add customize objective

R-package/demo/custom_objective.R

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+require(xgboost)
+# load in the agaricus dataset
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+
+# 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))
+}
+print ('start training with user customized objective')
+# 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)