library(caret)
test_that('glmnet classification', {
skip_on_cran()
set.seed(1)
tr_dat <- twoClassSim(200)
te_dat <- twoClassSim(200)
set.seed(2)
class_trim <- train(Class ~ ., data = tr_dat,
method = "glmnet",
tuneGrid = data.frame(lambda = .1, alpha = .5),
trControl = trainControl(method = "none",
classProbs = TRUE,
trim = TRUE))
set.seed(2)
class_notrim <- train(Class ~ ., data = tr_dat,
method = "glmnet",
tuneGrid = data.frame(lambda = .1, alpha = .5),
trControl = trainControl(method = "none",
classProbs = TRUE,
trim = FALSE))
expect_equal(predict(class_trim, te_dat),
predict(class_notrim, te_dat))
expect_equal(predict(class_trim, te_dat, type = "prob"),
predict(class_notrim, te_dat, type = "prob"))
expect_less_than(object.size(class_trim)-object.size(class_notrim), 0)
})
test_that('glmnet regression', {
skip_on_cran()
set.seed(1)
tr_dat <- SLC14_1(200)
te_dat <- SLC14_1(200)
set.seed(2)
reg_trim <- train(y ~ ., data = tr_dat,
method = "glmnet",
tuneGrid = data.frame(lambda = .1, alpha = .5),
trControl = trainControl(method = "none",
trim = TRUE))
set.seed(2)
reg_notrim <- train(y ~ ., data = tr_dat,
method = "glmnet",
tuneGrid = data.frame(lambda = .1, alpha = .5),
trControl = trainControl(method = "none",
trim = FALSE))
expect_equal(predict(reg_trim, te_dat),
predict(reg_notrim, te_dat))
expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0)
})
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