context("classif_bartMachine")
test_that("classif_bartMachine", {
requirePackagesOrSkip("bartMachine", default.method = "load")
parset.list = list(
list(seed = getOption("mlr.debug.seed")),
list(num_burn_in = 20L, num_iterations_after_burn_in = 50L, run_in_sample = FALSE, seed = getOption("mlr.debug.seed")),
list(num_burn_in = 20L, num_iterations_after_burn_in = 50L, alpha = 0.8, num_trees = 25L, run_in_sample = FALSE, seed = getOption("mlr.debug.seed"))
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
x = binaryclass.train
y = factor(x[, binaryclass.class.col], levels = binaryclass.class.levs)
x[, binaryclass.class.col] = NULL
pars = list(X = x, y = y, verbose = FALSE)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
m = do.call(bartMachine::bartMachine, pars)
newx = binaryclass.test
newx[, binaryclass.class.col] = NULL
set.seed(getOption("mlr.debug.seed"))
p = predict(m, new_data = newx, type = "class")
set.seed(getOption("mlr.debug.seed"))
p2 = predict(m, new_data = newx, type = "prob")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.bartMachine", binaryclass.df, binaryclass.target, binaryclass.train.inds,
old.predicts.list, parset.list)
testProbParsets("classif.bartMachine", binaryclass.df, binaryclass.target, binaryclass.train.inds,
old.probs.list, parset.list)
for (i in seq_along(parset.list)){
expect_true(length(old.predicts.list[[i]]) == nrow(binaryclass.test))
expect_true(length(old.probs.list[[i]]) == nrow(binaryclass.test))
}
})
# issue 422
test_that("classif_bartMachine works with missing values", {
requirePackagesOrSkip("bartMachine", default.method = "load")
foo = binaryclass.df
foo[1, 1] = NA
bartMachine::bartMachine(X = foo, y = foo[[binaryclass.target]], use_missing_data = TRUE)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.