add customize objective

R-package/R/utils.R

@@ -133,9 +133,9 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
     .Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain, 
           PACKAGE = "xgboost")
   } else {
-    pred <- xgb.predict(bst, dtrain)
+    pred <- xgb.predict(booster, dtrain)
     gpair <- obj(pred, dtrain)
-    succ <- xgb.iter.boost(bst, dtrain, gpair)
+    succ <- xgb.iter.boost(booster, dtrain, gpair)
   }
   return(TRUE)
 }
@@ -172,7 +172,7 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL) {
         if (length(names(w)) == 0) {
           stop("xgb.eval: name tag must be presented for every elements in watchlist")
         }
-        ret <- feval(xgb.predict(bst, w[[1]]), w[[1]])
+        ret <- feval(xgb.predict(booster, w[[1]]), w[[1]])
         msg <- paste(msg, "\t", names(w), "-", ret$metric, ":", ret$value, sep="")
       }
     }

R-package/demo/custom_objective.R

@@ -0,0 +1,39 @@
+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)