library(caret)

test_that('single tree', {
  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 = "C5.0",
                      tuneGrid = data.frame(trials = 1, 
                                            model = "tree", 
                                            winnow = FALSE),
                      trControl = trainControl(method = "none", 
                                               classProbs = TRUE,
                                               trim = TRUE))
  
  set.seed(2)
  class_notrim <- train(Class ~ ., data = tr_dat,
                        method = "C5.0",
                        tuneGrid = data.frame(trials = 1, 
                                              model = "tree", 
                                              winnow = FALSE),
                        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('single rule', {
  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 = "C5.0",
                      tuneGrid = data.frame(trials = 1, 
                                            model = "rules", 
                                            winnow = FALSE),
                      trControl = trainControl(method = "none", 
                                               classProbs = TRUE,
                                               trim = TRUE))
  
  set.seed(2)
  class_notrim <- train(Class ~ ., data = tr_dat,
                        method = "C5.0",
                        tuneGrid = data.frame(trials = 1, 
                                              model = "rules", 
                                              winnow = FALSE),
                        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('boosted tree', {
  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 = "C5.0",
                      tuneGrid = data.frame(trials = 5, 
                                            model = "tree", 
                                            winnow = FALSE),
                      trControl = trainControl(method = "none", 
                                               classProbs = TRUE,
                                               trim = TRUE))
  
  set.seed(2)
  class_notrim <- train(Class ~ ., data = tr_dat,
                        method = "C5.0",
                        tuneGrid = data.frame(trials = 5, 
                                              model = "tree", 
                                              winnow = FALSE),
                        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('boosted rule', {
  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 = "C5.0",
                      tuneGrid = data.frame(trials = 5, 
                                            model = "rules", 
                                            winnow = FALSE),
                      trControl = trainControl(method = "none", 
                                               classProbs = TRUE,
                                               trim = TRUE))
  
  set.seed(2)
  class_notrim <- train(Class ~ ., data = tr_dat,
                        method = "C5.0",
                        tuneGrid = data.frame(trials = 5, 
                                              model = "rules", 
                                              winnow = FALSE),
                        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)
})



