require(xgboost)

data(agaricus.train)
data(agaricus.test)

trainX = agaricus.train$data
trainY = agaricus.train$label
testX = agaricus.test$data
testY = agaricus.test$label

dtrain <- xgb.DMatrix(trainX, label=trainY)
dtest <- xgb.DMatrix(testX, label=testY)

# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param <- list(max_depth=2,eta=1,silent=1)
watchlist <- list(eval = dtest, train = dtrain)
num_round <- 2


# user define objective function, given prediction, return gradient and second order gradient
# this is loglikelihood loss
logregobj <- function(preds, dtrain) {
  labels <- getinfo(dtrain, "label")
  preds <- 1/(1 + exp(-preds))
  grad <- preds - labels
  hess <- preds * (1 - preds)
  return(list(grad = grad, hess = hess))
}

# user defined evaluation function, return a pair metric_name, result
# NOTE: when you do customized loss function, the default prediction value is margin
# this may make buildin evalution metric not function properly
# for example, we are doing logistic loss, the prediction is score before logistic transformation
# the buildin evaluation error assumes input is after logistic transformation
# Take this in mind when you use the customization, and maybe you need write customized evaluation function
evalerror <- function(preds, dtrain) {
  labels <- getinfo(dtrain, "label")
  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
  return(list(metric = "error", value = err))
}


# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)