.5078125,28.4 1727277400,481.5078125,21.9 1727277400,481.5078125,30.6 1727277400,481.5078125,23.3 1727277401,481.5078125,28.5 1727277401,481.5078125,29.7 1727277401,481.5078125,30.3 1727277401,481.5078125,21.9 1727277403,481.5078125,28.4 1727277403,481.5078125,36.6 1727277403,481.5078125,28.6 1727277403,481.5078125,22.6 1727277404,481.5078125,28.4 1727277404,481.5078125,35.9 1727277404,481.5078125,28.1 1727277404,481.5078125,31.4 1727277405,481.5078125,28.5 1727277405,481.5078125,21.9 1727277405,481.5078125,31.2 1727277405,481.5078125,25.0 1727277406,481.5078125,28.6 1727277406,481.5078125,21.9 1727277406,481.5078125,30.4 1727277406,481.5078125,21.9 1727277408,481.5078125,28.4 1727277408,481.5078125,28.6 1727277408,481.5078125,28.7 1727277408,481.5078125,28.6 1727277409,481.5078125,28.4 1727277409,481.5078125,25.7 1727277409,481.5078125,29.0 1727277409,481.5078125,29.4 1727277410,481.5078125,28.4 1727277410,481.5078125,23.5 1727277410,481.5078125,29.8 1727277410,481.5078125,24.2 1727277412,481.5078125,28.4 1727277412,481.5078125,24.2 1727277412,481.5078125,29.9 1727277412,481.5078125,22.6 1727277413,481.5078125,28.4 1727277413,481.5078125,21.2 1727277413,481.5078125,28.7 1727277413,481.5078125,25.0 1727277414,481.5078125,28.4 1727277414,481.5078125,32.4 1727277414,481.5078125,28.6 1727277414,481.5078125,28.6 1727277416,481.51171875,28.4 1727277416,481.51171875,21.9 1727277416,481.51171875,30.5 1727277416,481.51171875,25.0 1727277417,481.51171875,28.4 1727277417,481.51171875,24.2 1727277417,481.51171875,30.2 1727277417,481.51171875,22.6 1727277418,481.51171875,28.4 1727277418,481.51171875,33.3 1727277418,481.51171875,27.2 1727277418,481.51171875,29.4 1727277420,481.51171875,28.4 1727277420,481.51171875,21.9 1727277420,481.51171875,30.9 1727277420,481.51171875,23.3 1727277421,481.51171875,29.1 1727277421,481.51171875,39.0 1727277421,481.51171875,34.0 1727277421,481.51171875,27.3 1727277422,481.51171875,31.9 1727277422,481.51171875,29.4 1727277422,481.51171875,32.3 1727277422,481.51171875,25.8 1727277424,481.51171875,29.3 1727277424,481.51171875,25.7 1727277424,481.51171875,25.8 1727277424,481.51171875,35.9 1727277425,481.51171875,26.6 1727277425,481.51171875,18.2 1727277425,481.51171875,27.3 1727277425,481.51171875,19.4 1727277426,481.51171875,25.9 1727277426,481.51171875,26.3 1727277426,481.51171875,26.5 1727277426,481.51171875,18.8 1727277427,481.51171875,25.6 1727277427,481.51171875,28.9 1727277427,481.51171875,26.3 1727277427,481.51171875,19.4 1727277429,481.51171875,25.7 1727277429,481.51171875,22.9 1727277429,481.51171875,27.0 1727277429,481.51171875,19.4 1727277430,481.51171875,25.4 1727277430,481.51171875,31.7 1727277430,481.51171875,25.7 1727277430,481.51171875,18.8 1727277431,481.51171875,23.9 1727277431,481.51171875,17.6 1727277431,481.51171875,22.9 1727277431,481.51171875,32.4 1727277436,482.9296875,22.6 1727277436,482.9296875,20.0 1727277436,482.9296875,23.2 1727277436,482.9296875,16.7 1727277437,482.9296875,22.4 1727277437,482.9296875,15.6 1727277438,482.9296875,23.7 1727277438,482.9296875,16.1 1727277439,482.9296875,22.1 1727277439,482.9296875,15.6 1727277439,482.9296875,22.0 1727277439,482.9296875,26.5 1727277442,482.9765625,23.1 1727277442,482.9765625,17.6 1727277442,482.9765625,27.3 1727277442,482.9765625,19.4 1727277444,483.0078125,25.0 1727277444,483.0078125,25.0 1727277444,483.0078125,20.6 1727277444,483.0078125,31.6 1727277446,483.0078125,25.1 1727277446,483.0078125,22.2 1727277446,483.0078125,22.7 1727277446,483.0078125,36.1 1727277448,483.0078125,24.3 1727277448,483.0078125,25.0 1727277448,483.0078125,20.0 1727277448,483.0078125,32.4 1727277450,483.0078125,22.1 1727277450,483.0078125,17.6 1727277450,483.0078125,22.2 1727277450,483.0078125,21.9 1727277451,483.0078125,22.1 1727277451,483.0078125,17.6 1727277451,483.0078125,23.5 1727277451,483.0078125,18.8 1727277453,483.0078125,22.2 1727277453,483.0078125,28.9 1727277453,483.0078125,21.2 1727277453,483.0078125,25.0 1727277455,483.0078125,22.1 1727277455,483.0078125,30.0 1727277455,483.0078125,20.8 1727277455,483.0078125,20.6 1727277457,483.0078125,22.1 1727277457,483.0078125,15.6 1727277457,483.0078125,23.8 1727277457,483.0078125,23.5 1727277459,483.0625,22.1 1727277459,483.0625,20.0 1727277459,483.0625,22.5 1727277459,483.0625,27.0 1727277461,483.0625,22.1 1727277461,483.0625,20.6 1727277461,483.0625,21.9 1727277461,483.0625,21.9 1727277463,483.0625,22.1 1727277463,483.0625,25.0 1727277463,483.0625,22.5 1727277463,483.0625,25.7 1727277465,483.0625,22.1 1727277465,483.0625,18.2 1727277465,483.0625,22.0 1727277465,483.0625,18.8 1727277466,483.0625,22.1 1727277466,483.0625,17.6 1727277466,483.0625,22.0 1727277466,483.0625,26.5 1727277468,483.0625,22.2 1727277468,483.0625,18.2 1727277468,483.0625,22.8 1727277468,483.0625,18.2 1727277470,483.0625,24.0 1727277470,483.0625,15.2 1727277470,483.0625,29.2 1727277470,483.0625,40.0 1727277472,483.0625,24.9 1727277472,483.0625,31.7 1727277472,483.0625,25.7 1727277472,483.0625,15.6 1727277474,483.0625,24.5 1727277474,483.0625,17.6 1727277474,483.0625,21.0 1727277474,483.0625,21.2 1727277476,483.0625,23.4 1727277476,483.0625,28.2 1727277476,483.0625,20.8 1727277476,483.0625,18.7 1727277478,483.0625,20.6 1727277478,483.0625,25.0 1727277478,483.0625,19.8 1727277478,483.0625,23.5 1727277480,483.0625,20.6 1727277480,483.0625,18.2 1727277480,483.0625,19.8 1727277480,483.0625,18.7 1727277481,483.0625,20.6 1727277481,483.0625,22.4 1727277481,483.0625,20.5 1727277481,483.0625,18.8 1727277483,483.0625,20.6 1727277483,483.0625,15.2 1727277483,483.0625,20.8 1727277483,483.0625,27.8 1727277485,483.0625,19.9 1727277485,483.0625,25.6 1727277485,483.0625,19.1 1727277485,483.0625,13.3 1727277487,483.0625,19.7 1727277487,483.0625,16.7 1727277487,483.0625,20.5 1727277487,483.0625,14.7 1727277489,483.0625,19.2 1727277489,483.0625,14.7 1727277489,483.0625,18.7 1727277489,483.0625,27.0 1727277491,483.0625,21.4 1727277491,483.0625,18.4 1727277491,483.0625,20.7 1727277491,483.0625,21.9 1727277493,483.06640625,22.3 1727277493,483.06640625,15.2 1727277493,483.06640625,19.0 1727277493,483.06640625,26.3 1727277494,483.06640625,22.3 1727277494,483.06640625,14.7 1727277494,483.06640625,20.2 1727277494,483.06640625,32.4 1727277496,483.06640625,19.9 1727277496,483.06640625,14.7 1727277496,483.06640625,18.3 1727277496,483.06640625,17.6 1727277498,483.06640625,19.0 1727277498,483.06640625,16.7 1727277498,483.06640625,19.6 1727277498,483.06640625,18.2 1727277500,483.06640625,19.1 1727277500,483.06640625,17.1 1727277500,483.06640625,20.2 1727277500,483.06640625,24.3 1727277502,483.06640625,20.4 1727277502,483.06640625,17.6 1727277502,483.06640625,24.5 1727277502,483.06640625,26.5 1727277504,483.06640625,22.0 1727277504,483.06640625,23.7 1727277504,483.06640625,22.1 1727277504,483.06640625,19.4 1727277506,483.06640625,21.9 1727277506,483.06640625,15.6 1727277506,483.06640625,19.6 1727277506,483.06640625,27.0 1727277507,483.06640625,21.6 1727277507,483.06640625,11.8 1727277507,483.06640625,20.4 1727277507,483.06640625,25.0 1727277510,483.06640625,19.3 1727277510,483.06640625,19.4 1727277510,483.06640625,24.8 1727277510,483.06640625,21.9 1727277511,483.07421875,22.0 1727277511,483.07421875,25.7 1727277511,483.07421875,23.4 1727277511,483.07421875,34.2 1727277513,483.07421875,22.4 1727277513,483.07421875,35.9 1727277513,483.07421875,23.9 1727277513,483.07421875,16.1 1727277515,483.07421875,22.4 1727277515,483.07421875,15.2 1727277515,483.07421875,21.7 1727277515,483.07421875,21.9 1727277517,483.07421875,19.5 1727277517,483.07421875,21.6 1727277517,483.07421875,18.3 1727277517,483.07421875,16.1 1727277519,483.07421875,19.0 1727277519,483.07421875,15.2 1727277519,483.07421875,18.1 1727277519,483.07421875,24.3 1727277521,483.07421875,19.0 1727277521,483.07421875,21.6 1727277521,483.07421875,19.6 1727277521,483.07421875,16.7 1727277523,483.07421875,19.0 1727277523,483.07421875,23.7 1727277523,483.07421875,18.3 1727277523,483.07421875,18.9 1727277525,483.07421875,19.0 1727277525,483.07421875,14.7 1727277525,483.07421875,20.0 1727277525,483.07421875,22.9 1727277526,483.07421875,19.0 1727277526,483.07421875,16.7 1727277526,483.07421875,20.0 1727277526,483.07421875,15.6 1727277528,483.07421875,19.0 1727277528,483.07421875,20.0 1727277528,483.07421875,19.8 1727277528,483.07421875,15.2 1727277530,483.07421875,19.1 1727277530,483.07421875,23.7 1727277530,483.07421875,18.0 1727277530,483.07421875,20.0 1727277532,483.07421875,22.0 1727277532,483.07421875,35.7 1727277532,483.07421875,22.7 1727277532,483.07421875,35.0 1727277534,483.07421875,25.3 1727277534,483.07421875,15.2 1727277534,483.07421875,18.2 1727277534,483.07421875,34.2 1727277536,483.08203125,24.9 1727277536,483.08203125,29.4 1727277536,483.08203125,27.0 1727277536,483.08203125,35.1 1727277538,483.08203125,23.2 1727277538,483.08203125,15.2 1727277538,483.08203125,18.2 1727277538,483.08203125,25.0 1727277540,483.08203125,19.0 1727277540,483.08203125,23.7 1727277540,483.08203125,19.3 1727277540,483.08203125,18.2 1727277541,483.08203125,19.0 1727277541,483.08203125,24.3 1727277541,483.08203125,19.3 1727277541,483.08203125,18.2 1727277543,483.08203125,19.1 1727277543,483.08203125,23.7 1727277543,483.08203125,17.1 1727277543,483.08203125,25.0 1727277545,483.08203125,19.0 1727277545,483.08203125,18.5 1727277545,483.08203125,19.8 1727277545,483.08203125,15.2 1727277547,483.08203125,19.0 1727277547,483.08203125,21.6 1727277547,483.08203125,17.3 1727277547,483.08203125,25.0 1727277549,483.08203125,19.0 1727277549,483.08203125,14.7 1727277549,483.08203125,19.1 1727277549,483.08203125,18.8 1727277551,483.08203125,19.0 1727277551,483.08203125,23.7 1727277551,483.08203125,17.3 1727277551,483.08203125,18.2 1727277552,483.08203125,19.0 1727277552,483.08203125,11.8 1727277552,483.08203125,20.4 1727277552,483.08203125,20.0 1727277554,483.08203125,19.0 1727277554,483.08203125,22.2 1727277554,483.08203125,20.2 1727277554,483.08203125,15.6 1727277556,483.08203125,19.0 1727277556,483.08203125,21.6 1727277556,483.08203125,17.1 1727277556,483.08203125,22.9 1727277558,483.08203125,19.1 1727277558,483.08203125,15.2 1727277558,483.08203125,17.4 1727277558,483.08203125,24.3 1727277560,483.08203125,19.0 1727277560,483.08203125,14.3 1727277560,483.08203125,18.3 1727277560,483.08203125,27.0 1727277562,483.0859375,20.6 1727277562,483.0859375,18.7 1727277562,483.0859375,26.0 1727277562,483.0859375,15.6 1727277564,483.0859375,24.7 1727277564,483.0859375,23.5 1727277564,483.0859375,25.2 1727277564,483.0859375,18.7 1727277566,483.0859375,24.7 1727277566,483.0859375,22.6 1727277566,483.0859375,33.1 1727277566,483.0859375,16.7 1727277567,483.0859375,25.0 1727277567,483.0859375,32.6 1727277567,483.0859375,27.6 1727277567,483.0859375,36.8 1727277569,483.0859375,22.3 1727277569,483.0859375,28.9 1727277569,483.0859375,19.8 1727277569,483.0859375,16.1 1727277571,483.0859375,30.9 1727277571,483.0859375,30.3 1727277571,483.0859375,40.7 1727277571,483.0859375,30.0 1727277573,483.0859375,37.7 1727277573,483.0859375,29.0 1727277573,483.0859375,38.9 1727277573,483.0859375,30.0 1727277575,483.0859375,37.7 1727277575,483.0859375,45.0 1727277575,483.0859375,39.1 1727277575,483.0859375,29.0 1727277577,483.0859375,37.7 1727277577,483.0859375,36.4 1727277577,483.0859375,37.6 1727277577,483.0859375,47.5 1727277579,483.0859375,37.7 1727277579,483.0859375,46.3 1727277579,483.0859375,34.5 1727277579,483.0859375,48.8 1727277581,483.0859375,37.7 1727277581,483.0859375,31.2 1727277581,483.0859375,38.9 1727277581,483.0859375,32.3 1727277583,483.0859375,37.7 1727277583,483.0859375,31.2 1727277583,483.0859375,38.4 1727277583,483.0859375,46.2 1727277585,483.0859375,37.7 1727277585,483.0859375,42.5 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This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
submitit INFO (2024-09-25 17:15:50,757) - Starting with JobEnvironment(job_id=2198184, hostname=i7182, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2024-09-25 17:15:50,759) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main/runs/ClusteredStatisticalTestDriftDetectionMethod_NOAAWeather/0/single_runs/2198184/2198184_submitted.pkl
Parameters: {"n_samples": 963, "confidence": 0.05, "feature_proportion": 0.09052510857582093, "n_clusters": 1}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 963 confidence 0.05 feature_proportion 0.09052510857582093 n_clusters 1
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate
UID: 2063769
GID: 200270
Status-Change-Time: 1724762786.0
Size: 2165 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727277260.0
Last modification: 1724759185.0
Hostname: i7182
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/unsupervised-concept-drift-detection-main
File: main_omniopt.py
UID: 2063769
GID: 200270
Status-Change-Time: 1727277242.0
Size: 1729 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1727277225.0
Last modification: 1727277242.0
Hostname: i7182
========
DEBUG INFOS END
module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main_omniopt.py NOAAWeather 1000 ClusteredStatisticalTestDriftDetectionMethod n_samples 963 confidence 0.05 feature_proportion 0.09052510857582093 n_clusters 1
stdout:
RESULT: 0.30306201123471743
OO-Info: runtime: 9
OO-Info: lpd: 0.0018530156858425726
Result: 0.30306201123471743
EXIT_CODE: 0
submitit INFO (2024-09-25 17:17:11,119) - Job completed successfully
submitit INFO (2024-09-25 17:17:11,121) - Exiting after successful completion
Job-Infos
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border-left: 8px solid #3498db;
border-radius: 50%;
width: 50px;
height: 50px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
#spinner-overlay {
-webkit-text-stroke: 1px black;
white !important;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
z-index: 9999;
}
#spinner-container {
text-align: center;
color: #fff;
display: contents;
}
#spinner-text {
font-size: 3vw;
margin-left: 10px;
}
a, a:visited, a:active, a:hover, a:link {
color: #007bff;
text-decoration: none;
}
.copy-container {
display: inline-block;
position: relative;
cursor: pointer;
margin-left: 10px;
color: blue;
}
.copy-container:hover {
text-decoration: underline;
}
.clipboard-icon {
position: absolute;
top: 5px;
right: 5px;
font-size: 1.5em;
}
#main_tab {
overflow: scroll;
width: max-content;
}
.ui-tabs .ui-tabs-nav li {
user-select: none;
}
.stacktrace_table {
background-color: black !important;
color: white !important;
}
#breadcrumb {
user-select: none;
}
#statusBar {
user-select: none;
}
.error_line {
background-color: red !important;
color: white !important;
}
.header_table {
border: 0px !important;
padding: 0px !important;
width: revert !important;
min-width: revert !important;
}
.img_auto_width {
max-width: revert !important;
}
#main_dir_or_plot_view {
display: inline-grid;
}
#refresh_button {
width: 300px;
}
._share_link {
color: black !important;
}
#footer_element {
height: 30px;
background-color: #f8f9fa;
padding: 0px;
text-align: center;
border-top: 1px solid #dee2e6;
width: 100%;
box-sizing: border-box;
position: fixed;
bottom: 0;
z-index: 2;
margin-left: -9px;
z-index: 99;
}
.switch {
position: relative;
display: inline-block;
width: 50px;
height: 26px;
}
.switch input {
opacity: 0;
width: 0;
height: 0;
}
.slider {
position: absolute;
cursor: pointer;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: #ccc;
transition: .4s;
border-radius: 26px;
}
.slider:before {
position: absolute;
content: "";
height: 20px;
width: 20px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #444;
}
input:checked + .slider:before {
transform: translateX(24px);
}
.mode-text {
position: absolute;
top: 5px;
left: 65px;
font-size: 14px;
color: black;
transition: .4s;
width: 60px;
display: block;
font-size: 0.7rem;
text-align: center;
}
input:checked + .slider .mode-text {
content: "Dark Mode";
color: white;
}
#mainContent {
height: fit-content;
min-height: 100%;
}
li {
text-align: left;
}
#share_path {
margin-bottom: 20px;
margin-top: 20px;
}
#sortForm {
margin-bottom: 20px;
}
.share_folder_buttons {
margin-top: 10px;
margin-bottom: 10px;
}
.nav_tab_button {
margin: 10px;
}
.header_table {
margin: 10px;
}
.no_border {
border: unset !important;
}
.gui_table {
padding: 5px !important;
}
.gui_parameter_row {
}
.gui_parameter_row_cell {
border: unset !important;
}
.gui_param_table {
width: 95%;
margin: unset !important;
}
table td, table tr,
.parameterRow table {
padding: 2px !important;
}
.parameterRow table {
margin: 0px;
border: unset;
}
.parameterRow > td {
border: 0px !important;
}
.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
border: 0px !important;
}
.green_text {
color: green;
}
.remove_parameter {
white-space: pre;
}
select {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
background-color: #fff;
color: #222;
padding: 5px 30px 5px 5px;
border: 1px solid #555;
border-radius: 5px;
cursor: pointer;
outline: none;
transition: all 0.3s ease;
background:
url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
no-repeat right 10px center,
linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
background-size: 12px, auto;
}
select:hover {
border-color: #888;
}
select:focus {
border-color: #4caf50;
box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
}
select::-ms-expand {
display: none;
}
input, textarea {
border-radius: 5px;
}
#search {
width: 200px;
max-width: 70%;
background-image: url(images/search.svg);
background-repeat: no-repeat;
background-size: auto 40px;
height: 40px;
line-height: 40px;
padding-left: 40px;
box-sizing: border-box;
}
input[type="checkbox"] {
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
width: 25px;
height: 25px;
border: 2px solid #3498db;
border-radius: 5px;
background-color: #fff;
position: relative;
cursor: pointer;
transition: all 0.3s ease;
width: 25px !important;
}
input[type="checkbox"]:checked {
background-color: #3498db;
border-color: #2980b9;
}
input[type="checkbox"]:checked::before {
content: '✔';
position: absolute;
left: 4px;
top: 2px;
color: #fff;
}
input[type="checkbox"]:hover {
border-color: #2980b9;
background-color: #3caffc;
}
.toc {
margin-bottom: 20px;
}
.toc li {
margin-bottom: 5px;
}
.toc a {
text-decoration: none;
color: #007bff;
}
.toc a:hover {
text-decoration: underline;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.section-header {
background-color: #1d6f9a !important;
color: white;
}
.warning {
color: red;
}
.li_list a {
text-decoration: none;
color: #007bff;
}
.gridjs-td {
white-space: nowrap;
}
th, td {
border: 1px solid gray !important;
}
.no_border {
border: 0px !important;
}
.no_break {
}
img {
user-select: none;
pointer-events: none;
}
#config_table, #hidden_config_table {
user-select: none;
}
.copy_clipboard_button {
margin-bottom: 10px;
}
.badge_table {
background-color: unset !important;
}
.make_markable {
user-select: text;
}
.header-container {
display: flex;
flex-wrap: wrap;
align-items: center;
justify-content: space-between;
gap: 1rem;
padding: 10px;
background: var(--header-bg, #fff);
border-bottom: 1px solid #ccc;
}
.header-logo-group {
display: flex;
gap: 1rem;
align-items: center;
flex: 1 1 auto;
min-width: 200px;
}
.logo-img {
max-height: 45px;
height: auto;
width: auto;
object-fit: contain;
pointer-events: unset;
}
.header-badges {
flex-direction: column;
gap: 5px;
align-items: flex-start;
flex: 0 1 auto;
margin-top: auto;
margin-bottom: auto;
}
.badge-img {
height: auto;
max-width: 130px;
}
.header-tabs {
margin-top: 10px;
display: flex;
flex-wrap: wrap;
gap: 10px;
flex: 2 1 100%;
justify-content: center;
}
.nav-tab {
display: inline-block;
text-decoration: none;
padding: 8px 16px;
border-radius: 20px;
background: linear-gradient(to right, #4a90e2, #357ABD);
color: white;
font-weight: bold;
white-space: nowrap;
transition: background 0.2s ease-in-out, transform 0.2s;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.nav-tab:hover {
background: linear-gradient(to right, #5aa0f2, #4a90e2);
transform: translateY(-2px);
}
.current-tag {
padding-left: 10px;
font-size: 0.9rem;
color: #666;
}
.header-theme-toggle {
flex: 1 1 auto;
align-items: center;
margin-top: 20px;
min-width: 120px;
}
.switch {
position: relative;
display: inline-block;
width: 60px;
height: 30px;
}
.switch input {
display: none;
}
.slider {
position: absolute;
top: 0; left: 0; right: 0; bottom: 0;
background-color: #ccc;
border-radius: 34px;
cursor: pointer;
}
.slider::before {
content: "";
position: absolute;
height: 24px;
width: 24px;
left: 3px;
bottom: 3px;
background-color: white;
transition: .4s;
border-radius: 50%;
}
input:checked + .slider {
background-color: #2196F3;
}
input:checked + .slider::before {
transform: translateX(30px);
}
@media (max-width: 768px) {
.header-logo-group,
.header-badges,
.header-theme-toggle {
justify-content: center;
flex: 1 1 100%;
text-align: center;
}
.logo-img {
max-height: 50px;
pointer-events: unset;
}
.badge-img {
max-width: 100px;
}
.nav-tab {
font-size: 0.9rem;
padding: 6px 12px;
}
.header_button {
font-size: 2em;
}
}
.header_button {
margin-top: 20px;
margin: 5px;
}
.line_break_anywhere {
line-break: anywhere;
}
.responsive-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
gap: 20px;
}
.responsive-container .half {
flex: 1 1 48%;
box-sizing: border-box;
min-width: 500px;
}
.config-section table {
width: 100%;
border-collapse: collapse;
}
@media (max-width: 768px) {
.responsive-container .half {
flex: 1 1 100%;
}
}
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
.rotate {
animation: spin 2s linear infinite;
display: inline-block;
}
/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
body{
color: #222
}
.surface{
background: #ece9d8
}
u{
text-decoration: none;
border-bottom: .5px solid #222
}
a{
color: #00f
}
a: focus{
outline: 1px dotted #00f
}
code,code *{
font-family: monospace
}
pre{
display: block;
padding: 12px 8px;
background-color: #000;
color: silver;
font-size: 1rem;
margin: 0;
overflow: scroll;
}
summary: focus{
outline: 1px dotted #000
}
: :-webkit-scrollbar{
width: 16px
}
: :-webkit-scrollbar: horizontal{
height: 17px
}
: :-webkit-scrollbar-track{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-color: #dfdfdf;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
display: block
}
: :-webkit-scrollbar-button: vertical: start{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: end{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: start{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: horizontal: end{
width: 16px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
}
button{
border: none;
background: #ece9d8;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
border-radius: 0;
min-width: 75px;
min-height: 23px;
padding: 0 12px
}
button: not(: disabled).active,button: not(: disabled): active{
box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
}
button.focused,button: focus{
outline: 1px dotted #000;
outline-offset: -4px
}
label{
display: inline-flex;
align-items: center
}
textarea{
padding: 3px 4px;
border: none;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0
}
textarea: focus{
outline: none
}
select: focus option{
color: #000;
background-color: #fff
}
.vertical-bar{
width: 4px;
height: 20px;
background: silver;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
}
&: disabled,&: disabled+label{
color: grey;
text-shadow: 1px 1px 0 #fff
}
input[type=radio]+label{
line-height: 13px;
position: relative;
margin-left: 19px
}
input[type=radio]+label: before{
content: "";
position: absolute;
top: 0;
left: -19px;
display: inline-block;
width: 13px;
height: 13px;
margin-right: 6px;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
}
input[type=radio]: active+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio]: checked+label: after{
content: "";
display: block;
width: 5px;
height: 5px;
top: 5px;
left: -14px;
position: absolute;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=radio][disabled]+label: before{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
}
input[type=radio][disabled]: checked+label: after{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
}
input[type=email],input[type=password]{
padding: 3px 4px;
border: 1px solid #7f9db9;
background-color: #fff;
box-sizing: border-box;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
border-radius: 0;
height: 21px;
line-height: 2
}
input[type=email]: focus,input[type=password]: focus{
outline: none
}
input[type=range]{
-webkit-appearance: none;
width: 100%;
background: transparent
}
input[type=range]: focus{
outline: none
}
input[type=range]: :-webkit-slider-thumb{
-webkit-appearance: none;
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range]: :-webkit-slider-runnable-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range]: :-moz-range-track{
background: #000;
border-right: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
}
input[type=range].has-box-indicator: :-webkit-slider-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
input[type=range].has-box-indicator: :-moz-range-thumb{
background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
}
.is-vertical{
display: inline-block;
width: 4px;
height: 150px;
transform: translateY(50%)
}
.is-vertical>input[type=range]{
width: 150px;
height: 4px;
margin: 0 16px 0 10px;
transform-origin: left;
transform: rotate(270deg) translateX(calc(-50% + 8px))
}
.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-moz-range-track{
border-left: 1px solid grey;
border-bottom: 1px solid grey;
box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
}
.is-vertical>input[type=range]: :-webkit-slider-thumb{
transform: translateY(-8px) scaleX(-1)
}
.is-vertical>input[type=range]: :-moz-range-thumb{
transform: translateY(2px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
transform: translateY(-10px) scaleX(-1)
}
.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
transform: translateY(0) scaleX(-1)
}
.window{
font-size: 11px;
box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
background: #ece9d8;
padding: 3px
}
.window fieldset{
margin-bottom: 9px
}
.title-bar{
background: #000;
padding: 3px 2px 3px 3px;
display: flex;
justify-content: space-between;
align-items: center
}
.title-bar-text{
font-weight: 700;
color: #fff;
letter-spacing: 0;
margin-right: 24px
}
.title-bar-controls button{
padding: 0;
display: block;
min-width: 16px;
min-height: 14px
}
.title-bar-controls button: focus{
outline: none
}
.window-body{
margin: 8px
}
.window-body pre{
margin: -8px
}
.status-bar{
margin: 0 1px;
display: flex;
gap: 1px
}
.status-bar-field{
box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
flex-grow: 1;
padding: 2px 3px;
margin: 0
}
ul.tree-view{
display: block;
background: #fff;
padding: 6px;
margin: 0
}
ul.tree-view li{
list-style-type: none;
margin-top: 3px
}
ul.tree-view a{
text-decoration: none;
color: #000
}
ul.tree-view a: focus{
background-color: #2267cb;
color: #fff
}
ul.tree-view ul{
margin-top: 3px;
margin-left: 16px;
padding-left: 16px;
border-left: 1px dotted grey
}
ul.tree-view ul>li{
position: relative
}
ul.tree-view ul>li: before{
content: "";
display: block;
position: absolute;
left: -16px;
top: 6px;
width: 12px;
border-bottom: 1px dotted grey
}
ul.tree-view ul>li: last-child: after{
content: "";
display: block;
position: absolute;
left: -20px;
top: 7px;
bottom: 0;
width: 8px;
background: #fff
}
ul.tree-view ul details>summary: before{
margin-left: -22px;
position: relative;
z-index: 1
}
ul.tree-view details{
margin-top: 0
}
ul.tree-view details>summary: before{
text-align: center;
display: block;
float: left;
content: "+";
border: 1px solid grey;
width: 8px;
height: 9px;
line-height: 9px;
margin-right: 5px;
padding-left: 1px;
background-color: #fff
}
ul.tree-view details[open] summary{
margin-bottom: 0
}
ul.tree-view details[open]>summary: before{
content: "-"
}
fieldset{
border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
padding: 10px;
padding-block-start: 8px;
margin: 0
}
legend{
background: #ece9d8
}
menu[role=tablist]{
position: relative;
margin: 0 0 -2px;
text-indent: 0;
list-style-type: none;
display: flex;
padding-left: 3px
}
menu[role=tablist] button{
z-index: 1;
display: block;
color: #222;
text-decoration: none;
min-width: unset
}
menu[role=tablist] button[aria-selected=true]{
padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
}
menu[role=tablist] button: focus{
outline: 1px dotted #222;outline-offset: -4px
}
menu[role=tablist].justified button{
flex-grow: 1;text-align: center
}
[role=tabpanel]{
padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
}
: :-webkit-scrollbar{
width: 17px
}
: :-webkit-scrollbar-corner{
background: #dfdfdf
}
: :-webkit-scrollbar-track: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-track: horizontal{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb{
background-position: 50%;
background-repeat: no-repeat;
background-color: #c8d6fb;
background-size: 7px;
border: 1px solid #fff;
border-radius: 2px;
box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
}
: :-webkit-scrollbar-thumb: vertical{
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-thumb: horizontal{
background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
}
: :-webkit-scrollbar-button: vertical: start{
height: 17px;
background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 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}
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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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21.2
],
[
1727279491,
546.12109375,
23
],
[
1727279491,
546.12109375,
24.3
],
[
1727279491,
546.12109375,
21
],
[
1727279491,
546.12109375,
17.6
],
[
1727279803,
554.23046875,
28.5
],
[
1727279803,
554.23046875,
21.1
],
[
1727279803,
554.23046875,
19.1
],
[
1727279803,
554.23046875,
14.7
],
[
1727280088,
553.44921875,
28.1
],
[
1727280088,
553.44921875,
35.4
],
[
1727280088,
553.44921875,
33.5
],
[
1727280088,
553.44921875,
41
],
[
1727280390,
554.87109375,
35
],
[
1727280390,
554.87109375,
37.1
],
[
1727280390,
554.87109375,
34.9
],
[
1727280390,
554.87109375,
30
],
[
1727280725,
558.93359375,
35
],
[
1727280725,
558.93359375,
38.5
],
[
1727280725,
558.93359375,
35.2
],
[
1727280725,
558.93359375,
31.2
],
[
1727281068,
556.09375,
33.8
],
[
1727281068,
556.09375,
27.3
],
[
1727281068,
556.09375,
36.6
],
[
1727281068,
556.09375,
25.8
],
[
1727281445,
529.3515625,
35
],
[
1727281445,
529.3515625,
25.7
],
[
1727281445,
529.3515625,
36.7
],
[
1727281445,
529.3515625,
29
],
[
1727281825,
520.4921875,
34.9
],
[
1727281825,
520.4921875,
27.3
],
[
1727281825,
520.4921875,
34.6
],
[
1727281825,
520.4921875,
42.9
],
[
1727282154,
521.33203125,
34
],
[
1727282154,
521.33203125,
40.5
],
[
1727282154,
521.33203125,
35.2
],
[
1727282154,
521.33203125,
29
],
[
1727282500,
522.37890625,
35
],
[
1727282500,
522.37890625,
27.3
],
[
1727282500,
522.37890625,
34.3
],
[
1727282500,
522.37890625,
45.2
],
[
1727282853,
522.74609375,
34.3
],
[
1727282853,
522.74609375,
38.3
],
[
1727282853,
522.74609375,
33.2
],
[
1727282853,
522.74609375,
45
],
[
1727283240,
523.9296875,
33.9
],
[
1727283240,
523.9296875,
29.4
],
[
1727283240,
523.9296875,
36.1
],
[
1727283240,
523.9296875,
35.3
],
[
1727283643,
535.49609375,
34.9
],
[
1727283643,
535.49609375,
45.2
],
[
1727283643,
535.49609375,
34.1
],
[
1727283643,
535.49609375,
34.3
],
[
1727284042,
529.44140625,
31.2
],
[
1727284042,
529.44140625,
36.8
],
[
1727284042,
529.44140625,
34.6
],
[
1727284042,
529.44140625,
36.1
],
[
1727284436,
561.71484375,
35
],
[
1727284436,
561.71484375,
28.1
],
[
1727284436,
561.71484375,
34.6
],
[
1727284436,
561.71484375,
42.5
],
[
1727284851,
548.63671875,
34.3
],
[
1727284851,
548.63671875,
27.3
],
[
1727284851,
548.63671875,
35.6
],
[
1727284851,
548.63671875,
26.7
],
[
1727285424,
561.6171875,
34.3
],
[
1727285424,
561.6171875,
40.5
],
[
1727285424,
561.6171875,
34.1
],
[
1727285424,
561.6171875,
44.7
],
[
1727285965,
564.18359375,
32.2
],
[
1727285965,
564.18359375,
27.3
],
[
1727285965,
564.18359375,
35
],
[
1727285965,
564.18359375,
36.8
],
[
1727286566,
562.65625,
35.1
],
[
1727286566,
562.65625,
30.3
],
[
1727286566,
562.65625,
34.6
],
[
1727286566,
562.65625,
34.7
],
[
1727287178,
548.63671875,
33.2
],
[
1727287178,
548.63671875,
39.5
],
[
1727287178,
548.63671875,
35.5
],
[
1727287178,
548.63671875,
31.3
],
[
1727287749,
560.37890625,
33.6
],
[
1727287749,
560.37890625,
32.4
],
[
1727287764,
560.39453125,
34.9
],
[
1727287764,
560.39453125,
28.1
]
];
var tab_main_worker_cpu_ram_headers_json = [
"timestamp",
"ram_usage_mb",
"cpu_usage_percent"
];
"use strict";
function add_default_layout_data (layout) {
layout["width"] = get_graph_width();
layout["height"] = get_graph_height();
layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
return layout;
}
function get_marker_size() {
return 12;
}
function get_text_color() {
return theme == "dark" ? "white" : "black";
}
function get_font_size() {
return 14;
}
function get_graph_height() {
return 800;
}
function get_font_data() {
return {
size: get_font_size(),
color: get_text_color()
}
}
function get_axis_title_data(name, axis_type = "") {
if(axis_type) {
return {
text: name,
type: axis_type,
font: get_font_data()
};
}
return {
text: name,
font: get_font_data()
};
}
function get_graph_width() {
var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
return Math.max(800, Math.floor(width * 0.9));
}
function createTable(data, headers, table_name) {
if (!$("#" + table_name).length) {
console.error("#" + table_name + " not found");
return;
}
new gridjs.Grid({
columns: headers,
data: data,
search: true,
sort: true
}).render(document.getElementById(table_name));
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
colorize_table_entries();
add_colorize_to_gridjs_table();
}
function download_as_file(id, filename) {
var text = $("#" + id).text();
var blob = new Blob([text], {
type: "text/plain"
});
var link = document.createElement("a");
link.href = URL.createObjectURL(blob);
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
function copy_to_clipboard_from_id (id) {
var text = $("#" + id).text();
copy_to_clipboard(text);
}
function copy_to_clipboard(text) {
if (!navigator.clipboard) {
let textarea = document.createElement("textarea");
textarea.value = text;
document.body.appendChild(textarea);
textarea.select();
try {
document.execCommand("copy");
} catch (err) {
console.error("Copy failed:", err);
}
document.body.removeChild(textarea);
return;
}
navigator.clipboard.writeText(text).then(() => {
console.log("Text copied to clipboard");
}).catch(err => {
console.error("Failed to copy text:", err);
});
}
function filterNonEmptyRows(data) {
var new_data = [];
for (var row_idx = 0; row_idx < data.length; row_idx++) {
var line = data[row_idx];
var line_has_empty_data = false;
for (var col_idx = 0; col_idx < line.length; col_idx++) {
var col_header_name = tab_results_headers_json[col_idx];
var single_data_point = line[col_idx];
if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
line_has_empty_data = true;
continue;
}
}
if(!line_has_empty_data) {
new_data.push(line);
}
}
return new_data;
}
function make_text_in_parallel_plot_nicer() {
$(".parcoords g > g > text").each(function() {
if (theme == "dark") {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "white")
.css("stroke", "black")
.css("stroke-width", "2px")
.css("paint-order", "stroke fill");
} else {
$(this)
.css("text-shadow", "unset")
.css("font-size", "0.9em")
.css("fill", "black")
.css("stroke", "unset")
.css("stroke-width", "unset")
.css("paint-order", "stroke fill");
}
});
}
function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
if ($("#parallel-plot").data("loaded") == "true") {
return;
}
dataArray = filterNonEmptyRows(dataArray);
const ignoreSet = new Set(ignoreColumns);
const numericalCols = [];
const categoricalCols = [];
const categoryMappings = {};
headers.forEach((header, colIndex) => {
if (ignoreSet.has(header)) return;
const values = dataArray.map(row => row[colIndex]);
if (values.every(val => !isNaN(parseFloat(val)))) {
numericalCols.push({ name: header, index: colIndex });
} else {
categoricalCols.push({ name: header, index: colIndex });
const uniqueValues = [...new Set(values)];
categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
}
});
const dimensions = [];
numericalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => parseFloat(row[col.index])),
range: [
Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
Math.max(...dataArray.map(row => parseFloat(row[col.index])))
]
});
});
categoricalCols.forEach(col => {
dimensions.push({
label: col.name,
values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
tickvals: Object.values(categoryMappings[col.name]),
ticktext: Object.keys(categoryMappings[col.name])
});
});
let colorScale = null;
let colorValues = null;
if (resultNames.length > 1) {
let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
selectBox += '<option value="none">No color</option>';
var k = 0;
resultNames.forEach(resultName => {
var minMax = result_min_max[k];
if(minMax === undefined) {
minMax = "min [automatically chosen]"
}
selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
k = k + 1;
});
selectBox += '</select>';
$("#parallel-plot").before(selectBox);
$("#result-select").change(function() {
const selectedResult = $(this).val();
if (selectedResult === "none") {
colorValues = null;
colorScale = null;
} else {
const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
let minResult = Math.min(...colorValues);
let maxResult = Math.max(...colorValues);
var _result_min_max_idx = result_names.indexOf(selectedResult);
let invertColor = false;
if (result_min_max.length > _result_min_max_idx) {
invertColor = result_min_max[_result_min_max_idx] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
}
updatePlot();
});
} else {
let invertColor = false;
if (Object.keys(result_min_max).length == 1) {
invertColor = result_min_max[0] === "max";
}
colorScale = invertColor
? [[0, 'red'], [1, 'green']]
: [[0, 'green'], [1, 'red']];
const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
}
function updatePlot() {
const trace = {
type: 'parcoords',
dimensions: dimensions,
line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
unselected: {
line: {
color: get_text_color(),
opacity: 0
}
},
};
dimensions.forEach(dim => {
if (!dim.line) {
dim.line = {};
}
if (!dim.line.color) {
dim.line.color = 'rgba(169,169,169, 0.01)';
}
});
Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
make_text_in_parallel_plot_nicer();
}
updatePlot();
$("#parallel-plot").data("loaded", "true");
make_text_in_parallel_plot_nicer();
}
function plotWorkerUsage() {
if($("#workerUsagePlot").data("loaded") == "true") {
return;
}
var data = tab_worker_usage_csv_json;
if (!Array.isArray(data) || data.length === 0) {
console.error("Invalid or empty data provided.");
return;
}
let timestamps = [];
let desiredWorkers = [];
let realWorkers = [];
for (let i = 0; i < data.length; i++) {
let entry = data[i];
if (!Array.isArray(entry) || entry.length < 3) {
console.warn("Skipping invalid entry:", entry);
continue;
}
let unixTime = parseFloat(entry[0]);
let desired = parseInt(entry[1], 10);
let real = parseInt(entry[2], 10);
if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
console.warn("Skipping invalid numerical values:", entry);
continue;
}
timestamps.push(new Date(unixTime * 1000).toISOString());
desiredWorkers.push(desired);
realWorkers.push(real);
}
let trace1 = {
x: timestamps,
y: desiredWorkers,
mode: 'lines+markers',
name: 'Desired Workers',
line: {
color: 'blue'
}
};
let trace2 = {
x: timestamps,
y: realWorkers,
mode: 'lines+markers',
name: 'Real Workers',
line: {
color: 'red'
}
};
let layout = {
title: "Worker Usage Over Time",
xaxis: {
title: get_axis_title_data("Time", "date")
},
yaxis: {
title: get_axis_title_data("Number of Workers")
},
legend: {
x: 0,
y: 1
}
};
Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
$("#workerUsagePlot").data("loaded", "true");
}
function plotCPUAndRAMUsage() {
if($("#mainWorkerCPURAM").data("loaded") == "true") {
return;
}
var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
var trace1 = {
x: timestamps,
y: cpuUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'CPU Usage (%)',
type: 'scatter',
yaxis: 'y1'
};
var trace2 = {
x: timestamps,
y: ramUsage,
mode: 'lines+markers',
marker: {
size: get_marker_size(),
},
name: 'RAM Usage (MB)',
type: 'scatter',
yaxis: 'y2'
};
var layout = {
title: 'CPU and RAM Usage Over Time',
xaxis: {
title: get_axis_title_data("Timestamp", "date"),
tickmode: 'array',
tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
tickangle: -45
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
rangemode: 'tozero'
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
overlaying: 'y',
side: 'right',
rangemode: 'tozero'
},
legend: {
x: 0.1,
y: 0.9
}
};
var data = [trace1, trace2];
Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
$("#mainWorkerCPURAM").data("loaded", "true");
}
function plotScatter2d() {
if ($("#plotScatter2d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter2d");
var minInput = document.getElementById("minValue");
var maxInput = document.getElementById("maxValue");
if (!minInput || !maxInput) {
minInput = document.createElement("input");
minInput.id = "minValue";
minInput.type = "number";
minInput.placeholder = "Min Value";
minInput.step = "any";
maxInput = document.createElement("input");
maxInput.id = "maxValue";
maxInput.type = "number";
maxInput.placeholder = "Max Value";
maxInput.step = "any";
var inputContainer = document.createElement("div");
inputContainer.style.marginBottom = "10px";
inputContainer.appendChild(minInput);
inputContainer.appendChild(maxInput);
plotDiv.appendChild(inputContainer);
}
var resultSelect = document.getElementById("resultSelect");
if (result_names.length > 1 && !resultSelect) {
resultSelect = document.createElement("select");
resultSelect.id = "resultSelect";
resultSelect.style.marginBottom = "10px";
var sortedResults = [...result_names].sort();
sortedResults.forEach(result => {
var option = document.createElement("option");
option.value = result;
option.textContent = result;
resultSelect.appendChild(option);
});
var selectContainer = document.createElement("div");
selectContainer.style.marginBottom = "10px";
selectContainer.appendChild(resultSelect);
plotDiv.appendChild(selectContainer);
}
minInput.addEventListener("input", updatePlots);
maxInput.addEventListener("input", updatePlots);
if (resultSelect) {
resultSelect.addEventListener("change", updatePlots);
}
updatePlots();
async function updatePlots() {
var minValue = parseFloat(minInput.value);
var maxValue = parseFloat(maxInput.value);
if (isNaN(minValue)) minValue = -Infinity;
if (isNaN(maxValue)) maxValue = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var selectedResult = resultSelect ? resultSelect.value : result_names[0];
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 2) {
console.error("Not enough columns for Scatter-Plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
showlegend: false
};
let subDiv = document.createElement("div");
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
symbol: data.map(d => d.result === null ? 'x' : 'circle'),
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter',
showlegend: false
};
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
$("#plotScatter2d").data("loaded", "true");
}
function plotScatter3d() {
if ($("#plotScatter3d").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotScatter3d");
if (!plotDiv) {
console.error("Div element with id 'plotScatter3d' not found");
return;
}
plotDiv.innerHTML = "";
var minInput3d = document.getElementById("minValue3d");
var maxInput3d = document.getElementById("maxValue3d");
if (!minInput3d || !maxInput3d) {
minInput3d = document.createElement("input");
minInput3d.id = "minValue3d";
minInput3d.type = "number";
minInput3d.placeholder = "Min Value";
minInput3d.step = "any";
maxInput3d = document.createElement("input");
maxInput3d.id = "maxValue3d";
maxInput3d.type = "number";
maxInput3d.placeholder = "Max Value";
maxInput3d.step = "any";
var inputContainer3d = document.createElement("div");
inputContainer3d.style.marginBottom = "10px";
inputContainer3d.appendChild(minInput3d);
inputContainer3d.appendChild(maxInput3d);
plotDiv.appendChild(inputContainer3d);
}
var select3d = document.getElementById("select3dScatter");
if (result_names.length > 1 && !select3d) {
if (!select3d) {
select3d = document.createElement("select");
select3d.id = "select3dScatter";
select3d.style.marginBottom = "10px";
select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
select3d.addEventListener("change", updatePlots3d);
plotDiv.appendChild(select3d);
}
}
minInput3d.addEventListener("input", updatePlots3d);
maxInput3d.addEventListener("input", updatePlots3d);
updatePlots3d();
async function updatePlots3d() {
var selectedResult = select3d ? select3d.value : result_names[0];
var minValue3d = parseFloat(minInput3d.value);
var maxValue3d = parseFloat(maxInput3d.value);
if (isNaN(minValue3d)) minValue3d = -Infinity;
if (isNaN(maxValue3d)) maxValue3d = Infinity;
while (plotDiv.children.length > 2) {
plotDiv.removeChild(plotDiv.lastChild);
}
var resultIndex = tab_results_headers_json.findIndex(header =>
header.toLowerCase() === selectedResult.toLowerCase()
);
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 3) {
console.error("Not enough columns for 3D scatter plots");
return;
}
for (let i = 0; i < numericColumns.length; i++) {
for (let j = i + 1; j < numericColumns.length; j++) {
for (let k = j + 1; k < numericColumns.length; k++) {
let xCol = numericColumns[i];
let yCol = numericColumns[j];
let zCol = numericColumns[k];
let xIndex = tab_results_headers_json.indexOf(xCol);
let yIndex = tab_results_headers_json.indexOf(yCol);
let zIndex = tab_results_headers_json.indexOf(zCol);
let data = tab_results_csv_json.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
z: parseFloat(row[zIndex]),
result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
}));
data = data.filter(d => d.result >= minResult && d.result <= maxResult);
let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
let layout = {
title: layoutTitle,
scene: {
xaxis: {
title: get_axis_title_data(xCol)
},
yaxis: {
title: get_axis_title_data(yCol)
},
zaxis: {
title: get_axis_title_data(zCol)
}
},
showlegend: false
};
let spinnerContainer = document.createElement("div");
spinnerContainer.style.display = "flex";
spinnerContainer.style.alignItems = "center";
spinnerContainer.style.justifyContent = "center";
spinnerContainer.style.width = layout.width + "px";
spinnerContainer.style.height = layout.height + "px";
spinnerContainer.style.position = "relative";
let spinner = document.createElement("div");
spinner.className = "spinner";
spinner.style.width = "40px";
spinner.style.height = "40px";
let loadingText = document.createElement("span");
loadingText.innerText = `Loading ${layoutTitle}`;
loadingText.style.marginLeft = "10px";
spinnerContainer.appendChild(spinner);
spinnerContainer.appendChild(loadingText);
plotDiv.appendChild(spinnerContainer);
await new Promise(resolve => setTimeout(resolve, 50));
let colors = data.map(d => {
if (d.result === null) {
return 'rgb(0, 0, 0)';
} else {
let norm = (d.result - minResult) / (maxResult - minResult);
if (invertColor) {
norm = 1 - norm;
}
return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
}
});
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
z: data.map(d => d.z),
mode: 'markers',
marker: {
size: get_marker_size(),
color: data.map(d => d.result !== null ? d.result : null),
colorscale: invertColor ? [
[0, 'red'],
[1, 'green']
] : [
[0, 'green'],
[1, 'red']
],
colorbar: {
title: 'Result',
tickvals: [minResult, maxResult],
ticktext: [`${minResult}`, `${maxResult}`]
},
},
text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
type: 'scatter3d',
showlegend: false
};
let subDiv = document.createElement("div");
try {
plotDiv.replaceChild(subDiv, spinnerContainer);
} catch (err) {
//
}
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
}
}
$("#plotScatter3d").data("loaded", "true");
}
async function load_pareto_graph() {
if($("#tab_pareto_fronts").data("loaded") == "true") {
return;
}
var data = pareto_front_data;
if (!data || typeof data !== "object") {
console.error("Invalid data format for pareto_front_data");
return;
}
if (!Object.keys(data).length) {
console.warn("No data found in pareto_front_data");
return;
}
let categories = Object.keys(data);
let allMetrics = new Set();
function extractMetrics(obj, prefix = "") {
let keys = Object.keys(obj);
for (let key of keys) {
let newPrefix = prefix ? `${prefix} -> ${key}` : key;
if (typeof obj[key] === "object" && !Array.isArray(obj[key])) {
extractMetrics(obj[key], newPrefix);
} else {
if (!newPrefix.includes("param_dicts") && !newPrefix.includes(" -> sems -> ") && !newPrefix.includes("absolute_metrics")) {
allMetrics.add(newPrefix);
}
}
}
}
for (let cat of categories) {
extractMetrics(data[cat]);
}
allMetrics = Array.from(allMetrics);
function extractValues(obj, metricPath, values) {
let parts = metricPath.split(" -> ");
let data = obj;
for (let part of parts) {
if (data && typeof data === "object") {
data = data[part];
} else {
return;
}
}
if (Array.isArray(data)) {
values.push(...data);
}
}
let graphContainer = document.getElementById("pareto_front_graphs_container");
graphContainer.classList.add("invert_in_dark_mode");
graphContainer.innerHTML = "";
var already_plotted = [];
for (let i = 0; i < allMetrics.length; i++) {
for (let j = i + 1; j < allMetrics.length; j++) {
let xMetric = allMetrics[i];
let yMetric = allMetrics[j];
let xValues = [];
let yValues = [];
for (let cat of categories) {
let metricData = data[cat];
extractValues(metricData, xMetric, xValues);
extractValues(metricData, yMetric, yValues);
}
xValues = xValues.filter(v => v !== undefined && v !== null);
yValues = yValues.filter(v => v !== undefined && v !== null);
let cleanXMetric = xMetric.replace(/.* -> /g, "");
let cleanYMetric = yMetric.replace(/.* -> /g, "");
let plot_key = `${cleanXMetric}-${cleanYMetric}`;
if (xValues.length > 0 && yValues.length > 0 && xValues.length === yValues.length && !already_plotted.includes(plot_key)) {
let div = document.createElement("div");
div.id = `pareto_front_graph_${i}_${j}`;
div.style.marginBottom = "20px";
graphContainer.appendChild(div);
let layout = {
title: `${cleanXMetric} vs ${cleanYMetric}`,
xaxis: {
title: get_axis_title_data(cleanXMetric)
},
yaxis: {
title: get_axis_title_data(cleanYMetric)
},
hovermode: "closest"
};
let trace = {
x: xValues,
y: yValues,
mode: "markers",
marker: {
size: get_marker_size(),
},
type: "scatter",
name: `${cleanXMetric} vs ${cleanYMetric}`
};
Plotly.newPlot(div.id, [trace], add_default_layout_data(layout));
already_plotted.push(plot_key);
}
}
}
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
$("#tab_pareto_fronts").data("loaded", "true");
}
async function plot_worker_cpu_ram() {
if($("#worker_cpu_ram_pre").data("loaded") == "true") {
return;
}
const logData = $("#worker_cpu_ram_pre").text();
const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
const hostData = {};
logData.split("\n").forEach(line => {
line = line.trim();
const match = line.match(regex);
if (match) {
const timestamp = new Date(parseInt(match[1]) * 1000);
const hostname = match[2];
const cpu = parseFloat(match[3]);
const ram = parseFloat(match[4]);
if (!hostData[hostname]) {
hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
}
hostData[hostname].timestamps.push(timestamp);
hostData[hostname].cpuUsage.push(cpu);
hostData[hostname].ramUsage.push(ram);
}
});
if (!Object.keys(hostData).length) {
console.log("No valid data found");
return;
}
const container = document.getElementById("cpuRamWorkerChartContainer");
container.innerHTML = "";
var i = 1;
Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
const chartId = `workerChart_${index}`;
const chartDiv = document.createElement("div");
chartDiv.id = chartId;
chartDiv.style.marginBottom = "40px";
container.appendChild(chartDiv);
const cpuTrace = {
x: timestamps,
y: cpuUsage,
mode: "lines+markers",
name: "CPU Usage (%)",
yaxis: "y1",
line: {
color: "red"
}
};
const ramTrace = {
x: timestamps,
y: ramUsage,
mode: "lines+markers",
name: "RAM Usage (MB)",
yaxis: "y2",
line: {
color: "blue"
}
};
const layout = {
title: `Worker CPU and RAM Usage - ${hostname}`,
xaxis: {
title: get_axis_title_data("Timestamp", "date")
},
yaxis: {
title: get_axis_title_data("CPU Usage (%)"),
side: "left",
color: "red"
},
yaxis2: {
title: get_axis_title_data("RAM Usage (MB)"),
side: "right",
overlaying: "y",
color: "blue"
},
showlegend: true
};
Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
i++;
});
$("#plot_worker_cpu_ram_button").remove();
$("#worker_cpu_ram_pre").data("loaded", "true");
}
function load_log_file(log_nr, filename) {
var pre_id = `single_run_${log_nr}_pre`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
fetch(url)
.then(response => response.json())
.then(data => {
if (data.data) {
$("#" + pre_id).html(data.data);
$("#" + pre_id).data("loaded", true);
} else {
log(`No 'data' key found in response.`);
}
$("#spinner_log_" + log_nr).remove();
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#spinner_log_" + log_nr).remove();
});
}
}
function load_debug_log () {
var pre_id = `here_debuglogs_go`;
if (!$("#" + pre_id).data("loaded")) {
const params = new URLSearchParams(window.location.search);
const user_id = params.get('user_id');
const experiment_name = params.get('experiment_name');
const run_nr = params.get('run_nr');
var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
fetch(url)
.then(response => response.json())
.then(data => {
$("#debug_log_spinner").remove();
if (data.data) {
try {
$("#" + pre_id).html(data.data);
} catch (err) {
$("#" + pre_id).text(`Error loading data: ${err}`);
}
$("#" + pre_id).data("loaded", true);
if (typeof apply_theme_based_on_system_preferences === 'function') {
apply_theme_based_on_system_preferences();
}
} else {
log(`No 'data' key found in response.`);
}
})
.catch(error => {
log(`Error loading log: ${error}`);
$("#debug_log_spinner").remove();
});
}
}
function plotBoxplot() {
if ($("#plotBoxplot").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough numeric columns for Boxplot");
return;
}
var resultIndex = tab_results_headers_json.findIndex(function(header) {
return result_names.includes(header.toLowerCase());
});
var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
var plotDiv = document.getElementById("plotBoxplot");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'box',
name: col,
boxmean: 'sd',
marker: {
color: 'rgb(0, 255, 0)'
},
};
});
let layout = {
title: 'Boxplot of Numerical Columns',
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Value")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotBoxplot").data("loaded", "true");
}
function plotHeatmap() {
if ($("#plotHeatmap").data("loaded") === "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col => {
if (special_col_names.includes(col) || result_names.includes(col)) {
return false;
}
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.every(row => {
let value = parseFloat(row[index]);
return !isNaN(value) && isFinite(value);
});
});
if (numericColumns.length < 2) {
console.error("Not enough valid numeric columns for Heatmap");
return;
}
var columnData = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
return tab_results_csv_json.map(row => parseFloat(row[index]));
});
var dataMatrix = numericColumns.map((_, i) =>
numericColumns.map((_, j) => {
let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
return values.reduce((a, b) => a + b, 0) / values.length;
})
);
var trace = {
z: dataMatrix,
x: numericColumns,
y: numericColumns,
colorscale: 'Viridis',
type: 'heatmap'
};
var layout = {
xaxis: {
title: get_axis_title_data("Columns")
},
yaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
var plotDiv = document.getElementById("plotHeatmap");
plotDiv.innerHTML = "";
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotHeatmap").data("loaded", "true");
}
function plotHistogram() {
if ($("#plotHistogram").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Histogram");
return;
}
var plotDiv = document.getElementById("plotHistogram");
plotDiv.innerHTML = "";
const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
let traces = numericColumns.map((col, index) => {
let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
return {
x: data,
type: 'histogram',
name: col,
opacity: 0.7,
marker: {
color: colorPalette[index % colorPalette.length]
},
autobinx: true
};
});
let layout = {
title: 'Histogram of Numerical Columns',
xaxis: {
title: get_axis_title_data("Value")
},
yaxis: {
title: get_axis_title_data("Frequency")
},
showlegend: true,
barmode: 'overlay'
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotHistogram").data("loaded", "true");
}
function plotViolin() {
if ($("#plotViolin").data("loaded") == "true") {
return;
}
var numericColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !result_names.includes(col) &&
tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
);
if (numericColumns.length < 1) {
console.error("Not enough columns for Violin Plot");
return;
}
var plotDiv = document.getElementById("plotViolin");
plotDiv.innerHTML = "";
let traces = numericColumns.map(col => {
let index = tab_results_headers_json.indexOf(col);
let data = tab_results_csv_json.map(row => parseFloat(row[index]));
return {
y: data,
type: 'violin',
name: col,
box: {
visible: true
},
line: {
color: 'rgb(0, 255, 0)'
},
marker: {
color: 'rgb(0, 255, 0)'
},
meanline: {
visible: true
},
};
});
let layout = {
title: 'Violin Plot of Numerical Columns',
yaxis: {
title: get_axis_title_data("Value")
},
xaxis: {
title: get_axis_title_data("Columns")
},
showlegend: false
};
Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
$("#plotViolin").data("loaded", "true");
}
function plotExitCodesPieChart() {
if ($("#plotExitCodesPieChart").data("loaded") == "true") {
return;
}
var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
counts[exitCode] = (counts[exitCode] || 0) + 1;
return counts;
}, {});
var labels = Object.keys(exitCodeCounts);
var values = Object.values(exitCodeCounts);
var plotDiv = document.getElementById("plotExitCodesPieChart");
plotDiv.innerHTML = "";
var trace = {
labels: labels,
values: values,
type: 'pie',
hoverinfo: 'label+percent',
textinfo: 'label+value',
marker: {
colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
}
};
var layout = {
title: 'Exit Code Distribution',
showlegend: true
};
Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
$("#plotExitCodesPieChart").data("loaded", "true");
}
function plotResultEvolution() {
if ($("#plotResultEvolution").data("loaded") == "true") {
return;
}
result_names.forEach(resultName => {
var relevantColumns = tab_results_headers_json.filter(col =>
!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
);
var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
var resultIndex = tab_results_headers_json.indexOf(resultName);
let data = tab_results_csv_json.map(row => ({
x: row[xColumnIndex],
y: parseFloat(row[resultIndex])
}));
data.sort((a, b) => a.x - b.x);
let xData = data.map(item => item.x);
let yData = data.map(item => item.y);
let trace = {
x: xData,
y: yData,
mode: 'lines+markers',
name: resultName,
line: {
shape: 'linear'
},
marker: {
size: get_marker_size()
}
};
let layout = {
title: `Evolution of ${resultName} over time`,
xaxis: {
title: get_axis_title_data("Trial-Index")
},
yaxis: {
title: get_axis_title_data(resultName)
},
showlegend: true
};
let subDiv = document.createElement("div");
document.getElementById("plotResultEvolution").appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
});
$("#plotResultEvolution").data("loaded", "true");
}
function plotResultPairs() {
if ($("#plotResultPairs").data("loaded") == "true") {
return;
}
var plotDiv = document.getElementById("plotResultPairs");
plotDiv.innerHTML = "";
for (let i = 0; i < result_names.length; i++) {
for (let j = i + 1; j < result_names.length; j++) {
let xName = result_names[i];
let yName = result_names[j];
let xIndex = tab_results_headers_json.indexOf(xName);
let yIndex = tab_results_headers_json.indexOf(yName);
let data = tab_results_csv_json
.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
.map(row => ({
x: parseFloat(row[xIndex]),
y: parseFloat(row[yIndex]),
status: row[tab_results_headers_json.indexOf("trial_status")]
}));
let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
let trace = {
x: data.map(d => d.x),
y: data.map(d => d.y),
mode: 'markers',
marker: {
size: get_marker_size(),
color: colors
},
text: data.map(d => `Status: ${d.status}`),
type: 'scatter',
showlegend: false
};
let layout = {
xaxis: {
title: get_axis_title_data(xName)
},
yaxis: {
title: get_axis_title_data(yName)
},
showlegend: false
};
let subDiv = document.createElement("div");
plotDiv.appendChild(subDiv);
Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
}
}
$("#plotResultPairs").data("loaded", "true");
}
function add_up_down_arrows_for_scrolling () {
const upArrow = document.createElement('div');
const downArrow = document.createElement('div');
const style = document.createElement('style');
style.innerHTML = `
.scroll-arrow {
position: fixed;
right: 10px;
z-index: 100;
cursor: pointer;
font-size: 25px;
display: none;
background-color: green;
color: white;
padding: 5px;
outline: 2px solid white;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
transition: background-color 0.3s, transform 0.3s;
}
.scroll-arrow:hover {
background-color: darkgreen;
transform: scale(1.1);
}
#up-arrow {
top: 10px;
}
#down-arrow {
bottom: 10px;
}
`;
document.head.appendChild(style);
upArrow.id = "up-arrow";
upArrow.classList.add("scroll-arrow");
upArrow.classList.add("invert_in_dark_mode");
upArrow.innerHTML = "↑";
downArrow.id = "down-arrow";
downArrow.classList.add("scroll-arrow");
downArrow.classList.add("invert_in_dark_mode");
downArrow.innerHTML = "↓";
document.body.appendChild(upArrow);
document.body.appendChild(downArrow);
function checkScrollPosition() {
const scrollPosition = window.scrollY;
const pageHeight = document.documentElement.scrollHeight;
const windowHeight = window.innerHeight;
if (scrollPosition > 0) {
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var colors = statuses.map((status, i) =>
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plotCPUAndRAMUsage();;
createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
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</script>
<h1> Overview</h1>
<h2>Best parameter (total: 0, failed: 287): </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>0.1</td><td>0.034026</td><td>2</td><td>0.273213 </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>287</td>
<td>211</td>
<td>9</td>
<td>507</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,0.303062011234717432195395758754,963,0.050000000000000002775557561563,0.090525108575820925627120061563,1
1,1_0,COMPLETED,Sobol,0.288688181517788300389781852573,410,0.250000000000000000000000000000,0.167912307754158995898308148753,1
2,2_0,COMPLETED,Sobol,0.288963542240334825272896068782,519,0.250000000000000000000000000000,0.104168479330837726593017578125,4
3,3_0,COMPLETED,Sobol,0.292047582332855992781617260334,619,0.250000000000000000000000000000,0.022104877047240734100341796875,4
4,4_0,COMPLETED,Sobol,0.298766383962991488587590538373,654,0.100000000000000005551115123126,0.195973979309201240539550781250,3
5,5_0,COMPLETED,Sobol,0.282795462055292379233151223161,112,0.025000000000000001387778780781,0.124943611398339274320967717813,4
6,6_0,COMPLETED,Sobol,0.279766494107280494496592382347,178,0.001000000000000000020816681712,0.058544055186212064223472140156,2
7,7_0,COMPLETED,Sobol,0.294415684546756262207622967253,992,0.010000000000000000208166817117,0.016774291545152666266238483672,3
8,8_0,COMPLETED,Sobol,0.282134596321180763922598089266,210,0.005000000000000000104083408559,0.161182905361056338922054465002,2
9,9_0,COMPLETED,Sobol,0.293589602379116687558280318626,880,0.025000000000000001387778780781,0.016868462413549424605552218281,4
10,10_0,COMPLETED,Sobol,0.299262033263575255581656620052,810,0.001000000000000000020816681712,0.194715055823326127493189119377,4
11,11_0,COMPLETED,Sobol,0.285438924991739173542271146289,296,0.250000000000000000000000000000,0.134585285000503068753019420001,1
12,12_0,COMPLETED,Sobol,0.295627271725961038306706996082,651,0.005000000000000000104083408559,0.027049892954528333838259968047,2
13,13_0,COMPLETED,Sobol,0.296288137460072653617260129977,860,0.250000000000000000000000000000,0.148602385446429258175626841876,2
14,14_0,COMPLETED,Sobol,0.294140323824209737324508751044,529,0.250000000000000000000000000000,0.112633097544312485438489090939,4
15,15_0,COMPLETED,Sobol,0.294030179535191060757881587051,998,0.005000000000000000104083408559,0.043478224985301495986167452656,3
16,16_0,COMPLETED,Sobol,0.290340345853067560710769612342,835,0.050000000000000002775557561563,0.098502206616103649139404296875,4
17,17_0,COMPLETED,Sobol,0.296783786760656420611326211656,325,0.001000000000000000020816681712,0.144428371451795101165771484375,1
18,18_0,COMPLETED,Sobol,0.292267870910893234892569125805,798,0.100000000000000005551115123126,0.066104659624397751893631891562,3
19,19_0,COMPLETED,Sobol,0.297334508205749581399857106589,653,0.250000000000000000000000000000,0.191614809818565851040617076251,3
20,20_0,COMPLETED,BoTorch,0.282575173477255248144501820207,130,0.005000000000000000104083408559,0.097383231306390108383830295224,3
21,21_0,COMPLETED,BoTorch,0.281473730587069037589742492855,127,0.010000000000000000208166817117,0.098496977213928568750667125187,2
22,22_0,COMPLETED,BoTorch,0.282354884899218006033549954736,200,0.025000000000000001387778780781,0.083681939960258869271036985538,3
23,23_0,COMPLETED,BoTorch,0.286265007159378748191613794916,100,0.001000000000000000020816681712,0.113182872816724233722140979808,3
24,24_0,COMPLETED,BoTorch,0.279325916951206121296991113923,114,0.025000000000000001387778780781,0.168159959532115599323276455834,3
25,25_0,COMPLETED,BoTorch,0.285438924991739173542271146289,100,0.005000000000000000104083408559,0.028910586306439074039120740167,3
26,26_0,COMPLETED,BoTorch,0.293314241656570051652863639902,215,0.001000000000000000020816681712,0.081342478114249897047649540127,3
27,27_0,COMPLETED,BoTorch,0.280537504130410786373772680236,100,0.010000000000000000208166817117,0.040441863507982622749636902881,2
28,28_0,COMPLETED,BoTorch,0.282685317766273813688826521684,120,0.050000000000000002775557561563,0.124401711174049953156917069919,2
29,29_0,COMPLETED,BoTorch,0.285053419980174083114832228603,100,0.001000000000000000020816681712,0.106279604584790132726368483418,2
30,30_0,COMPLETED,BoTorch,0.286265007159378748191613794916,235,0.010000000000000000208166817117,0.049882784523490091010966551721,2
31,31_0,COMPLETED,BoTorch,0.282024452032162087355970925273,104,0.005000000000000000104083408559,0.194003465969583455130731408644,3
32,32_0,COMPLETED,BoTorch,0.282740389910783096460988872423,100,0.001000000000000000020816681712,0.018468888734889537389660674194,2
33,33_0,COMPLETED,BoTorch,0.282905606344311055799778387154,140,0.001000000000000000020816681712,0.144121023614028093140504438452,3
34,34_0,FAILED,BoTorch,,199,0.001000000000000000020816681712,0.000000000000000000000000000000,3
35,35_0,COMPLETED,BoTorch,0.280757792708448028484724545706,189,0.005000000000000000104083408559,0.142858111407180632390634400508,3
36,36_0,COMPLETED,BoTorch,0.289238902962881350156010284991,250,0.005000000000000000104083408559,0.112690125891816705916426144540,3
37,37_0,COMPLETED,BoTorch,0.284888203546646123776042713871,166,0.050000000000000002775557561563,0.039532969656030508831534575620,3
38,38_0,COMPLETED,BoTorch,0.282299812754708723261387603998,136,0.100000000000000005551115123126,0.111431966640026580694922131443,3
39,39_0,COMPLETED,BoTorch,0.284392554246062356781976632192,100,0.010000000000000000208166817117,0.167280718658282007149651349209,2
40,40_0,COMPLETED,BoTorch,0.277453464037889618865051488683,180,0.025000000000000001387778780781,0.197395279598435141599210851382,3
41,41_0,COMPLETED,BoTorch,0.279711421962771211724430031609,100,0.025000000000000001387778780781,0.123391138167767241973216130191,3
42,42_0,COMPLETED,BoTorch,0.281253442009031795478790627385,100,0.050000000000000002775557561563,0.200000000000000011102230246252,3
43,43_0,COMPLETED,BoTorch,0.280647648419429462940399844229,164,0.025000000000000001387778780781,0.200000000000000011102230246252,2
44,44_0,COMPLETED,BoTorch,0.278389690494547870081021301303,100,0.025000000000000001387778780781,0.200000000000000011102230246252,4
45,45_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.016772286104730034506093971913,1
46,46_0,COMPLETED,BoTorch,0.280041854829827019379706598556,100,0.050000000000000002775557561563,0.088872069637165179711857376788,3
47,47_0,COMPLETED,BoTorch,0.283511399933913388338169170311,206,0.050000000000000002775557561563,0.189321426289413508037284827878,3
48,48_0,COMPLETED,BoTorch,0.279270844806696727502526300668,100,0.025000000000000001387778780781,0.200000000000000011102230246252,2
49,49_0,COMPLETED,BoTorch,0.279160700517678161958201599191,100,0.025000000000000001387778780781,0.075672156407027513225216353021,1
50,50_0,COMPLETED,BoTorch,0.277783896904945426520328055631,100,0.050000000000000002775557561563,0.131572762476871607528750018901,3
51,51_0,COMPLETED,BoTorch,0.279656349818261928952267680870,100,0.100000000000000005551115123126,0.200000000000000011102230246252,4
52,52_0,COMPLETED,BoTorch,0.280317215552373655285123277281,100,0.250000000000000000000000000000,0.200000000000000011102230246252,1
53,53_0,COMPLETED,BoTorch,0.281198369864522512706628276646,155,0.005000000000000000104083408559,0.015263594869020234365275534572,1
54,54_0,COMPLETED,BoTorch,0.281473730587069037589742492855,157,0.025000000000000001387778780781,0.200000000000000011102230246252,4
55,55_0,COMPLETED,BoTorch,0.279380989095715404069153464661,161,0.025000000000000001387778780781,0.167245117074233701215391079131,3
56,56_0,COMPLETED,BoTorch,0.284282409957043680215349468199,160,0.010000000000000000208166817117,0.200000000000000011102230246252,3
57,57_0,COMPLETED,BoTorch,0.289293975107390632928172635729,203,0.100000000000000005551115123126,0.200000000000000011102230246252,4
58,58_0,FAILED,BoTorch,,131,0.025000000000000001387778780781,0.000000000000000000000000000000,1
59,59_0,FAILED,BoTorch,,147,0.001000000000000000020816681712,0.000000000000000000000000000000,1
60,60_0,COMPLETED,BoTorch,0.285053419980174083114832228603,150,0.005000000000000000104083408559,0.055951993657619131239400189770,1
61,61_0,COMPLETED,BoTorch,0.282795462055292379233151223161,174,0.010000000000000000208166817117,0.196432733221418964753013369773,3
62,62_0,COMPLETED,BoTorch,0.281749091309615562472856709064,186,0.025000000000000001387778780781,0.161379139920321101886457881847,3
63,63_0,COMPLETED,BoTorch,0.276847670448287286326660705527,158,0.010000000000000000208166817117,0.200000000000000011102230246252,4
64,64_0,COMPLETED,BoTorch,0.285714285714285698425385362498,154,0.025000000000000001387778780781,0.200000000000000011102230246252,3
65,65_0,COMPLETED,BoTorch,0.289899768696993076488865881402,254,0.100000000000000005551115123126,0.200000000000000011102230246252,3
66,66_0,FAILED,BoTorch,,143,0.010000000000000000208166817117,0.000000000000000000000000000000,1
67,67_0,COMPLETED,BoTorch,0.280096926974336413174171411811,100,0.100000000000000005551115123126,0.008081171800640817373673208124,1
68,68_0,COMPLETED,BoTorch,0.281363586298050472045417791378,100,0.250000000000000000000000000000,0.098285548983866308225820773714,1
69,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
70,70_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.200000000000000011102230246252,2
71,71_0,COMPLETED,BoTorch,0.279711421962771211724430031609,100,0.250000000000000000000000000000,0.200000000000000011102230246252,3
72,72_0,COMPLETED,BoTorch,0.281638947020596996928532007587,144,0.050000000000000002775557561563,0.200000000000000011102230246252,1
73,73_0,COMPLETED,BoTorch,0.279931710540808453835381897079,100,0.050000000000000002775557561563,0.146667877415456449075037426155,1
74,74_0,COMPLETED,BoTorch,0.278279546205529193514394137310,100,0.250000000000000000000000000000,0.087106881242826963984704491395,2
75,75_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.148886028762708788608648546870,2
76,76_0,COMPLETED,BoTorch,0.286485295737415990302565660386,172,0.250000000000000000000000000000,0.200000000000000011102230246252,1
77,77_0,COMPLETED,BoTorch,0.279325916951206121296991113923,100,0.050000000000000002775557561563,0.067082055787048136541450560344,1
78,78_0,COMPLETED,BoTorch,0.278720123361603677736297868250,137,0.100000000000000005551115123126,0.163905464382652882315838382965,1
79,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
80,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
81,81_0,COMPLETED,BoTorch,0.278279546205529193514394137310,100,0.250000000000000000000000000000,0.119808151095350090553637301127,2
82,82_0,COMPLETED,BoTorch,0.281363586298050472045417791378,100,0.250000000000000000000000000000,0.054810520901026262008404188464,1
83,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
84,84_0,COMPLETED,BoTorch,0.281583874876087714156369656848,157,0.050000000000000002775557561563,0.056729940933681249903841603555,1
85,85_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
86,86_0,FAILED,BoTorch,,151,0.250000000000000000000000000000,0.000000000000000000000000000000,1
87,87_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.129240514166755054992208329168,4
88,88_0,COMPLETED,BoTorch,0.278665051217094394964135517512,100,0.100000000000000005551115123126,0.154183314505685897799480699177,2
89,89_0,FAILED,BoTorch,,208,0.250000000000000000000000000000,0.000000000000000000000000000000,1
90,90_0,COMPLETED,BoTorch,0.280096926974336413174171411811,172,0.100000000000000005551115123126,0.019043071984322468975792119750,1
91,91_0,COMPLETED,BoTorch,0.277343319748871053320726787206,100,0.050000000000000002775557561563,0.084601905107450525722612155732,2
92,92_0,COMPLETED,BoTorch,0.277178103315343093981937272474,158,0.250000000000000000000000000000,0.071380868764979774065970730135,1
93,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
94,94_0,COMPLETED,BoTorch,0.279546205529243363407942979393,100,0.250000000000000000000000000000,0.126063321334941941254115249649,3
95,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
96,96_0,COMPLETED,BoTorch,0.282244740610199329466922790743,162,0.100000000000000005551115123126,0.082406085220461888773968439637,1
97,97_0,COMPLETED,BoTorch,0.277783896904945426520328055631,100,0.050000000000000002775557561563,0.052815648415997534792509782164,2
98,98_0,COMPLETED,BoTorch,0.279711421962771211724430031609,104,0.050000000000000002775557561563,0.040917674834797496119520587854,2
99,99_0,COMPLETED,BoTorch,0.283015750633329621344103088632,175,0.100000000000000005551115123126,0.059508679931502175830537737511,1
100,100_0,COMPLETED,BoTorch,0.280317215552373655285123277281,100,0.250000000000000000000000000000,0.132175142840226106377699011318,1
101,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
102,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
103,103_0,FAILED,BoTorch,,209,0.250000000000000000000000000000,0.000000000000000000000000000000,1
104,104_0,COMPLETED,BoTorch,0.279160700517678161958201599191,100,0.100000000000000005551115123126,0.147701455916858542805059073544,3
105,105_0,COMPLETED,BoTorch,0.281694019165106279700694358326,100,0.250000000000000000000000000000,0.015408072592119799459897677707,4
106,106_0,FAILED,BoTorch,,133,0.250000000000000000000000000000,0.000000000000000000000000000000,1
107,107_0,COMPLETED,BoTorch,0.280427359841392220829447978758,100,0.010000000000000000208166817117,0.200000000000000011102230246252,4
108,108_0,COMPLETED,BoTorch,0.274038991078312643701053730183,100,0.100000000000000005551115123126,0.107651776383508049339532419708,2
109,85_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,3
110,110_0,FAILED,BoTorch,,246,0.250000000000000000000000000000,0.000000000000000000000000000000,1
111,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
112,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
113,113_0,FAILED,BoTorch,,181,0.100000000000000005551115123126,0.000000000000000000000000000000,1
114,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
115,115_0,COMPLETED,BoTorch,0.280372287696882938057285628020,100,0.250000000000000000000000000000,0.058837232069032657788554274703,3
116,116_0,COMPLETED,BoTorch,0.280372287696882938057285628020,100,0.250000000000000000000000000000,0.019451621923215790810335334982,3
117,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
118,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
119,119_0,COMPLETED,BoTorch,0.278499834783566435625346002780,100,0.100000000000000005551115123126,0.071280474695899459502967943081,2
120,120_0,COMPLETED,BoTorch,0.277453464037889618865051488683,100,0.250000000000000000000000000000,0.027479106722264542983502622064,2
121,121_0,COMPLETED,BoTorch,0.280096926974336413174171411811,100,0.100000000000000005551115123126,0.107526642964011473480034908334,1
122,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
123,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
124,124_0,COMPLETED,BoTorch,0.280207071263354978718496113288,127,0.250000000000000000000000000000,0.080205005851055805043969826329,2
125,125_0,FAILED,BoTorch,,229,0.250000000000000000000000000000,0.000000000000000000000000000000,1
126,126_0,FAILED,BoTorch,,311,0.250000000000000000000000000000,0.000000000000000000000000000000,1
127,127_0,COMPLETED,BoTorch,0.280096926974336413174171411811,100,0.100000000000000005551115123126,0.063816599022502473737006312149,1
128,128_0,FAILED,BoTorch,,203,0.250000000000000000000000000000,0.000000000000000000000000000000,3
129,129_0,COMPLETED,BoTorch,0.273212908910672958029408619041,100,0.100000000000000005551115123126,0.034025703431758771988491218963,2
130,130_0,FAILED,BoTorch,,376,0.250000000000000000000000000000,0.000000000000000000000000000000,3
131,131_0,FAILED,BoTorch,,252,0.250000000000000000000000000000,0.000000000000000000000000000000,3
132,132_0,COMPLETED,BoTorch,0.278499834783566435625346002780,172,0.001000000000000000020816681712,0.200000000000000011102230246252,4
133,133_0,COMPLETED,BoTorch,0.276792598303778003554498354788,100,0.250000000000000000000000000000,0.051157768335299030892926452907,2
134,134_0,FAILED,BoTorch,,301,0.100000000000000005551115123126,0.000000000000000000000000000000,1
135,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
136,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
137,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
138,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
139,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
140,140_0,FAILED,BoTorch,,166,0.250000000000000000000000000000,0.000000000000000000000000000000,1
141,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
142,142_0,FAILED,BoTorch,,143,0.250000000000000000000000000000,0.000000000000000000000000000000,2
143,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
144,144_0,COMPLETED,BoTorch,0.286705584315453232413517525856,142,0.001000000000000000020816681712,0.200000000000000011102230246252,4
145,145_0,COMPLETED,BoTorch,0.279325916951206121296991113923,100,0.050000000000000002775557561563,0.031364224861028959512321279135,1
146,146_0,COMPLETED,BoTorch,0.277067959026324528437612570997,100,0.100000000000000005551115123126,0.050877720506665717603578258377,2
147,147_0,FAILED,BoTorch,,230,0.250000000000000000000000000000,0.000000000000000000000000000000,1
148,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
149,149_0,FAILED,BoTorch,,288,0.250000000000000000000000000000,0.000000000000000000000000000000,2
150,150_0,FAILED,BoTorch,,235,0.250000000000000000000000000000,0.000000000000000000000000000000,2
151,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
152,152_0,FAILED,BoTorch,,372,0.250000000000000000000000000000,0.000000000000000000000000000000,1
153,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
154,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
155,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
156,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
157,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
158,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
159,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
160,160_0,FAILED,BoTorch,,258,0.250000000000000000000000000000,0.000000000000000000000000000000,1
161,142_0,FAILED,BoTorch,,143,0.250000000000000000000000000000,0.000000000000000000000000000000,2
162,162_0,FAILED,BoTorch,,246,0.250000000000000000000000000000,0.000000000000000000000000000000,3
163,163_0,FAILED,BoTorch,,189,0.250000000000000000000000000000,0.000000000000000000000000000000,1
164,164_0,FAILED,BoTorch,,252,0.250000000000000000000000000000,0.000000000000000000000000000000,2
165,165_0,COMPLETED,BoTorch,0.282024452032162087355970925273,130,0.100000000000000005551115123126,0.014940955056160890729310963820,1
166,166_0,COMPLETED,BoTorch,0.282740389910783096460988872423,241,0.250000000000000000000000000000,0.004611359294566957402194962157,2
167,167_0,FAILED,BoTorch,,138,0.250000000000000000000000000000,0.000000000000000000000000000000,1
168,168_0,COMPLETED,BoTorch,0.285493997136248456314433497027,100,0.050000000000000002775557561563,0.033234922768231221767987193516,4
169,169_0,FAILED,BoTorch,,420,0.250000000000000000000000000000,0.000000000000000000000000000000,2
170,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
171,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
172,172_0,FAILED,BoTorch,,342,0.250000000000000000000000000000,0.000000000000000000000000000000,1
173,173_0,COMPLETED,BoTorch,0.288688181517788300389781852573,379,0.250000000000000000000000000000,0.055260351827480319597540869836,2
174,174_0,FAILED,BoTorch,,423,0.250000000000000000000000000000,0.000000000000000000000000000000,1
175,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
176,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
177,177_0,COMPLETED,BoTorch,0.279546205529243363407942979393,100,0.100000000000000005551115123126,0.091784815734130054121209241202,2
178,178_0,FAILED,BoTorch,,141,0.250000000000000000000000000000,0.000000000000000000000000000000,2
179,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
180,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
181,181_0,COMPLETED,BoTorch,0.283456327789404105566006819572,371,0.250000000000000000000000000000,0.035268170977396595677788582179,2
182,182_0,COMPLETED,BoTorch,0.288798325806806865934106554050,240,0.250000000000000000000000000000,0.006550836244915510600539398922,2
183,183_0,FAILED,BoTorch,,128,0.100000000000000005551115123126,0.000000000000000000000000000000,2
184,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
185,185_0,COMPLETED,BoTorch,0.283346183500385540021682118095,371,0.250000000000000000000000000000,0.012474694277423533511628406245,2
186,186_0,COMPLETED,BoTorch,0.282575173477255248144501820207,138,0.010000000000000000208166817117,0.105835394462641854684825659660,1
187,187_0,FAILED,BoTorch,,312,0.100000000000000005551115123126,0.000000000000000000000000000000,2
188,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
189,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
190,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
191,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
192,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
193,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
194,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
195,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
196,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
197,197_0,COMPLETED,BoTorch,0.284282409957043680215349468199,121,0.100000000000000005551115123126,0.064490437360056454552115212664,2
198,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
199,199_0,COMPLETED,BoTorch,0.280482431985901503601610329497,100,0.025000000000000001387778780781,0.200000000000000011102230246252,1
200,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
201,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
202,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
203,203_0,FAILED,BoTorch,,475,0.250000000000000000000000000000,0.000000000000000000000000000000,1
204,204_0,COMPLETED,BoTorch,0.275030289679480066666883431026,100,0.050000000000000002775557561563,0.200000000000000011102230246252,4
205,205_0,COMPLETED,BoTorch,0.280427359841392220829447978758,100,0.250000000000000000000000000000,0.179974964553872268657741528841,4
206,206_0,FAILED,BoTorch,,376,0.250000000000000000000000000000,0.000000000000000000000000000000,4
207,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
208,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
209,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
210,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
211,211_0,FAILED,BoTorch,,144,0.250000000000000000000000000000,0.000000000000000000000000000000,1
212,212_0,FAILED,BoTorch,,100,0.005000000000000000104083408559,0.000000000000000000000000000000,1
213,213_0,FAILED,BoTorch,,160,0.010000000000000000208166817117,0.000000000000000000000000000000,1
214,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
215,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
216,216_0,COMPLETED,BoTorch,0.280757792708448028484724545706,100,0.250000000000000000000000000000,0.200000000000000011102230246252,4
217,217_0,FAILED,BoTorch,,145,0.001000000000000000020816681712,0.000000000000000000000000000000,1
218,218_0,COMPLETED,BoTorch,0.280427359841392220829447978758,100,0.050000000000000002775557561563,0.124746793075073622580895005285,2
219,219_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.016780906584811027676407135800,1
220,220_0,FAILED,BoTorch,,328,0.250000000000000000000000000000,0.000000000000000000000000000000,1
221,221_0,FAILED,BoTorch,,438,0.250000000000000000000000000000,0.000000000000000000000000000000,1
222,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
223,223_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.016828327482921595636966571874,1
224,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
225,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
226,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
227,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
228,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
229,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
230,230_0,COMPLETED,BoTorch,0.276792598303778003554498354788,100,0.250000000000000000000000000000,0.040516131459451071306077096779,2
231,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
232,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
233,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
234,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
235,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
236,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
237,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
238,238_0,FAILED,BoTorch,,447,0.250000000000000000000000000000,0.000000000000000000000000000000,1
239,239_0,FAILED,BoTorch,,494,0.250000000000000000000000000000,0.000000000000000000000000000000,1
240,240_0,COMPLETED,BoTorch,0.295847560303998280417658861552,584,0.025000000000000001387778780781,0.012052556872367859233663445195,4
241,241_0,COMPLETED,BoTorch,0.280372287696882938057285628020,100,0.100000000000000005551115123126,0.200000000000000011102230246252,3
242,242_0,FAILED,BoTorch,,385,0.250000000000000000000000000000,0.000000000000000000000000000000,4
243,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
244,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
245,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
246,246_0,FAILED,BoTorch,,525,0.100000000000000005551115123126,0.000000000000000000000000000000,4
247,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
248,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
249,249_0,COMPLETED,BoTorch,0.284943275691155406548205064610,270,0.100000000000000005551115123126,0.062503175008246217303486957917,4
250,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
251,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
252,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
253,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
254,254_0,COMPLETED,BoTorch,0.289349047251900026722637448984,246,0.250000000000000000000000000000,0.037427697376058485789762642071,4
255,255_0,FAILED,BoTorch,,461,0.250000000000000000000000000000,0.000000000000000000000000000000,3
256,256_0,COMPLETED,BoTorch,0.275525938980063833660949512705,100,0.100000000000000005551115123126,0.097796768657165744631498682793,3
257,257_0,COMPLETED,BoTorch,0.287641810772111483629487338476,300,0.250000000000000000000000000000,0.017493468304008136238181236877,3
258,258_0,FAILED,BoTorch,,434,0.250000000000000000000000000000,0.000000000000000000000000000000,1
259,259_0,FAILED,BoTorch,,434,0.250000000000000000000000000000,0.000000000000000000000000000000,4
260,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
261,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
262,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
263,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
264,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
265,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
266,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
267,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
268,268_0,RUNNING,BoTorch,,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1
269,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
270,270_0,FAILED,BoTorch,,527,0.250000000000000000000000000000,0.000000000000000000000000000000,1
271,271_0,COMPLETED,BoTorch,0.284172265668025114671024766722,100,0.001000000000000000020816681712,0.148762731647136131618580634495,1
272,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
273,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
274,274_0,FAILED,BoTorch,,523,0.250000000000000000000000000000,0.000000000000000000000000000000,1
275,275_0,COMPLETED,BoTorch,0.293093953078532920564214236947,392,0.250000000000000000000000000000,0.008157396350348457628176568335,1
276,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
277,277_0,COMPLETED,BoTorch,0.278720123361603677736297868250,100,0.005000000000000000104083408559,0.162367116239565456581317448581,1
278,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
279,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
280,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
281,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
282,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
283,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
284,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
285,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
286,286_0,RUNNING,BoTorch,,418,0.001000000000000000020816681712,0.200000000000000011102230246252,4
287,287_0,RUNNING,BoTorch,,210,0.005000000000000000104083408559,0.195229531824729851674504743642,4
288,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
289,289_0,FAILED,BoTorch,,399,0.250000000000000000000000000000,0.000000000000000000000000000000,4
290,290_0,COMPLETED,BoTorch,0.288522965084260341050992337841,391,0.250000000000000000000000000000,0.200000000000000011102230246252,4
291,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
292,292_0,COMPLETED,BoTorch,0.286430223592906707530403309647,398,0.250000000000000000000000000000,0.124213997212494514643665866060,4
293,293_0,COMPLETED,BoTorch,0.290835995153651327704835694021,265,0.005000000000000000104083408559,0.195706359202674429198509642447,4
294,294_0,COMPLETED,BoTorch,0.295186694569886554084803265141,379,0.005000000000000000104083408559,0.138826016297458193493596922963,4
295,295_0,COMPLETED,BoTorch,0.293204097367551486108538938424,398,0.250000000000000000000000000000,0.116092535794456830355869669802,4
296,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
297,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
298,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
299,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
300,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
301,301_0,COMPLETED,BoTorch,0.285549069280757739086595847766,164,0.001000000000000000020816681712,0.200000000000000011102230246252,1
302,302_0,COMPLETED,BoTorch,0.279215772662187444730363949930,116,0.001000000000000000020816681712,0.200000000000000011102230246252,1
303,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
304,304_0,COMPLETED,BoTorch,0.282354884899218006033549954736,126,0.001000000000000000020816681712,0.200000000000000011102230246252,1
305,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
306,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
307,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
308,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
309,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
310,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
311,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
312,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
313,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
314,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
315,315_0,COMPLETED,BoTorch,0.279931710540808453835381897079,100,0.050000000000000002775557561563,0.200000000000000011102230246252,1
316,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
317,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
318,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
319,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
320,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
321,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
322,322_0,COMPLETED,BoTorch,0.276792598303778003554498354788,100,0.250000000000000000000000000000,0.065092367586101540499754491975,2
323,323_0,COMPLETED,BoTorch,0.276902742592796569098823056265,100,0.250000000000000000000000000000,0.086519324341300926639597435042,3
324,324_0,COMPLETED,BoTorch,0.276737526159268609760033541534,100,0.025000000000000001387778780781,0.148885438950722565065731828327,4
325,325_0,COMPLETED,BoTorch,0.277949113338473385859117570362,100,0.010000000000000000208166817117,0.114237107451868713514642195150,1
326,326_0,COMPLETED,BoTorch,0.278554906928075829419810816034,100,0.050000000000000002775557561563,0.162751877759727187067184672742,4
327,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
328,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
329,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
330,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
331,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
332,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
333,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
334,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
335,335_0,COMPLETED,BoTorch,0.280482431985901503601610329497,100,0.025000000000000001387778780781,0.166185298806437647956002479077,1
336,336_0,COMPLETED,BoTorch,0.279160700517678161958201599191,100,0.025000000000000001387778780781,0.118538379812509614419369086136,1
337,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
338,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
339,339_0,COMPLETED,BoTorch,0.279050556228659596413876897714,100,0.250000000000000000000000000000,0.058328797540965919565802977331,2
340,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
341,341_0,COMPLETED,BoTorch,0.281804163454124956267321522319,100,0.010000000000000000208166817117,0.200000000000000011102230246252,1
342,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
343,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
344,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
345,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
346,346_0,COMPLETED,BoTorch,0.278279546205529193514394137310,100,0.250000000000000000000000000000,0.067999752960812329116713215171,2
347,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
348,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
349,349_0,COMPLETED,BoTorch,0.279931710540808453835381897079,100,0.050000000000000002775557561563,0.157042400667316200957301930430,1
350,350_0,COMPLETED,BoTorch,0.282024452032162087355970925273,104,0.050000000000000002775557561563,0.040941970392548272805743891922,2
351,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
352,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
353,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
354,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
355,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
356,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
357,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
358,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
359,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
360,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
361,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
362,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
363,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
364,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
365,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
366,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
367,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
368,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
369,369_0,COMPLETED,BoTorch,0.286870800748981191752307040588,156,0.010000000000000000208166817117,0.147898331283112555167846835502,1
370,79_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,1
371,371_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,2
372,372_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,4
373,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
374,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
375,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
376,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
377,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
378,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
379,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
380,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
381,381_0,COMPLETED,BoTorch,0.279436061240224686841315815400,100,0.250000000000000000000000000000,0.077617748101248329462009678537,4
382,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
383,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
384,384_0,COMPLETED,BoTorch,0.277288247604361659526261973951,100,0.100000000000000005551115123126,0.186089965302586257678285619477,1
385,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
386,386_0,COMPLETED,BoTorch,0.278885339795131637075087382982,100,0.005000000000000000104083408559,0.062498399400783159751693318640,1
387,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
388,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
389,389_0,COMPLETED,BoTorch,0.278665051217094394964135517512,100,0.100000000000000005551115123126,0.144907860933490367782994212575,3
390,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
391,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
392,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
393,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
394,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
395,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
396,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
397,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
398,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
399,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
400,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
401,401_0,COMPLETED,BoTorch,0.280151999118845695946333762549,100,0.250000000000000000000000000000,0.173224199560479408255275757256,4
402,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
403,403_0,COMPLETED,BoTorch,0.281363586298050472045417791378,100,0.250000000000000000000000000000,0.045459298479500663547892003180,1
404,404_0,COMPLETED,BoTorch,0.285273708558211214203481631557,171,0.010000000000000000208166817117,0.100038032419979577847257701251,2
405,405_0,COMPLETED,BoTorch,0.279491133384733969613478166139,100,0.050000000000000002775557561563,0.163307962482790558489398335951,3
406,95_0,FAILED,BoTorch,,100,0.050000000000000002775557561563,0.000000000000000000000000000000,2
407,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
408,80_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,1
409,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
410,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
411,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
412,268_0,COMPLETED,BoTorch,0.284172265668025114671024766722,100,0.001000000000000000020816681712,0.200000000000000011102230246252,1
413,413_0,COMPLETED,BoTorch,0.277288247604361659526261973951,100,0.100000000000000005551115123126,0.158134305030585536888665387778,1
414,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
415,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
416,416_0,COMPLETED,BoTorch,0.284172265668025114671024766722,100,0.001000000000000000020816681712,0.151038369651452092368160151636,1
417,227_0,FAILED,BoTorch,,100,0.001000000000000000020816681712,0.000000000000000000000000000000,1
418,418_0,COMPLETED,BoTorch,0.277453464037889618865051488683,100,0.250000000000000000000000000000,0.089778478806331427075626550049,2
419,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
420,420_0,COMPLETED,BoTorch,0.279656349818261928952267680870,100,0.050000000000000002775557561563,0.147690880372298688483212458777,3
421,141_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,2
422,69_0,FAILED,BoTorch,,100,0.025000000000000001387778780781,0.000000000000000000000000000000,1
423,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
424,424_0,FAILED,BoTorch,,133,0.250000000000000000000000000000,0.000000000000000000000000000000,2
425,425_0,COMPLETED,BoTorch,0.281528802731578320361904843594,172,0.010000000000000000208166817117,0.200000000000000011102230246252,4
426,426_0,FAILED,BoTorch,,153,0.250000000000000000000000000000,0.000000000000000000000000000000,1
427,427_0,COMPLETED,BoTorch,0.280537504130410786373772680236,129,0.250000000000000000000000000000,0.200000000000000011102230246252,1
428,428_0,COMPLETED,BoTorch,0.284062121379006549126700065244,146,0.250000000000000000000000000000,0.072270933389340472063899767363,1
429,429_0,FAILED,BoTorch,,191,0.005000000000000000104083408559,0.000000000000000000000000000000,1
430,430_0,COMPLETED,BoTorch,0.279986782685317736607544247818,169,0.005000000000000000104083408559,0.144232418191124950324066844587,4
431,431_0,COMPLETED,BoTorch,0.279105628373168879186039248452,119,0.050000000000000002775557561563,0.200000000000000011102230246252,1
432,432_0,COMPLETED,BoTorch,0.286540367881925273074728011125,205,0.025000000000000001387778780781,0.118217182327579162226527387247,1
433,433_0,COMPLETED,BoTorch,0.283786760656459913221283386520,169,0.001000000000000000020816681712,0.086063488076073779708963229496,4
434,434_0,COMPLETED,BoTorch,0.279325916951206121296991113923,195,0.005000000000000000104083408559,0.200000000000000011102230246252,1
435,435_0,COMPLETED,BoTorch,0.280207071263354978718496113288,199,0.005000000000000000104083408559,0.098712345832683456858802628631,1
436,436_0,COMPLETED,BoTorch,0.279270844806696727502526300668,115,0.005000000000000000104083408559,0.019650920318037799761068384896,1
437,437_0,COMPLETED,BoTorch,0.283676616367441347676958685042,144,0.250000000000000000000000000000,0.089353878213655715812002711118,1
438,438_0,COMPLETED,BoTorch,0.278499834783566435625346002780,114,0.005000000000000000104083408559,0.090201890089163697106222628008,1
439,439_0,COMPLETED,BoTorch,0.284007049234497155332235251990,199,0.010000000000000000208166817117,0.053078853834053155158478887188,1
440,440_0,COMPLETED,BoTorch,0.279050556228659596413876897714,118,0.050000000000000002775557561563,0.200000000000000011102230246252,1
441,441_0,FAILED,BoTorch,,205,0.100000000000000005551115123126,0.000000000000000000000000000000,1
442,442_0,COMPLETED,BoTorch,0.277067959026324528437612570997,110,0.010000000000000000208166817117,0.069722779905971490888028085919,1
443,443_0,COMPLETED,BoTorch,0.281473730587069037589742492855,106,0.050000000000000002775557561563,0.200000000000000011102230246252,4
444,444_0,FAILED,BoTorch,,342,0.250000000000000000000000000000,0.000000000000000000000000000000,2
445,445_0,COMPLETED,BoTorch,0.280482431985901503601610329497,190,0.001000000000000000020816681712,0.200000000000000011102230246252,2
446,446_0,COMPLETED,BoTorch,0.285714285714285698425385362498,200,0.001000000000000000020816681712,0.200000000000000011102230246252,1
447,447_0,COMPLETED,BoTorch,0.280427359841392220829447978758,131,0.250000000000000000000000000000,0.200000000000000011102230246252,4
448,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1
449,114_0,FAILED,BoTorch,,100,0.010000000000000000208166817117,0.000000000000000000000000000000,1
450,450_0,COMPLETED,BoTorch,0.279380989095715404069153464661,185,0.001000000000000000020816681712,0.200000000000000011102230246252,3
451,451_0,COMPLETED,BoTorch,0.282740389910783096460988872423,126,0.250000000000000000000000000000,0.200000000000000011102230246252,3
452,452_0,FAILED,BoTorch,,350,0.250000000000000000000000000000,0.000000000000000000000000000000,4
453,453_0,COMPLETED,BoTorch,0.281253442009031795478790627385,117,0.100000000000000005551115123126,0.200000000000000011102230246252,4
454,454_0,FAILED,BoTorch,,110,0.010000000000000000208166817117,0.000000000000000000000000000000,1
455,455_0,FAILED,BoTorch,,340,0.250000000000000000000000000000,0.000000000000000000000000000000,1
456,456_0,COMPLETED,BoTorch,0.291882365899328144465130208118,345,0.250000000000000000000000000000,0.154313144062262819211639452988,4
457,457_0,COMPLETED,BoTorch,0.277728824760436143748165704892,142,0.250000000000000000000000000000,0.200000000000000011102230246252,4
458,458_0,COMPLETED,BoTorch,0.285604141425267132881060661020,154,0.250000000000000000000000000000,0.139474740217836762345982037914,4
459,459_0,COMPLETED,BoTorch,0.290285273708558166916304799088,465,0.250000000000000000000000000000,0.200000000000000011102230246252,4
460,93_0,FAILED,BoTorch,,100,0.250000000000000000000000000000,0.000000000000000000000000000000,2
461,461_0,COMPLETED,BoTorch,0.280647648419429462940399844229,114,0.010000000000000000208166817117,0.108764820140075157972603392409,1
462,462_0,COMPLETED,BoTorch,0.283731688511950630449121035781,163,0.100000000000000005551115123126,0.200000000000000011102230246252,4
463,463_0,COMPLETED,BoTorch,0.277067959026324528437612570997,110,0.010000000000000000208166817117,0.044969895511009175259609094155,1
464,464_0,FAILED,BoTorch,,109,0.010000000000000000208166817117,0.000000000000000000000000000000,1
465,465_0,FAILED,BoTorch,,180,0.001000000000000000020816681712,0.000000000000000000000000000000,1
466,466_0,COMPLETED,BoTorch,0.292267870910893234892569125805,475,0.250000000000000000000000000000,0.200000000000000011102230246252,1
467,467_0,COMPLETED,BoTorch,0.294911333847340029201689048932,468,0.250000000000000000000000000000,0.200000000000000011102230246252,3
468,468_0,COMPLETED,BoTorch,0.299206961119065972809494269313,470,0.250000000000000000000000000000,0.021224734671334202079640007810,4
469,469_0,FAILED,BoTorch,,341,0.250000000000000000000000000000,0.000000000000000000000000000000,1
470,470_0,COMPLETED,BoTorch,0.286870800748981191752307040588,190,0.010000000000000000208166817117,0.200000000000000011102230246252,2
471,471_0,COMPLETED,BoTorch,0.294580900980284221546412481985,346,0.250000000000000000000000000000,0.098074682697481499471514609922,1
472,472_0,COMPLETED,BoTorch,0.294635973124793504318574832723,466,0.250000000000000000000000000000,0.148270767925898189210087707579,4
473,473_0,FAILED,BoTorch,,271,0.250000000000000000000000000000,0.000000000000000000000000000000,1
474,474_0,FAILED,BoTorch,,110,0.005000000000000000104083408559,0.000000000000000000000000000000,1
475,475_0,COMPLETED,BoTorch,0.277067959026324528437612570997,110,0.010000000000000000208166817117,0.000140524550070662655873107716,1
476,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1
477,477_0,COMPLETED,BoTorch,0.276186804714175559993805109116,186,0.001000000000000000020816681712,0.078044541724856641984331417916,1
478,478_0,FAILED,BoTorch,,182,0.001000000000000000020816681712,0.000000000000000000000000000000,1
479,479_0,FAILED,BoTorch,,113,0.001000000000000000020816681712,0.000000000000000000000000000000,1
480,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1
481,481_0,COMPLETED,BoTorch,0.282354884899218006033549954736,106,0.010000000000000000208166817117,0.007445980293814238176741682196,1
482,482_0,FAILED,BoTorch,,164,0.250000000000000000000000000000,0.000000000000000000000000000000,1
483,483_0,COMPLETED,BoTorch,0.280041854829827019379706598556,178,0.001000000000000000020816681712,0.058670282113353955377732518173,2
484,139_0,FAILED,BoTorch,,100,0.100000000000000005551115123126,0.000000000000000000000000000000,2
485,485_0,FAILED,BoTorch,,738,0.250000000000000000000000000000,0.000000000000000000000000000000,1
486,486_0,FAILED,BoTorch,,104,0.010000000000000000208166817117,0.000000000000000000000000000000,1
487,487_0,COMPLETED,BoTorch,0.278334618350038587308858950564,184,0.001000000000000000020816681712,0.200000000000000011102230246252,4
488,488_0,FAILED,BoTorch,,267,0.025000000000000001387778780781,0.000000000000000000000000000000,1
489,448_0,FAILED,BoTorch,,181,0.001000000000000000020816681712,0.000000000000000000000000000000,1
490,490_0,FAILED,BoTorch,,269,0.100000000000000005551115123126,0.000000000000000000000000000000,1
491,478_0,FAILED,BoTorch,,182,0.001000000000000000020816681712,0.000000000000000000000000000000,1
492,492_0,FAILED,BoTorch,,266,0.005000000000000000104083408559,0.000000000000000000000000000000,1
493,493_0,COMPLETED,BoTorch,0.286870800748981191752307040588,182,0.001000000000000000020816681712,0.009659092574850258738905495193,1
494,494_0,FAILED,BoTorch,,117,0.001000000000000000020816681712,0.000000000000000000000000000000,1
495,465_0,FAILED,BoTorch,,180,0.001000000000000000020816681712,0.000000000000000000000000000000,1
496,496_0,FAILED,BoTorch,,284,0.010000000000000000208166817117,0.000000000000000000000000000000,1
497,497_0,COMPLETED,BoTorch,0.278940411939640919847249733721,183,0.001000000000000000020816681712,0.024836256220531940930307257531,1
498,498_0,FAILED,BoTorch,,272,0.050000000000000002775557561563,0.000000000000000000000000000000,1
499,499_0,COMPLETED,BoTorch,0.289459191540918592266962150461,224,0.010000000000000000208166817117,0.147249993213455082630147785494,4
500,500_0,COMPLETED,BoTorch,0.282630245621764530916664170945,270,0.050000000000000002775557561563,0.000008729249569113619228221193,1
501,501_0,RUNNING,BoTorch,,161,0.250000000000000000000000000000,0.000000000000000000000000000000,1
502,502_0,RUNNING,BoTorch,,178,0.005000000000000000104083408559,0.000000000000000000000000000000,1
503,503_0,RUNNING,BoTorch,,271,0.050000000000000002775557561563,0.000000000000000000000000000000,1
504,504_0,RUNNING,BoTorch,,254,0.025000000000000001387778780781,0.029757720669419746722939024153,1
505,478_0,RUNNING,BoTorch,,182,0.001000000000000000020816681712,0.000000000000000000000000000000,1
506,506_0,RUNNING,BoTorch,,118,0.001000000000000000020816681712,0.000000000000000000000000000000,1
</pre>
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<h1> CPU/RAM-Usage (main)</h1>
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<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1727277346,476.44921875,28.5
1727277346,476.44921875,29.8
1727277346,476.44921875,28.6
1727277346,476.44921875,36.8
1727277346,476.44921875,25.0
1727277346,476.44921875,28.3
1727277346,476.44921875,29.4
1727277389,481.49609375,29.2
1727277389,481.49609375,20.6
1727277389,481.49609375,28.4
1727277389,481.49609375,25.0
1727277391,481.49609375,28.4
1727277391,481.49609375,24.2
1727277391,481.49609375,29.8
1727277391,481.49609375,22.6
1727277392,481.49609375,28.4
1727277392,481.49609375,21.9
1727277392,481.49609375,30.2
1727277392,481.49609375,25.0
1727277393,481.49609375,28.5
1727277393,481.49609375,26.5
1727277393,481.49609375,28.7
1727277393,481.49609375,27.3
1727277395,481.49609375,28.4
1727277395,481.49609375,35.9
1727277395,481.49609375,25.6
1727277395,481.49609375,33.3
1727277396,481.49609375,28.5
1727277396,481.49609375,38.1
1727277396,481.49609375,25.3
1727277396,481.49609375,34.3
1727277397,481.5078125,28.4
1727277397,481.5078125,26.5
1727277397,481.5078125,27.8
1727277397,481.5078125,31.4
1727277399,481.5078125,28.4
1727277399,481.5078125,21.9
1727277399,481.5078125,30.2
1727277399,481.5078125,27.3
1727277400,481.5078125,28.4
1727277400,481.5078125,21.9
1727277400,481.5078125,30.6
1727277400,481.5078125,23.3
1727277401,481.5078125,28.5
1727277401,481.5078125,29.7
1727277401,481.5078125,30.3
1727277401,481.5078125,21.9
1727277403,481.5078125,28.4
1727277403,481.5078125,36.6
1727277403,481.5078125,28.6
1727277403,481.5078125,22.6
1727277404,481.5078125,28.4
1727277404,481.5078125,35.9
1727277404,481.5078125,28.1
1727277404,481.5078125,31.4
1727277405,481.5078125,28.5
1727277405,481.5078125,21.9
1727277405,481.5078125,31.2
1727277405,481.5078125,25.0
1727277406,481.5078125,28.6
1727277406,481.5078125,21.9
1727277406,481.5078125,30.4
1727277406,481.5078125,21.9
1727277408,481.5078125,28.4
1727277408,481.5078125,28.6
1727277408,481.5078125,28.7
1727277408,481.5078125,28.6
1727277409,481.5078125,28.4
1727277409,481.5078125,25.7
1727277409,481.5078125,29.0
1727277409,481.5078125,29.4
1727277410,481.5078125,28.4
1727277410,481.5078125,23.5
1727277410,481.5078125,29.8
1727277410,481.5078125,24.2
1727277412,481.5078125,28.4
1727277412,481.5078125,24.2
1727277412,481.5078125,29.9
1727277412,481.5078125,22.6
1727277413,481.5078125,28.4
1727277413,481.5078125,21.2
1727277413,481.5078125,28.7
1727277413,481.5078125,25.0
1727277414,481.5078125,28.4
1727277414,481.5078125,32.4
1727277414,481.5078125,28.6
1727277414,481.5078125,28.6
1727277416,481.51171875,28.4
1727277416,481.51171875,21.9
1727277416,481.51171875,30.5
1727277416,481.51171875,25.0
1727277417,481.51171875,28.4
1727277417,481.51171875,24.2
1727277417,481.51171875,30.2
1727277417,481.51171875,22.6
1727277418,481.51171875,28.4
1727277418,481.51171875,33.3
1727277418,481.51171875,27.2
1727277418,481.51171875,29.4
1727277420,481.51171875,28.4
1727277420,481.51171875,21.9
1727277420,481.51171875,30.9
1727277420,481.51171875,23.3
1727277421,481.51171875,29.1
1727277421,481.51171875,39.0
1727277421,481.51171875,34.0
1727277421,481.51171875,27.3
1727277422,481.51171875,31.9
1727277422,481.51171875,29.4
1727277422,481.51171875,32.3
1727277422,481.51171875,25.8
1727277424,481.51171875,29.3
1727277424,481.51171875,25.7
1727277424,481.51171875,25.8
1727277424,481.51171875,35.9
1727277425,481.51171875,26.6
1727277425,481.51171875,18.2
1727277425,481.51171875,27.3
1727277425,481.51171875,19.4
1727277426,481.51171875,25.9
1727277426,481.51171875,26.3
1727277426,481.51171875,26.5
1727277426,481.51171875,18.8
1727277427,481.51171875,25.6
1727277427,481.51171875,28.9
1727277427,481.51171875,26.3
1727277427,481.51171875,19.4
1727277429,481.51171875,25.7
1727277429,481.51171875,22.9
1727277429,481.51171875,27.0
1727277429,481.51171875,19.4
1727277430,481.51171875,25.4
1727277430,481.51171875,31.7
1727277430,481.51171875,25.7
1727277430,481.51171875,18.8
1727277431,481.51171875,23.9
1727277431,481.51171875,17.6
1727277431,481.51171875,22.9
1727277431,481.51171875,32.4
1727277436,482.9296875,22.6
1727277436,482.9296875,20.0
1727277436,482.9296875,23.2
1727277436,482.9296875,16.7
1727277437,482.9296875,22.4
1727277437,482.9296875,15.6
1727277438,482.9296875,23.7
1727277438,482.9296875,16.1
1727277439,482.9296875,22.1
1727277439,482.9296875,15.6
1727277439,482.9296875,22.0
1727277439,482.9296875,26.5
1727277442,482.9765625,23.1
1727277442,482.9765625,17.6
1727277442,482.9765625,27.3
1727277442,482.9765625,19.4
1727277444,483.0078125,25.0
1727277444,483.0078125,25.0
1727277444,483.0078125,20.6
1727277444,483.0078125,31.6
1727277446,483.0078125,25.1
1727277446,483.0078125,22.2
1727277446,483.0078125,22.7
1727277446,483.0078125,36.1
1727277448,483.0078125,24.3
1727277448,483.0078125,25.0
1727277448,483.0078125,20.0
1727277448,483.0078125,32.4
1727277450,483.0078125,22.1
1727277450,483.0078125,17.6
1727277450,483.0078125,22.2
1727277450,483.0078125,21.9
1727277451,483.0078125,22.1
1727277451,483.0078125,17.6
1727277451,483.0078125,23.5
1727277451,483.0078125,18.8
1727277453,483.0078125,22.2
1727277453,483.0078125,28.9
1727277453,483.0078125,21.2
1727277453,483.0078125,25.0
1727277455,483.0078125,22.1
1727277455,483.0078125,30.0
1727277455,483.0078125,20.8
1727277455,483.0078125,20.6
1727277457,483.0078125,22.1
1727277457,483.0078125,15.6
1727277457,483.0078125,23.8
1727277457,483.0078125,23.5
1727277459,483.0625,22.1
1727277459,483.0625,20.0
1727277459,483.0625,22.5
1727277459,483.0625,27.0
1727277461,483.0625,22.1
1727277461,483.0625,20.6
1727277461,483.0625,21.9
1727277461,483.0625,21.9
1727277463,483.0625,22.1
1727277463,483.0625,25.0
1727277463,483.0625,22.5
1727277463,483.0625,25.7
1727277465,483.0625,22.1
1727277465,483.0625,18.2
1727277465,483.0625,22.0
1727277465,483.0625,18.8
1727277466,483.0625,22.1
1727277466,483.0625,17.6
1727277466,483.0625,22.0
1727277466,483.0625,26.5
1727277468,483.0625,22.2
1727277468,483.0625,18.2
1727277468,483.0625,22.8
1727277468,483.0625,18.2
1727277470,483.0625,24.0
1727277470,483.0625,15.2
1727277470,483.0625,29.2
1727277470,483.0625,40.0
1727277472,483.0625,24.9
1727277472,483.0625,31.7
1727277472,483.0625,25.7
1727277472,483.0625,15.6
1727277474,483.0625,24.5
1727277474,483.0625,17.6
1727277474,483.0625,21.0
1727277474,483.0625,21.2
1727277476,483.0625,23.4
1727277476,483.0625,28.2
1727277476,483.0625,20.8
1727277476,483.0625,18.7
1727277478,483.0625,20.6
1727277478,483.0625,25.0
1727277478,483.0625,19.8
1727277478,483.0625,23.5
1727277480,483.0625,20.6
1727277480,483.0625,18.2
1727277480,483.0625,19.8
1727277480,483.0625,18.7
1727277481,483.0625,20.6
1727277481,483.0625,22.4
1727277481,483.0625,20.5
1727277481,483.0625,18.8
1727277483,483.0625,20.6
1727277483,483.0625,15.2
1727277483,483.0625,20.8
1727277483,483.0625,27.8
1727277485,483.0625,19.9
1727277485,483.0625,25.6
1727277485,483.0625,19.1
1727277485,483.0625,13.3
1727277487,483.0625,19.7
1727277487,483.0625,16.7
1727277487,483.0625,20.5
1727277487,483.0625,14.7
1727277489,483.0625,19.2
1727277489,483.0625,14.7
1727277489,483.0625,18.7
1727277489,483.0625,27.0
1727277491,483.0625,21.4
1727277491,483.0625,18.4
1727277491,483.0625,20.7
1727277491,483.0625,21.9
1727277493,483.06640625,22.3
1727277493,483.06640625,15.2
1727277493,483.06640625,19.0
1727277493,483.06640625,26.3
1727277494,483.06640625,22.3
1727277494,483.06640625,14.7
1727277494,483.06640625,20.2
1727277494,483.06640625,32.4
1727277496,483.06640625,19.9
1727277496,483.06640625,14.7
1727277496,483.06640625,18.3
1727277496,483.06640625,17.6
1727277498,483.06640625,19.0
1727277498,483.06640625,16.7
1727277498,483.06640625,19.6
1727277498,483.06640625,18.2
1727277500,483.06640625,19.1
1727277500,483.06640625,17.1
1727277500,483.06640625,20.2
1727277500,483.06640625,24.3
1727277502,483.06640625,20.4
1727277502,483.06640625,17.6
1727277502,483.06640625,24.5
1727277502,483.06640625,26.5
1727277504,483.06640625,22.0
1727277504,483.06640625,23.7
1727277504,483.06640625,22.1
1727277504,483.06640625,19.4
1727277506,483.06640625,21.9
1727277506,483.06640625,15.6
1727277506,483.06640625,19.6
1727277506,483.06640625,27.0
1727277507,483.06640625,21.6
1727277507,483.06640625,11.8
1727277507,483.06640625,20.4
1727277507,483.06640625,25.0
1727277510,483.06640625,19.3
1727277510,483.06640625,19.4
1727277510,483.06640625,24.8
1727277510,483.06640625,21.9
1727277511,483.07421875,22.0
1727277511,483.07421875,25.7
1727277511,483.07421875,23.4
1727277511,483.07421875,34.2
1727277513,483.07421875,22.4
1727277513,483.07421875,35.9
1727277513,483.07421875,23.9
1727277513,483.07421875,16.1
1727277515,483.07421875,22.4
1727277515,483.07421875,15.2
1727277515,483.07421875,21.7
1727277515,483.07421875,21.9
1727277517,483.07421875,19.5
1727277517,483.07421875,21.6
1727277517,483.07421875,18.3
1727277517,483.07421875,16.1
1727277519,483.07421875,19.0
1727277519,483.07421875,15.2
1727277519,483.07421875,18.1
1727277519,483.07421875,24.3
1727277521,483.07421875,19.0
1727277521,483.07421875,21.6
1727277521,483.07421875,19.6
1727277521,483.07421875,16.7
1727277523,483.07421875,19.0
1727277523,483.07421875,23.7
1727277523,483.07421875,18.3
1727277523,483.07421875,18.9
1727277525,483.07421875,19.0
1727277525,483.07421875,14.7
1727277525,483.07421875,20.0
1727277525,483.07421875,22.9
1727277526,483.07421875,19.0
1727277526,483.07421875,16.7
1727277526,483.07421875,20.0
1727277526,483.07421875,15.6
1727277528,483.07421875,19.0
1727277528,483.07421875,20.0
1727277528,483.07421875,19.8
1727277528,483.07421875,15.2
1727277530,483.07421875,19.1
1727277530,483.07421875,23.7
1727277530,483.07421875,18.0
1727277530,483.07421875,20.0
1727277532,483.07421875,22.0
1727277532,483.07421875,35.7
1727277532,483.07421875,22.7
1727277532,483.07421875,35.0
1727277534,483.07421875,25.3
1727277534,483.07421875,15.2
1727277534,483.07421875,18.2
1727277534,483.07421875,34.2
1727277536,483.08203125,24.9
1727277536,483.08203125,29.4
1727277536,483.08203125,27.0
1727277536,483.08203125,35.1
1727277538,483.08203125,23.2
1727277538,483.08203125,15.2
1727277538,483.08203125,18.2
1727277538,483.08203125,25.0
1727277540,483.08203125,19.0
1727277540,483.08203125,23.7
1727277540,483.08203125,19.3
1727277540,483.08203125,18.2
1727277541,483.08203125,19.0
1727277541,483.08203125,24.3
1727277541,483.08203125,19.3
1727277541,483.08203125,18.2
1727277543,483.08203125,19.1
1727277543,483.08203125,23.7
1727277543,483.08203125,17.1
1727277543,483.08203125,25.0
1727277545,483.08203125,19.0
1727277545,483.08203125,18.5
1727277545,483.08203125,19.8
1727277545,483.08203125,15.2
1727277547,483.08203125,19.0
1727277547,483.08203125,21.6
1727277547,483.08203125,17.3
1727277547,483.08203125,25.0
1727277549,483.08203125,19.0
1727277549,483.08203125,14.7
1727277549,483.08203125,19.1
1727277549,483.08203125,18.8
1727277551,483.08203125,19.0
1727277551,483.08203125,23.7
1727277551,483.08203125,17.3
1727277551,483.08203125,18.2
1727277552,483.08203125,19.0
1727277552,483.08203125,11.8
1727277552,483.08203125,20.4
1727277552,483.08203125,20.0
1727277554,483.08203125,19.0
1727277554,483.08203125,22.2
1727277554,483.08203125,20.2
1727277554,483.08203125,15.6
1727277556,483.08203125,19.0
1727277556,483.08203125,21.6
1727277556,483.08203125,17.1
1727277556,483.08203125,22.9
1727277558,483.08203125,19.1
1727277558,483.08203125,15.2
1727277558,483.08203125,17.4
1727277558,483.08203125,24.3
1727277560,483.08203125,19.0
1727277560,483.08203125,14.3
1727277560,483.08203125,18.3
1727277560,483.08203125,27.0
1727277562,483.0859375,20.6
1727277562,483.0859375,18.7
1727277562,483.0859375,26.0
1727277562,483.0859375,15.6
1727277564,483.0859375,24.7
1727277564,483.0859375,23.5
1727277564,483.0859375,25.2
1727277564,483.0859375,18.7
1727277566,483.0859375,24.7
1727277566,483.0859375,22.6
1727277566,483.0859375,33.1
1727277566,483.0859375,16.7
1727277567,483.0859375,25.0
1727277567,483.0859375,32.6
1727277567,483.0859375,27.6
1727277567,483.0859375,36.8
1727277569,483.0859375,22.3
1727277569,483.0859375,28.9
1727277569,483.0859375,19.8
1727277569,483.0859375,16.1
1727277571,483.0859375,30.9
1727277571,483.0859375,30.3
1727277571,483.0859375,40.7
1727277571,483.0859375,30.0
1727277573,483.0859375,37.7
1727277573,483.0859375,29.0
1727277573,483.0859375,38.9
1727277573,483.0859375,30.0
1727277575,483.0859375,37.7
1727277575,483.0859375,45.0
1727277575,483.0859375,39.1
1727277575,483.0859375,29.0
1727277577,483.0859375,37.7
1727277577,483.0859375,36.4
1727277577,483.0859375,37.6
1727277577,483.0859375,47.5
1727277579,483.0859375,37.7
1727277579,483.0859375,46.3
1727277579,483.0859375,34.5
1727277579,483.0859375,48.8
1727277581,483.0859375,37.7
1727277581,483.0859375,31.2
1727277581,483.0859375,38.9
1727277581,483.0859375,32.3
1727277583,483.0859375,37.7
1727277583,483.0859375,31.2
1727277583,483.0859375,38.4
1727277583,483.0859375,46.2
1727277585,483.0859375,37.7
1727277585,483.0859375,42.5
1727277585,483.0859375,39.6
1727277585,483.0859375,32.3
1727277586,483.0859375,37.7
1727277586,483.0859375,37.8
1727277586,483.0859375,37.4
1727277586,483.0859375,47.5
1727277588,483.0859375,37.7
1727277588,483.0859375,31.2
1727277588,483.0859375,37.6
1727277588,483.0859375,40.0
1727277590,483.0859375,37.7
1727277590,483.0859375,30.3
1727277590,483.0859375,38.1
1727277590,483.0859375,38.2
1727277592,483.0859375,37.8
1727277592,483.0859375,30.3
1727277592,483.0859375,38.0
1727277592,483.0859375,36.4
1727277594,483.0859375,37.8
1727277594,483.0859375,28.1
1727277594,483.0859375,37.5
1727277594,483.0859375,45.0
1727277596,483.0859375,37.7
1727277596,483.0859375,31.3
1727277596,483.0859375,37.8
1727277596,483.0859375,38.2
1727277598,483.0859375,36.9
1727277598,483.0859375,24.2
1727277598,483.0859375,35.2
1727277598,483.0859375,26.7
1727277600,483.0859375,32.5
1727277600,483.0859375,24.2
1727277600,483.0859375,31.8
1727277600,483.0859375,39.5
1727277602,483.0859375,31.5
1727277602,483.0859375,34.2
1727277602,483.0859375,30.2
1727277602,483.0859375,25.8
1727277604,483.0859375,29.8
1727277604,483.0859375,25.0
1727277604,483.0859375,31.7
1727277604,483.0859375,22.6
1727277606,483.0859375,29.1
1727277606,483.0859375,24.3
1727277606,483.0859375,29.0
1727277606,483.0859375,30.6
1727277608,483.0859375,28.2
1727277608,483.0859375,29.7
1727277608,483.0859375,28.7
1727277608,483.0859375,21.9
1727277610,483.0859375,29.0
1727277610,483.0859375,41.5
1727277610,483.0859375,33.8
1727277610,483.0859375,29.0
1727277612,483.0859375,33.1
1727277612,483.0859375,32.4
1727277612,483.0859375,31.7
1727277612,483.0859375,27.3
1727277613,483.0859375,30.5
1727277613,483.0859375,45.0
1727277613,483.0859375,38.3
1727277613,483.0859375,30.0
1727277615,483.0859375,37.7
1727277615,483.0859375,32.4
1727277615,483.0859375,37.9
1727277615,483.0859375,36.4
1727277617,483.0859375,37.7
1727277617,483.0859375,33.3
1727277617,483.0859375,38.1
1727277617,483.0859375,38.9
1727277619,483.0859375,37.7
1727277619,483.0859375,42.5
1727277619,483.0859375,38.3
1727277619,483.0859375,31.2
1727277621,483.0859375,37.7
1727277621,483.0859375,45.0
1727277621,483.0859375,37.5
1727277621,483.0859375,31.2
1727277623,483.0859375,37.7
1727277623,483.0859375,45.2
1727277623,483.0859375,37.6
1727277623,483.0859375,32.3
1727277625,483.0859375,37.8
1727277625,483.0859375,45.5
1727277625,483.0859375,37.2
1727277625,483.0859375,32.3
1727277627,483.0859375,37.7
1727277627,483.0859375,40.5
1727277627,483.0859375,39.8
1727277627,483.0859375,30.0
1727277629,483.0859375,37.7
1727277629,483.0859375,35.3
1727277629,483.0859375,38.2
1727277629,483.0859375,41.7
1727277631,483.0859375,37.8
1727277631,483.0859375,33.3
1727277631,483.0859375,37.3
1727277631,483.0859375,50.0
1727277633,483.0859375,37.7
1727277633,483.0859375,28.1
1727277633,483.0859375,40.1
1727277633,483.0859375,40.5
1727277635,483.0859375,37.7
1727277635,483.0859375,41.0
1727277635,483.0859375,37.5
1727277635,483.0859375,34.4
1727277636,483.0859375,37.7
1727277636,483.0859375,45.2
1727277636,483.0859375,36.6
1727277636,483.0859375,31.3
1727277638,483.0859375,37.7
1727277638,483.0859375,31.3
1727277638,483.0859375,38.3
1727277638,483.0859375,36.4
1727277640,483.0859375,37.7
1727277640,483.0859375,46.3
1727277640,483.0859375,36.9
1727277640,483.0859375,31.2
1727277642,483.0859375,37.7
1727277642,483.0859375,28.1
1727277642,483.0859375,38.0
1727277642,483.0859375,38.2
1727277644,483.0859375,37.7
1727277644,483.0859375,29.0
1727277644,483.0859375,38.2
1727277644,483.0859375,44.7
1727277646,483.0859375,37.7
1727277646,483.0859375,30.3
1727277646,483.0859375,37.8
1727277646,483.0859375,31.3
1727277648,483.0859375,37.7
1727277648,483.0859375,37.1
1727277648,483.0859375,40.0
1727277648,483.0859375,33.3
1727277650,483.0859375,35.7
1727277650,483.0859375,37.8
1727277650,483.0859375,34.9
1727277650,483.0859375,29.0
1727277652,483.0859375,35.0
1727277652,483.0859375,40.0
1727277652,483.0859375,34.1
1727277652,483.0859375,33.3
1727277654,483.0859375,34.6
1727277654,483.0859375,37.8
1727277654,483.0859375,35.3
1727277654,483.0859375,28.1
1727277656,483.0859375,34.6
1727277656,483.0859375,41.5
1727277656,483.0859375,34.1
1727277656,483.0859375,29.0
1727277658,483.0859375,34.6
1727277658,483.0859375,34.4
1727277658,483.0859375,34.7
1727277658,483.0859375,29.0
1727277660,483.0859375,34.7
1727277660,483.0859375,25.0
1727277660,483.0859375,33.8
1727277660,483.0859375,45.2
1727277662,483.0859375,34.6
1727277662,483.0859375,34.3
1727277662,483.0859375,35.4
1727277662,483.0859375,34.0
1727277664,483.34375,34.6
1727277664,483.34375,40.5
1727277664,483.34375,34.0
1727277664,483.34375,28.1
1727277666,483.34375,34.6
1727277666,483.34375,41.9
1727277666,483.34375,33.8
1727277666,483.34375,40.0
1727277668,483.34375,34.6
1727277668,483.34375,35.1
1727277668,483.34375,35.9
1727277668,483.34375,25.8
1727277670,483.34375,34.6
1727277670,483.34375,42.9
1727277670,483.34375,33.3
1727277670,483.34375,38.9
1727277672,483.34375,34.6
1727277672,483.34375,40.5
1727277672,483.34375,33.3
1727277672,483.34375,35.3
1727277674,483.34375,34.6
1727277674,483.34375,28.1
1727277674,483.34375,33.6
1727277674,483.34375,46.5
1727277676,483.34375,34.6
1727277676,483.34375,42.9
1727277676,483.34375,34.4
1727277676,483.34375,31.3
1727277678,483.34375,34.6
1727277678,483.34375,27.3
1727277678,483.34375,34.8
1727277678,483.34375,41.0
1727277680,483.34375,34.6
1727277680,483.34375,38.5
1727277680,483.34375,33.6
1727277680,483.34375,43.9
1727277681,483.34375,34.6
1727277681,483.34375,41.5
1727277681,483.34375,34.8
1727277681,483.34375,28.1
1727277683,483.34375,34.6
1727277683,483.34375,34.3
1727277683,483.34375,36.2
1727277683,483.34375,25.8
1727277686,483.38671875,34.6
1727277686,483.38671875,41.0
1727277686,483.38671875,34.1
1727277686,483.38671875,29.0
1727277688,483.38671875,34.6
1727277688,483.38671875,28.1
1727277688,483.38671875,37.3
1727277688,483.38671875,28.1
1727277689,483.38671875,34.6
1727277689,483.38671875,28.1
1727277689,483.38671875,33.3
1727277689,483.38671875,46.3
1727277691,483.38671875,34.6
1727277691,483.38671875,42.5
1727277691,483.38671875,33.3
1727277691,483.38671875,28.1
1727277693,483.38671875,34.6
1727277693,483.38671875,41.5
1727277693,483.38671875,32.8
1727277693,483.38671875,38.6
1727277695,483.38671875,34.6
1727277695,483.38671875,27.3
1727277695,483.38671875,35.0
1727277695,483.38671875,37.8
1727277697,483.38671875,34.6
1727277697,483.38671875,33.3
1727277697,483.38671875,32.8
1727277697,483.38671875,43.9
1727277699,483.38671875,34.6
1727277699,483.38671875,43.9
1727277699,483.38671875,33.6
1727277699,483.38671875,26.7
1727277701,483.38671875,34.6
1727277701,483.38671875,36.8
1727277701,483.38671875,32.8
1727277701,483.38671875,43.9
1727277703,483.38671875,34.6
1727277703,483.38671875,39.5
1727277703,483.38671875,33.8
1727277703,483.38671875,43.6
1727277705,483.390625,34.6
1727277705,483.390625,28.1
1727277705,483.390625,36.4
1727277705,483.390625,31.3
1727277707,483.390625,34.6
1727277707,483.390625,43.9
1727277707,483.390625,32.8
1727277707,483.390625,37.8
1727277709,483.390625,34.6
1727277709,483.390625,41.5
1727277709,483.390625,33.1
1727277709,483.390625,29.0
1727277710,483.390625,34.6
1727277710,483.390625,42.9
1727277710,483.390625,33.6
1727277710,483.390625,35.3
1727277712,483.390625,34.6
1727277712,483.390625,27.3
1727277712,483.390625,37.0
1727277712,483.390625,25.8
1727277714,483.390625,34.6
1727277714,483.390625,25.8
1727277714,483.390625,33.6
1727277714,483.390625,46.3
1727277716,483.390625,34.6
1727277716,483.390625,42.9
1727277716,483.390625,33.3
1727277716,483.390625,32.4
1727277718,483.390625,34.6
1727277718,483.390625,29.4
1727277718,483.390625,36.1
1727277718,483.390625,29.0
1727277720,483.390625,34.7
1727277720,483.390625,25.8
1727277720,483.390625,34.1
1727277720,483.390625,40.5
1727277722,483.390625,34.6
1727277722,483.390625,39.5
1727277722,483.390625,33.6
1727277722,483.390625,41.0
1727277724,483.390625,34.6
1727277724,483.390625,28.1
1727277724,483.390625,36.6
1727277724,483.390625,26.7
1727277726,483.390625,34.6
1727277726,483.390625,27.3
1727277726,483.390625,36.7
1727277726,483.390625,26.7
1727277728,483.390625,34.6
1727277728,483.390625,25.8
1727277728,483.390625,36.9
1727277728,483.390625,29.0
1727277730,483.390625,34.6
1727277730,483.390625,42.5
1727277730,483.390625,33.1
1727277730,483.390625,40.5
1727277732,483.390625,34.6
1727277732,483.390625,28.1
1727277732,483.390625,35.5
1727277732,483.390625,31.2
1727277734,483.390625,34.6
1727277734,483.390625,42.5
1727277734,483.390625,32.4
1727277734,483.390625,39.5
1727277736,483.390625,34.6
1727277736,483.390625,36.8
1727277736,483.390625,33.3
1727277736,483.390625,42.1
1727277737,483.390625,34.6
1727277737,483.390625,44.2
1727277737,483.390625,32.4
1727277737,483.390625,41.0
1727277739,483.390625,34.6
1727277739,483.390625,45.2
1727277739,483.390625,31.9
1727277739,483.390625,45.0
1727277741,483.390625,34.6
1727277741,483.390625,28.1
1727277741,483.390625,35.3
1727277741,483.390625,34.3
1727277743,483.390625,34.6
1727277743,483.390625,41.0
1727277743,483.390625,32.8
1727277743,483.390625,43.9
1727277745,483.390625,34.6
1727277745,483.390625,42.9
1727277745,483.390625,32.4
1727277745,483.390625,40.5
1727277747,483.390625,34.6
1727277747,483.390625,41.5
1727277747,483.390625,32.4
1727277747,483.390625,43.9
1727277749,483.390625,34.6
1727277749,483.390625,41.5
1727277749,483.390625,32.8
1727277749,483.390625,37.1
1727277751,483.390625,34.6
1727277751,483.390625,32.4
1727277751,483.390625,33.8
1727277751,483.390625,38.9
1727277753,483.390625,34.6
1727277753,483.390625,39.5
1727277753,483.390625,34.7
1727277753,483.390625,35.3
1727277755,483.390625,34.6
1727277755,483.390625,25.0
1727277755,483.390625,36.4
1727277755,483.390625,31.2
1727277756,483.41796875,34.6
1727277756,483.41796875,42.9
1727277756,483.41796875,32.7
1727277756,483.41796875,38.9
1727277758,483.41796875,34.6
1727277758,483.41796875,29.4
1727277758,483.41796875,36.1
1727277758,483.41796875,28.1
1727277760,483.41796875,34.6
1727277760,483.41796875,43.9
1727277760,483.41796875,32.1
1727277760,483.41796875,45.0
1727277762,483.41796875,34.6
1727277762,483.41796875,44.2
1727277762,483.41796875,35.1
1727277762,483.41796875,28.1
1727277764,483.41796875,34.6
1727277764,483.41796875,39.5
1727277764,483.41796875,34.5
1727277764,483.41796875,36.1
1727277766,483.41796875,34.6
1727277766,483.41796875,33.3
1727277766,483.41796875,35.0
1727277766,483.41796875,34.3
1727277768,483.41796875,34.6
1727277768,483.41796875,25.8
1727277768,483.41796875,35.6
1727277768,483.41796875,29.4
1727277770,483.41796875,34.6
1727277770,483.41796875,34.3
1727277770,483.41796875,36.0
1727277770,483.41796875,28.1
1727277772,483.41796875,34.6
1727277772,483.41796875,36.8
1727277772,483.41796875,34.0
1727277772,483.41796875,38.9
1727277774,483.41796875,34.6
1727277774,483.41796875,38.5
1727277774,483.41796875,35.6
1727277774,483.41796875,25.8
1727277776,483.41796875,34.6
1727277776,483.41796875,40.9
1727277776,483.41796875,33.1
1727277776,483.41796875,36.1
1727277777,483.41796875,34.6
1727277777,483.41796875,26.5
1727277777,483.41796875,34.4
1727277777,483.41796875,43.6
1727277779,483.41796875,34.6
1727277779,483.41796875,27.3
1727277779,483.41796875,36.2
1727277779,483.41796875,29.0
1727277781,483.41796875,34.7
1727277781,483.41796875,24.2
1727277781,483.41796875,36.0
1727277781,483.41796875,29.0
1727277783,483.41796875,34.6
1727277783,483.41796875,28.1
1727277783,483.41796875,35.4
1727277783,483.41796875,29.0
1727277785,483.41796875,34.6
1727277785,483.41796875,29.4
1727277785,483.41796875,34.4
1727277785,483.41796875,29.0
1727277787,483.41796875,34.6
1727277787,483.41796875,41.5
1727277787,483.41796875,35.1
1727277787,483.41796875,29.0
1727277789,483.41796875,34.6
1727277789,483.41796875,38.5
1727277789,483.41796875,35.6
1727277789,483.41796875,30.3
1727277791,483.41796875,34.6
1727277791,483.41796875,27.3
1727277791,483.41796875,34.5
1727277791,483.41796875,37.8
1727277793,483.41796875,34.6
1727277793,483.41796875,28.1
1727277793,483.41796875,34.9
1727277793,483.41796875,25.8
1727277795,483.41796875,34.6
1727277795,483.41796875,25.8
1727277795,483.41796875,35.5
1727277795,483.41796875,29.0
1727277797,483.41796875,34.6
1727277797,483.41796875,29.4
1727277797,483.41796875,35.1
1727277797,483.41796875,43.6
1727277798,483.41796875,34.6
1727277798,483.41796875,34.3
1727277798,483.41796875,33.8
1727277798,483.41796875,45.0
1727277800,483.41796875,34.6
1727277800,483.41796875,28.1
1727277800,483.41796875,36.2
1727277800,483.41796875,28.1
1727277802,483.41796875,34.6
1727277802,483.41796875,27.3
1727277802,483.41796875,34.6
1727277802,483.41796875,38.9
1727277804,483.41796875,33.0
1727277804,483.41796875,30.8
1727277804,483.41796875,30.3
1727277804,483.41796875,34.2
1727277806,483.41796875,29.5
1727277806,483.41796875,32.4
1727277806,483.41796875,29.8
1727277806,483.41796875,21.9
1727277808,483.41796875,29.1
1727277808,483.41796875,48.8
1727277808,483.41796875,37.9
1727277808,483.41796875,30.0
1727277810,483.41796875,28.8
1727277810,483.41796875,37.5
1727277810,483.41796875,29.4
1727277810,483.41796875,23.3
1727277812,483.41796875,28.4
1727277812,483.41796875,21.9
1727277812,483.41796875,29.3
1727277812,483.41796875,22.6
1727277951,525.4765625,29.1
1727277951,525.4765625,25.7
1727277951,525.4765625,32.2
1727277951,525.4765625,26.5
1727278062,529.65625,36.7
1727278062,529.65625,29.4
1727278062,529.65625,39.4
1727278062,529.65625,42.1
1727278205,535.22265625,33.2
1727278205,535.22265625,36.8
1727278206,535.22265625,41.3
1727278206,535.22265625,29.0
1727278362,539.8515625,38.7
1727278362,539.8515625,47.6
1727278362,539.8515625,37.6
1727278362,539.8515625,31.3
1727278550,539.30078125,35.8
1727278550,539.30078125,48.9
1727278550,539.30078125,37.8
1727278550,539.30078125,45.0
1727278763,544.359375,38.4
1727278763,544.359375,45.2
1727278763,544.359375,37.2
1727278763,544.359375,41.7
1727278983,544.77734375,30.4
1727278983,544.77734375,13.9
1727278983,544.77734375,18.4
1727278983,544.77734375,19.4
1727279230,542.5859375,24.2
1727279230,542.5859375,21.2
1727279230,542.5859375,30.6
1727279230,542.5859375,21.2
1727279491,546.12109375,23.0
1727279491,546.12109375,24.3
1727279491,546.12109375,21.0
1727279491,546.12109375,17.6
1727279803,554.23046875,28.5
1727279803,554.23046875,21.1
1727279803,554.23046875,19.1
1727279803,554.23046875,14.7
1727280088,553.44921875,28.1
1727280088,553.44921875,35.4
1727280088,553.44921875,33.5
1727280088,553.44921875,41.0
1727280390,554.87109375,35.0
1727280390,554.87109375,37.1
1727280390,554.87109375,34.9
1727280390,554.87109375,30.0
1727280725,558.93359375,35.0
1727280725,558.93359375,38.5
1727280725,558.93359375,35.2
1727280725,558.93359375,31.2
1727281068,556.09375,33.8
1727281068,556.09375,27.3
1727281068,556.09375,36.6
1727281068,556.09375,25.8
1727281445,529.3515625,35.0
1727281445,529.3515625,25.7
1727281445,529.3515625,36.7
1727281445,529.3515625,29.0
1727281825,520.4921875,34.9
1727281825,520.4921875,27.3
1727281825,520.4921875,34.6
1727281825,520.4921875,42.9
1727282154,521.33203125,34.0
1727282154,521.33203125,40.5
1727282154,521.33203125,35.2
1727282154,521.33203125,29.0
1727282500,522.37890625,35.0
1727282500,522.37890625,27.3
1727282500,522.37890625,34.3
1727282500,522.37890625,45.2
1727282853,522.74609375,34.3
1727282853,522.74609375,38.3
1727282853,522.74609375,33.2
1727282853,522.74609375,45.0
1727283240,523.9296875,33.9
1727283240,523.9296875,29.4
1727283240,523.9296875,36.1
1727283240,523.9296875,35.3
1727283643,535.49609375,34.9
1727283643,535.49609375,45.2
1727283643,535.49609375,34.1
1727283643,535.49609375,34.3
1727284042,529.44140625,31.2
1727284042,529.44140625,36.8
1727284042,529.44140625,34.6
1727284042,529.44140625,36.1
1727284436,561.71484375,35.0
1727284436,561.71484375,28.1
1727284436,561.71484375,34.6
1727284436,561.71484375,42.5
1727284851,548.63671875,34.3
1727284851,548.63671875,27.3
1727284851,548.63671875,35.6
1727284851,548.63671875,26.7
1727285424,561.6171875,34.3
1727285424,561.6171875,40.5
1727285424,561.6171875,34.1
1727285424,561.6171875,44.7
1727285965,564.18359375,32.2
1727285965,564.18359375,27.3
1727285965,564.18359375,35.0
1727285965,564.18359375,36.8
1727286566,562.65625,35.1
1727286566,562.65625,30.3
1727286566,562.65625,34.6
1727286566,562.65625,34.7
1727287178,548.63671875,33.2
1727287178,548.63671875,39.5
1727287178,548.63671875,35.5
1727287178,548.63671875,31.3
1727287749,560.37890625,33.6
1727287749,560.37890625,32.4
1727287764,560.39453125,34.9
1727287764,560.39453125,28.1
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download »cpu_ram_usage.csv« as file</button>
<h1> Parallel Plot</h1>
<div class="invert_in_dark_mode" id="parallel-plot"></div>
<h1> Job Status Distribution</h1>
<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
<h1> Boxplots</h1>
<div class="invert_in_dark_mode" id="plotBoxplot"></div>
<h1> Violin</h1>
<div class="invert_in_dark_mode" id="plotViolin"></div>
<h1> Histogram</h1>
<div class="invert_in_dark_mode" id="plotHistogram"></div>
<h1> Heatmap</h1>
<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
<h1>Correlation Heatmap Explanation</h1>
<p>
This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
</p>
<h2>How It Works</h2>
<p>
The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
</p>
<ul>
<li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
<li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
<li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
</ul>
<h2>Color Scale: Yellow to Purple (Viridis)</h2>
<p>
The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
</p>
<ul>
<li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
<li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
<li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
<li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
</ul>
<h2>What the Heatmap Shows</h2>
<p>
In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
</p>
<h1> Exit-Codes</h1>
<div class="invert_in_dark_mode" id="plotExitCodesPieChart"></div>
</body>
</html>