Nothing
context('Test models with custom objective')
require(xgboost)
set.seed(1994)
n_threads <- 2
data(agaricus.train, package = 'xgboost')
data(agaricus.test, package = 'xgboost')
dtrain <- xgb.DMatrix(
agaricus.train$data, label = agaricus.train$label, nthread = n_threads
)
dtest <- xgb.DMatrix(
agaricus.test$data, label = agaricus.test$label, nthread = n_threads
)
watchlist <- list(eval = dtest, train = dtrain)
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))
}
evalerror <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)
return(list(metric = "error", value = err))
}
param <- list(max_depth = 2, eta = 1, nthread = n_threads,
objective = logregobj, eval_metric = evalerror)
num_round <- 2
test_that("custom objective works", {
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")
expect_false(is.null(bst$evaluation_log))
expect_false(is.null(bst$evaluation_log$eval_error))
expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
})
test_that("custom objective in CV works", {
cv <- xgb.cv(param, dtrain, num_round, nfold = 10, verbose = FALSE)
expect_false(is.null(cv$evaluation_log))
expect_equal(dim(cv$evaluation_log), c(2, 5))
expect_lt(cv$evaluation_log[num_round, test_error_mean], 0.03)
})
test_that("custom objective with early stop works", {
bst <- xgb.train(param, dtrain, 10, watchlist)
expect_equal(class(bst), "xgb.Booster")
train_log <- bst$evaluation_log$train_error
expect_true(all(diff(train_log) <= 0))
})
test_that("custom objective using DMatrix attr works", {
attr(dtrain, 'label') <- getinfo(dtrain, 'label')
logregobjattr <- function(preds, dtrain) {
labels <- attr(dtrain, 'label')
preds <- 1 / (1 + exp(-preds))
grad <- preds - labels
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
param$objective <- logregobjattr
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")
})
test_that("custom objective with multi-class works", {
data <- as.matrix(iris[, -5])
label <- as.numeric(iris$Species) - 1
dtrain <- xgb.DMatrix(data = data, label = label, nthread = n_threads)
n_classes <- 3
fake_softprob <- function(preds, dtrain) {
expect_true(all(matrix(preds) == 0.5))
grad <- rnorm(dim(as.matrix(preds))[1])
expect_equal(dim(data)[1] * n_classes, dim(as.matrix(preds))[1])
hess <- rnorm(dim(as.matrix(preds))[1])
return (list(grad = grad, hess = hess))
}
fake_merror <- function(preds, dtrain) {
expect_equal(dim(data)[1] * n_classes, dim(as.matrix(preds))[1])
}
param$objective <- fake_softprob
param$eval_metric <- fake_merror
bst <- xgb.train(param, dtrain, 1, num_class = n_classes)
})
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