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[R-package] a few fixes for R (#1485)

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* [R] fix #1465

* [R] add sanity check to fix #1434

* [R] some clean-ups for custom obj&eval; require maximize only for early stopping
This commit is contained in:
Vadim Khotilovich
2016-08-20 05:09:03 -05:00
committed by Yuan (Terry) Tang
parent b8e6551734
commit bdfa8c0e09
8 changed files with 87 additions and 28 deletions
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28
R-package/R/xgb.train.R
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@@ -173,9 +173,10 @@
#' watchlist <- list(eval = dtest, train = dtrain)
#'
#' ## A simple xgb.train example:
#' param <- list(max_depth = 2, eta = 1, silent = 1,
#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
#' objective = "binary:logistic", eval_metric = "auc")
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist)
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
#'
#'
#' ## An xgb.train example where custom objective and evaluation metric are used:
#' logregobj <- function(preds, dtrain) {
@@ -190,16 +191,33 @@
#' err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
#' return(list(metric = "error", value = err))
#' }
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist)
#'
#' # These functions could be used by passing them either:
#' # as 'objective' and 'eval_metric' parameters in the params list:
#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
#' objective = logregobj, eval_metric = evalerror)
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
#'
#' # or through the ... arguments:
#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#' objective = logregobj, eval_metric = evalerror)
#'
#' # or as dedicated 'obj' and 'feval' parameters of xgb.train:
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#' obj = logregobj, feval = evalerror)
#'
#'
#' ## An xgb.train example of using variable learning rates at each iteration:
#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
#' my_etas <- list(eta = c(0.5, 0.1))
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist,
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#' callbacks = list(cb.reset.parameters(my_etas)))
#'
#'
#' ## Explicit use of the cb.evaluation.log callback allows to run
#' ## xgb.train silently but still store the evaluation results:
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist,
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#' verbose = 0, callbacks = list(cb.evaluation.log()))
#' print(bst$evaluation_log)
#'