context("Test importance_plot")
test_that(
desc = "Testing plot",
code = {
library(mlbench)
data("PimaIndiansDiabetes2")
dataset = data.table::as.data.table(PimaIndiansDiabetes2)
target_col = "diabetes"
vec = setdiff(colnames(dataset), target_col)
dataset = cbind(
dataset[, c(target_col), with = F],
lightgbm::lgb.convert_with_rules(dataset[, vec, with = F])[[1]]
)
task = mlr3::TaskClassif$new(
id = "pima",
backend = dataset,
target = target_col,
positive = "pos"
)
set.seed(17)
split = list(
train_index = sample(seq_len(task$nrow), size = 0.7 * task$nrow)
)
split$test_index = setdiff(seq_len(task$nrow), split$train_index)
learner = mlr3::lrn("classif.lightgbm", objective = "binary")
learner$param_set$values = mlr3misc::insert_named(
learner$param_set$values,
list(
"early_stopping_round" = 3,
"learning_rate" = 0.1,
"seed" = 17L,
"num_iterations" = 10,
"metric" = "auc"
)
)
learner$train(task, row_ids = split$train_index)
importance = learner$importance()
expect_length(importance, 8)
expect_class(importance_plot(importance), c("gg", "ggplot"))
}
)
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