46 lines
1.8 KiB
R
Executable File
46 lines
1.8 KiB
R
Executable File
require(xgboost)
|
|
|
|
data(iris)
|
|
iris[,5] <- as.numeric(iris[,5]=='setosa')
|
|
iris <- as.matrix(iris)
|
|
set.seed(20)
|
|
test_ind <- sample(1:nrow(iris),50)
|
|
train_ind <- setdiff(1:nrow(iris),test_ind)
|
|
dtrain <- xgb.DMatrix(iris[train_ind,1:4], label=iris[train_ind,5])
|
|
dtest <- xgb.DMatrix(iris[test_ind,1:4], label=iris[test_ind,5])
|
|
|
|
# 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)
|