При использовании функции xgb_cv_opt из пакета MlBayesOpt в R я получаю следующую ошибку после четырех раундов.
Error in GP_deviance(param_init_200d[i, ], X, Y, nug_thres, corr = corr) :
Infinite values of the Deviance Function,
unable to find optimum parameters
Я обнаружил эту открытую проблему Github , говоря об одной и той же ошибке в пакете rbayesianoptimization, и попытался поиграться с диапазонами параметров, но все еще не может запустить оптимизацию.
Воспроизводимый пример ниже.
df_example <- read.csv(textConnection("V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,V21,V22,V23,V24,V25,V26,V27,V28,V29,V30,V31,V32,V33,V34,V35,V36,V37,V38,V39,V40,V41,V42,V43,V44,V45,V46,V47,V48,V49,V50,V51,V52,V53,V54,V55,V56,V57,V58,V59,V60,V61,V62,V63,V64,V65,V66,V67,V68,V69,V70,V71,V72,V73,y
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-2.165635439,-0.362509685,-0.22656054,0,0,0,0,0,0,0,-0.890515214,-0.068217098,0,0,1,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0
-0.923959807,3.916687725,5.365929007,0,0,0,0,0,0,1,-0.755772905,-0.068217098,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1
1.027244757,-0.362509685,8.585847231,0,0,0,0,0,0,0,-0.719158148,-0.068217098,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0
0.140333592,6.056286429,-0.22656054,0,0,0,0,0,0,1,1.407426982,-0.068217098,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1
-0.923959807,-0.362509685,-0.22656054,0,0,0,0,0,0,0,-0.924200791,-0.068217098,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0
1.027244757,-0.362509685,-0.22656054,0,0,0,1,0,0,0,-0.995965716,-0.068217098,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1
0.140333592,-0.362509685,-0.14182585,0,0,0,0,0,0,0,-0.183118094,-0.068217098,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0
0.140333592,-0.362509685,-0.22656054,0,0,0,0,0,0,0,-0.042517425,-0.068217098,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0
0.140333592,-0.362509685,-0.22656054,0,0,0,0,0,0,1,0.077578981,-0.068217098,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1"
))
res0 <- xgb_cv_opt(data = df_example,
label = y,
objectfun = "binary:logistic",
evalmetric = "auc",
n_folds = 3,
classes = 10,
init_points = 4,
n_iter = 5)