150 lines
4.3 KiB
R
150 lines
4.3 KiB
R
context('Test models with custom objective')
|
|
|
|
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
|
|
)
|
|
evals <- 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, evals)
|
|
expect_equal(class(bst), "xgb.Booster")
|
|
expect_false(is.null(attributes(bst)$evaluation_log))
|
|
expect_false(is.null(attributes(bst)$evaluation_log$eval_error))
|
|
expect_lt(attributes(bst)$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$evaluation_log[num_round, test_error_mean], 0.03)
|
|
})
|
|
|
|
test_that("custom objective with early stop works", {
|
|
bst <- xgb.train(param, dtrain, 10, evals)
|
|
expect_equal(class(bst), "xgb.Booster")
|
|
train_log <- attributes(bst)$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, evals)
|
|
expect_equal(class(bst), "xgb.Booster")
|
|
})
|
|
|
|
test_that("custom objective with multi-class shape", {
|
|
data <- as.matrix(iris[, -5])
|
|
label <- as.numeric(iris$Species) - 1
|
|
dtrain <- xgb.DMatrix(data = data, label = label, nthread = n_threads)
|
|
n_classes <- 3
|
|
|
|
fake_softprob <- function(preds, dtrain) {
|
|
expect_true(all(matrix(preds) == 0.5))
|
|
## use numeric vector here to test compatibility with XGBoost < 2.1
|
|
grad <- rnorm(length(as.matrix(preds)))
|
|
expect_equal(dim(data)[1] * n_classes, dim(as.matrix(preds))[1] * n_classes)
|
|
hess <- rnorm(length(as.matrix(preds)))
|
|
return(list(grad = grad, hess = hess))
|
|
}
|
|
fake_merror <- function(preds, dtrain) {
|
|
expect_equal(dim(data)[1] * n_classes, dim(as.matrix(preds))[1])
|
|
}
|
|
param$objective <- fake_softprob
|
|
param$eval_metric <- fake_merror
|
|
bst <- xgb.train(param, dtrain, 1, num_class = n_classes)
|
|
})
|
|
|
|
softmax <- function(values) {
|
|
values <- as.numeric(values)
|
|
exps <- exp(values)
|
|
den <- sum(exps)
|
|
return(exps / den)
|
|
}
|
|
|
|
softprob <- function(predt, dtrain) {
|
|
y <- getinfo(dtrain, "label")
|
|
|
|
n_samples <- dim(predt)[1]
|
|
n_classes <- dim(predt)[2]
|
|
|
|
grad <- matrix(nrow = n_samples, ncol = n_classes)
|
|
hess <- matrix(nrow = n_samples, ncol = n_classes)
|
|
|
|
for (i in seq_len(n_samples)) {
|
|
t <- y[i]
|
|
p <- softmax(predt[i, ])
|
|
for (c in seq_len(n_classes)) {
|
|
g <- if (c - 1 == t) {
|
|
p[c] - 1.0
|
|
} else {
|
|
p[c]
|
|
}
|
|
h <- max((2.0 * p[c] * (1.0 - p[c])), 1e-6)
|
|
grad[i, c] <- g
|
|
hess[i, c] <- h
|
|
}
|
|
}
|
|
|
|
return(list(grad = grad, hess = hess))
|
|
}
|
|
|
|
|
|
test_that("custom objective with multi-class works", {
|
|
data <- as.matrix(iris[, -5])
|
|
label <- as.numeric(iris$Species) - 1
|
|
|
|
dtrain <- xgb.DMatrix(data = data, label = label)
|
|
|
|
param$num_class <- 3
|
|
param$objective <- softprob
|
|
param$eval_metric <- "merror"
|
|
param$base_score <- 0.5
|
|
|
|
custom_bst <- xgb.train(param, dtrain, 2)
|
|
custom_predt <- predict(custom_bst, dtrain)
|
|
|
|
param$objective <- "multi:softmax"
|
|
builtin_bst <- xgb.train(param, dtrain, 2)
|
|
builtin_predt <- predict(builtin_bst, dtrain)
|
|
|
|
expect_equal(custom_predt, builtin_predt)
|
|
})
|