# context("test-learners_reg.R -- General testing for continuous regression")

## define test dataset
# data(mtcars)
# task <- sl3_Task$new(mtcars, covariates = c(
# "cyl", "disp", "hp", "drat", "wt", "qsec",
# "vs", "am", "gear", "carb"
# ), outcome = "mpg")
# task2 <- sl3_Task$new(mtcars, covariates = c(
# "cyl", "disp", "hp", "drat", "wt", "qsec",
# "vs", "am", "gear", "carb"
# ), outcome = "mpg")

# interactions <- list(c("cyl", "disp"), c("hp", "drat"))
# task_with_interactions <- task$add_interactions(interactions)
# task2 <- task2$add_interactions(interactions)
# test_learner <- function(learner, task, ...) {

## test learner definition this requires that a learner can be instantiated with
## only default arguments. Not sure if this is a reasonable requirement
# learner_obj <- learner$new(...)
# print(sprintf("Testing Learner: %s", learner_obj$name))
## test learner training
# fit_obj <- learner_obj$train(task)
# test_that("Learner can be trained on data", expect_true(fit_obj$is_trained))

## test learner prediction
# train_preds <- fit_obj$predict()
# test_that("Learner can generate training set predictions", expect_equal(
# sl3:::safe_dim(train_preds)[1],
# length(task$Y)
# ))

# holdout_preds <- fit_obj$predict(task2)
# test_that("Learner can generate holdout set predictions", expect_equal(
# train_preds,
# holdout_preds
# ))

## test learner chaining
# chained_task <- fit_obj$chain()
# test_that("Chaining returns a task", expect_true(is(chained_task, "sl3_Task")))
# test_that("Chaining returns the correct number of rows", expect_equal(
# nrow(chained_task$X),
# nrow(task$X)
# ))
# }

# test_learner(Lrnr_glm, task)
# test_learner(Lrnr_glm_fast, task)

### test xgboost learner:
# op <- options(sl3.verbose = TRUE)

### nrounds is always needed
# test_learner(Lrnr_xgboost, task, nrounds = 10)

### linear link function
# test_learner(Lrnr_xgboost, task, nrounds = 10,
# objective = "reg:linear")

### logit-linear link function, need [0,1] outcomes
## test_learner(Lrnr_xgboost, task, nrounds = 50,
## objective = 'reg:logistic')

### GLM, i.e., use linear model for at each boosting stage
# test_learner(Lrnr_xgboost, task, nrounds = 50,
# booster = "gblinear")

### GBM (default), i.e., use tree model for at each boosting stage
# test_learner(Lrnr_xgboost, task, nrounds = 10,
# booster = "gbtree")

### another type of tree model
## test_learner(Lrnr_xgboost, task, nrounds = 50, booster = 'dart')

# test_learner(Lrnr_xgboost, task_with_interactions, nrounds = 10,
# booster = "gbtree",
# covariates = c("cyl", "disp", "drat", "cyl_disp", "hp_drat"))

# options(sl3.verbose = FALSE)
# test_learner(Lrnr_xgboost, task, nrounds = 50)

# options(op)