context("AutoxgboostMC")
test_that("autoxgboostMC works on different tasksfor single measure", {
tasks = list(
sonar.task, # binary classification
iris.fac, # binary classification with factors
iris.task, # multiclass classification
subsetTask(bh.task, subset = 1:50),
iris.fac)
for (t in tasks) {
axgb = AutoxgboostMC$new(measures = list(acc))
axgb$fit(t, time.budget = 5L)
expect_true(!is.null(axgb$model))
p = axgb$predict(t)
expect_class(p, "Prediction")
}
})
test_that("autoxgboostMC works on different tasks", {
tasks = list(
sonar.task, # binary classification
iris.fac, # binary classification with factors
iris.task) # multiclass classification
for (t in tasks) {
axgb = AutoxgboostMC$new(measures = list(acc, timepredict))
axgb$fit(t, time.budget = 10L)
expect_true(!is.null(axgb$model))
p = axgb$predict(t)
expect_class(p, "Prediction")
}
})
test_that("Multiple measures work", {
fairf11 = setMeasurePars(fairf1, grouping = function(df) as.factor(df$age > 30))
axgb = AutoxgboostMC$new(measures = list(acc, fairf11))
axgb$fit(pid.task, time.budget = 10L)
expect_true(!is.null(axgb$model))
p = axgb$predict(pid.task)
expect_class(p, "Prediction")
})
test_that("New measures work", {
fairf11 = setMeasurePars(fairf1, grouping = function(df) as.factor(df$age > 30))
axgb = AutoxgboostMC$new(measures = list(acc, fairf11, timepredict))
axgb$fit(pid.task, time.budget = 10L)
expect_true(!is.null(axgb$model))
p = axgb$predict(pid.task)
expect_class(p, "Prediction")
})
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