Nothing
library(caret)
test_that('single tree', {
skip_on_cran()
set.seed(1)
tr_dat <- twoClassSim(200)
te_dat <- twoClassSim(200)
grid <- data.frame(trials = 1,
model = "tree",
winnow = FALSE,
stringsAsFactors = TRUE)
set.seed(2)
class_trim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
trControl = trainControl(method = "none",
classProbs = TRUE,
trim = TRUE))
set.seed(2)
class_notrim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
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_lt(object.size(class_trim)-object.size(class_notrim), 0)
})
test_that('single rule', {
skip_on_cran()
set.seed(1)
tr_dat <- twoClassSim(200)
te_dat <- twoClassSim(200)
grid <- data.frame(trials = 1,
model = "rules",
winnow = FALSE,
stringsAsFactors = TRUE)
set.seed(2)
class_trim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
trControl = trainControl(method = "none",
classProbs = TRUE,
trim = TRUE))
set.seed(2)
class_notrim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
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_lt(object.size(class_trim)-object.size(class_notrim), 0)
})
test_that('boosted tree', {
skip_on_cran()
set.seed(1)
tr_dat <- twoClassSim(200)
te_dat <- twoClassSim(200)
grid <- data.frame(trials = 5,
model = "tree",
winnow = FALSE,
stringsAsFactors = TRUE)
set.seed(2)
class_trim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
trControl = trainControl(method = "none",
classProbs = TRUE,
trim = TRUE))
set.seed(2)
class_notrim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
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_lt(object.size(class_trim)-object.size(class_notrim), 0)
})
test_that('boosted rule', {
skip_on_cran()
set.seed(1)
tr_dat <- twoClassSim(200)
te_dat <- twoClassSim(200)
grid <- data.frame(trials = 5,
model = "rules",
winnow = FALSE,
stringsAsFactors = TRUE)
set.seed(2)
class_trim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
trControl = trainControl(method = "none",
classProbs = TRUE,
trim = TRUE))
set.seed(2)
class_notrim <- train(Class ~ ., data = tr_dat,
method = "C5.0",
tuneGrid = grid,
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_lt(object.size(class_trim)-object.size(class_notrim), 0)
})
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