Nothing
hparams_list <- list(lambda = c("1e-3", "1e-2", "1e-1"), alpha = "0.01")
outcome_type <- get_outcome_type(otu_mini_bin %>% dplyr::pull("dx"))
class_probs <- outcome_type != "numeric"
perf_metric_function <- get_perf_metric_fn(outcome_type)
cv_group <- c(
"C", "D", "E", "C", "D", "E", "A", "D", "A", "D", "D", "A",
"B", "E", "D", "A", "D", "E", "B", "E", "A", "B", "A", "E", "A",
"D", "A", "D", "A", "C", "A", "B", "B", "E", "A", "E", "B", "C",
"D", "D", "C", "A", "E", "E", "B", "B", "A", "C", "D", "D", "D",
"D", "A", "D", "C", "A", "D", "D", "B", "C", "E", "C", "E", "C",
"B", "D", "B", "D", "C", "B", "B", "B", "B", "B", "B", "B", "C",
"D", "D", "E", "A", "E", "D", "E", "A", "D", "A", "E", "E", "C",
"B", "B", "E", "B", "C", "C", "D", "A", "A", "E", "E", "C", "A",
"C", "E", "A", "D", "A", "C", "D", "E", "E", "A", "A", "B", "E",
"C", "B", "B", "C", "C", "D", "C", "E", "E", "E", "C", "E", "D",
"D", "B", "B", "B", "E", "E", "A", "A", "A", "B", "D", "B", "D",
"B", "B", "B", "D", "B", "B", "D", "B", "D", "C", "C", "B", "A",
"A", "D", "C", "E", "E", "A"
)
test_that("define_cv works on otu_mini training data with groups", {
set.seed(2019)
expect_message(
cv <- define_cv(otu_mini_bin_results_rf$trained_model$trainingData,
".outcome",
hparams_list,
perf_metric_function,
class_probs = class_probs,
cv_times = 2,
groups = cv_group
),
"Groups will be kept together in CV partitions"
)
expect_equal(cv, otu_mini_cv)
expect_equal(cv$method, "repeatedcv")
expect_equal(cv$number, 5)
expect_equal(cv$repeats, 2)
expect_message(
define_cv(otu_mini_bin_results_rf$trained_model$trainingData,
".outcome",
hparams_list,
perf_metric_function,
class_probs = class_probs,
cv_times = 2,
groups = sample(c("A", "B"), size = 160, replace = TRUE)
),
"Groups will not be kept together in CV partitions because the number of groups in the training set is not larger than `kfold`"
)
expect_message(
define_cv(
otu_mini_bin_results_rf$trained_model$trainingData,
".outcome",
hparams_list,
perf_metric_function,
class_probs = class_probs,
kfold = 5,
cv_times = 2,
groups = cv_group,
group_partitions = list(train = c("A", "B", "C", "D"), test = c("E"))
),
"Groups will not be kept together in CV partitions because the number of groups in the training set is not larger than `kfold`"
)
})
test_that("get_seeds_trainControl works", {
set.seed(0)
expect_equal(
length(get_seeds_trainControl(hparams_list, 2, 2, 2)),
2 * 2 + 1
)
expect_equal(
sapply((get_seeds_trainControl(hparams_list, 2, 2, 3)), length),
c(rep(3, 4), 1)
)
set.seed(0)
expect_equal(
get_seeds_trainControl(hparams_list, 2, 2, 3),
list(c(1422L, 1017L, 679L), c(2177L, 930L, 1533L), c(
471L, 2347L,
270L
), c(1211L, 597L, 1301L), 1974L)
)
})
test_that("create_grouped_k_multifolds keeps groups together in CV partitions", {
set.seed(0)
group <- c("A", "B", "A", "B", "C", "C", "A", "A", "D")
folds <- create_grouped_k_multifolds(group, kfold = 2, cv_times = 2)
expect_equal(folds, list(Fold1.Rep1 = c(1L, 3L, 5L, 6L, 7L, 8L), Fold2.Rep1 = c(
2L,
4L, 9L
), Fold1.Rep2 = c(2L, 4L), Fold2.Rep2 = c(
1L, 3L, 5L, 6L,
7L, 8L, 9L
)))
fold_grps <- sapply(folds, function(x) group[x])
expect_false(any(fold_grps$Fold1.Rep1 %in% fold_grps$Fold2.Rep1))
expect_false(any(fold_grps$Fold1.Rep2 %in% fold_grps$Fold2.Rep2))
set.seed(5)
expect_error(
create_grouped_k_multifolds(group, kfold = 2, cv_times = 2),
"Could not split the data into train and validate folds"
)
})
test_that("keep_groups_in_cv_partitions works", {
expect_true(keep_groups_in_cv_partitions(
groups = cv_group,
group_partitions = NULL,
kfold = 2
))
expect_false(keep_groups_in_cv_partitions(
groups = sample(c("A", "B"), size = 160, replace = TRUE),
group_partitions = NULL,
kfold = 5
))
expect_false(keep_groups_in_cv_partitions(
groups = cv_group,
group_partitions = list(train = c("A", "B", "C", "D"), test = c("E")),
kfold = 5
))
expect_false(keep_groups_in_cv_partitions(NULL, NULL, 1))
})
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