Nothing
library(caret)
library(recipes)
library(dplyr)
library(testthat)
set.seed(1)
dat <- twoClassSim(100)
dat_tb <- as_tibble(dat)
a <- dat[,5]
y <- dat[["Class"]]
df <- data.frame(a, y, stringsAsFactors = TRUE)
rec <- recipe(y ~ .,data = df)
ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
classProbs = TRUE,
summaryFunction = twoClassSummary)
test_that('train runs on tibbles and recipes with glm', {
expect_error(
train(
rec,
data = as_tibble(df),
method = "glm",
family = "binomial",
metric = "ROC",
trControl = ctrl
),
regexp = NA
)
})
test_that('train runs on tibbles and formulas with glm', {
expect_error(
train(
y ~ .,
data = as_tibble(df),
method = "glm",
family = "binomial",
metric = "ROC",
trControl = ctrl
),
regexp = NA
)
})
test_that('train runs on tibbles and recipes with glm', {
expect_error(
train(
rec,
data = as_tibble(df),
method = "glm",
family = "binomial",
metric = "ROC",
trControl = ctrl
),
regexp = NA
)
})
test_that('downsampling on tibble', {
expect_error(
caret:::parse_sampling("down")$func(dat_tb[, 1], dat_tb$Class),
regexp = NA
)
})
test_that('upsampling on tibble', {
expect_error(
caret:::parse_sampling("up")$func(dat_tb[, 1], dat_tb$Class),
regexp = NA
)
})
# check these manually to avoid more dependencies
# caret:::parse_sampling("smote")$func(dat_tb[, 1], dat_tb$Class)
# caret:::parse_sampling("rose")$func(dat_tb[, 1], dat_tb$Class)
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