Nothing
library(mlbench)
data("DNA")
dataset <- DNA |>
data.table::as.data.table() |>
na.omit()
seed <- 123
feature_cols <- colnames(dataset)[160:180]
param_list_xgboost <- expand.grid(
subsample = seq(0.6, 1, .2),
colsample_bytree = seq(0.6, 1, .2),
min_child_weight = seq(1, 5, 4),
learning_rate = seq(0.1, 0.2, 0.1),
max_depth = seq(1, 5, 4)
)
if (isTRUE(as.logical(Sys.getenv("_R_CHECK_LIMIT_CORES_")))) {
# on cran
ncores <- 2L
} else {
ncores <- ifelse(
test = parallel::detectCores() > 4,
yes = 4L,
no = ifelse(
test = parallel::detectCores() < 2L,
yes = 1L,
no = parallel::detectCores()
)
)
}
train_x <- model.matrix(
~ -1 + .,
dataset[, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[, get("Class")]) - 1L
options("mlexperiments.bayesian.max_init" = 10L)
options("mlexperiments.optim.xgb.nrounds" = 100L)
options("mlexperiments.optim.xgb.early_stopping_rounds" = 10L)
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
# ###########################################################################
# %% TUNING
# ###########################################################################
xgboost_bounds <- list(
subsample = c(0.2, 1),
colsample_bytree = c(0.2, 1),
min_child_weight = c(1L, 10L),
learning_rate = c(0.1, 0.2),
max_depth = c(1L, 10L)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
# ###########################################################################
# %% NESTED CV
# ###########################################################################
test_that(
desc = "test nested cv, bayesian, multi:softprob - xgboost, with weights",
code = {
xgboost_optimizer <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerXgboost$new(
metric_optimization_higher_better = FALSE
),
strategy = "bayesian",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
xgboost_optimizer$parameter_bounds <- xgboost_bounds
xgboost_optimizer$parameter_grid <- param_list_xgboost
xgboost_optimizer$split_type <- "stratified"
xgboost_optimizer$optim_args <- optim_args
y_weights <- ifelse(train_y == 1, 0.8, ifelse(train_y == 2, 1.2, 1))
xgboost_optimizer$learner_args <- list(
objective = "multi:softprob",
eval_metric = "mlogloss",
num_class = 3,
case_weights = y_weights
)
xgboost_optimizer$predict_args <- list(reshape = TRUE)
xgboost_optimizer$performance_metric <- mlexperiments::metric("bacc")
# set data
xgboost_optimizer$set_data(
x = train_x,
y = train_y
)
cv_results <- xgboost_optimizer$execute()
expect_type(cv_results, "list")
expect_equal(dim(cv_results), c(3, 11))
expect_true(inherits(
x = xgboost_optimizer$results,
what = "mlexCV"
))
}
)
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