xgboost w R: jak xgb.cv przekaż optymalne parametry do xgb.pociąg

To dobre pytanie i świetna odpowiedź od silo z mnóstwem szczegółów! Uważam, że to bardzo pomocne dla kogoś nowego xgboost Jak ja. Dziękuję. Metoda randomizacji i porównania do granicy jest bardzo inspirująca. Dobry w użyciu i dobrze wiedzieć. Teraz w 2018 r. potrzebne są niewielkie zmiany, na przykład early.stop.round powinno być early_stopping_rounds. Wyjście mdcv jest zorganizowane nieco inaczej:

  min_rmse_index  <-  mdcv$best_iteration
  min_rmse <-  mdcv$evaluation_log[min_rmse_index]$test_rmse_mean

I zależy od zastosowania (liniowe, logistyczne itp...),objective, eval_metric i parametry są odpowiednio dostosowywane.

Dla wygody każdego, kto uruchamia regresję, oto lekko dostosowana wersja kodu (większość jest taka sama jak powyżej).

library(xgboost)
# Matrix for xgb: dtrain and dtest, "label" is the dependent variable
dtrain <- xgb.DMatrix(X_train, label = Y_train)
dtest <- xgb.DMatrix(X_test, label = Y_test)

best_param <- list()
best_seednumber <- 1234
best_rmse <- Inf
best_rmse_index <- 0

set.seed(123)
for (iter in 1:100) {
  param <- list(objective = "reg:linear",
                eval_metric = "rmse",
                max_depth = sample(6:10, 1),
                eta = runif(1, .01, .3), # Learning rate, default: 0.3
                subsample = runif(1, .6, .9),
                colsample_bytree = runif(1, .5, .8), 
                min_child_weight = sample(1:40, 1),
                max_delta_step = sample(1:10, 1)
  )
  cv.nround <-  1000
  cv.nfold <-  5 # 5-fold cross-validation
  seed.number  <-  sample.int(10000, 1) # set seed for the cv
  set.seed(seed.number)
  mdcv <- xgb.cv(data = dtrain, params = param,  
                 nfold = cv.nfold, nrounds = cv.nround,
                 verbose = F, early_stopping_rounds = 8, maximize = FALSE)

  min_rmse_index  <-  mdcv$best_iteration
  min_rmse <-  mdcv$evaluation_log[min_rmse_index]$test_rmse_mean

  if (min_rmse < best_rmse) {
    best_rmse <- min_rmse
    best_rmse_index <- min_rmse_index
    best_seednumber <- seed.number
    best_param <- param
  }
}

# The best index (min_rmse_index) is the best "nround" in the model
nround = best_rmse_index
set.seed(best_seednumber)
xg_mod <- xgboost(data = dtest, params = best_param, nround = nround, verbose = F)

# Check error in testing data
yhat_xg <- predict(xg_mod, dtest)
(MSE_xgb <- mean((yhat_xg - Y_test)^2))

Okazało się, że odpowiedź silo jest bardzo pomocna. Oprócz jego podejścia do badań losowych, możesz użyć optymalizacji bayesowskiej, aby ułatwić proces wyszukiwania hiperparametrów, np. rbayesianoptimization library . Poniżej znajduje się mój kod z biblioteką rbayesianoptimization.

cv_folds <- KFold(dataFTR$isPreIctalTrain, nfolds = 5, stratified = FALSE, seed = seedNum)
xgb_cv_bayes <- function(nround,max.depth, min_child_weight, subsample,eta,gamma,colsample_bytree,max_delta_step) {
param<-list(booster = "gbtree",
            max_depth = max.depth,
            min_child_weight = min_child_weight,
            eta=eta,gamma=gamma,
            subsample = subsample, colsample_bytree = colsample_bytree,
            max_delta_step=max_delta_step,
            lambda = 1, alpha = 0,
            objective = "binary:logistic",
            eval_metric = "auc")
cv <- xgb.cv(params = param, data = dtrain, folds = cv_folds,nrounds = 1000,early_stopping_rounds = 10, maximize = TRUE, verbose = verbose)

list(Score = cv$evaluation_log$test_auc_mean[cv$best_iteration],
     Pred=cv$best_iteration)
# we don't need cross-validation prediction and we need the number of rounds.
# a workaround is to pass the number of rounds(best_iteration) to the Pred, which is a default parameter in the rbayesianoptimization library.
}
OPT_Res <- BayesianOptimization(xgb_cv_bayes,
                              bounds = list(max.depth =c(3L, 10L),min_child_weight = c(1L, 40L),
                                            subsample = c(0.6, 0.9),
                                            eta=c(0.01,0.3),gamma = c(0.0, 0.2),
                                            colsample_bytree=c(0.5,0.8),max_delta_step=c(1L,10L)),
                              init_grid_dt = NULL, init_points = 10, n_iter = 10,
                              acq = "ucb", kappa = 2.576, eps = 0.0,
                              verbose = verbose)
best_param <- list(
booster = "gbtree",
eval.metric = "auc",
objective = "binary:logistic",
max_depth = OPT_Res$Best_Par["max.depth"],
eta = OPT_Res$Best_Par["eta"],
gamma = OPT_Res$Best_Par["gamma"],
subsample = OPT_Res$Best_Par["subsample"],
colsample_bytree = OPT_Res$Best_Par["colsample_bytree"],
min_child_weight = OPT_Res$Best_Par["min_child_weight"],
max_delta_step = OPT_Res$Best_Par["max_delta_step"])
# number of rounds should be tuned using CV
#https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/beginners-tutorial-on-xgboost-parameter-tuning-r/tutorial/
# However, nrounds can not be directly derivied from the bayesianoptimization function
# Here, OPT_Res$Pred, which was supposed to be used for cross-validation, is used to record the number of rounds
nrounds=OPT_Res$Pred[[which.max(OPT_Res$History$Value)]]
xgb_model <- xgb.train (params = best_param, data = dtrain, nrounds = nrounds)