context("test-subset_covariates -- Subset covariates correctly to match prediction and training")
library(data.table)
library(uuid)


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

covariate_subset <- c("cyl", "disp", "hp")
task <- sl3_Task$new(mtcars, covariates = covariates, outcome = outcome)

lrnr_glm <- make_learner(Lrnr_glm_fast)
lrnr_mean <- make_learner(Lrnr_mean)
lrnr_sl_subset <- make_learner(Lrnr_sl, c(lrnr_glm, lrnr_mean), covariates = covariate_subset)
fit <- lrnr_sl_subset$train(task)
pred <- fit$predict(task)

glm_full_fit <- fit$fit_object$full_fit$fit_object$learner_fits[[1]]$fit_object$learner_fits[[1]]
glm_coef_names <- names(coef(glm_full_fit))
test_that("subsetting covariates extends to sublearners", expect_equal(glm_coef_names, c("intercept", covariate_subset)))

task_pre_subset <- sl3_Task$new(mtcars, covariates = covariate_subset, outcome = outcome)

glm_fit_pre_subset <- lrnr_glm$train(task_pre_subset)
full_preds <- glm_fit_pre_subset$predict(task)
training_preds <- glm_fit_pre_subset$predict()
test_that("extra covariates in prediction set get dropped correctly", expect_equal(full_preds, training_preds))


shuffled_subset <- sample(covariate_subset)
task_pre_subset_shuffled <- sl3_Task$new(mtcars, covariates = shuffled_subset, outcome = outcome)
# debugonce(glm_fit_pre_subset$subset_covariates)
shuffled_preds <- glm_fit_pre_subset$predict(task_pre_subset_shuffled)
test_that("covariates out of order prediction set get shuffled correctly", expect_equal(full_preds, shuffled_preds))


task_train <- sl3_Task$new(mtcars, covariates = covariates, outcome = outcome)
task_predict <- sl3_Task$new(mtcars, covariates = covariate_subset, outcome = outcome)
glm_fit <- lrnr_glm$train(task_train)
test_that("missing covariates in prediction set throws error", {
  expect_error(glm_fit$predict(task_predict))
})

missing_data <- data.table(mtcars[1, 1:7])
colnames(missing_data) <- colnames(mtcars)[1:7]
missing_data <- rbind(missing_data, data.table(mtcars[-1, ]), fill = T)
covs <- c("cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am", "gear", "carb")
Y <- "mpg"
task_missing_data <- suppressWarnings(
  sl3_Task$new(missing_data, covariates = covs, outcome = Y)
)

lrnr_glm <- make_learner(Lrnr_glm_fast)
suppressWarnings({glm_fit <- lrnr_glm$train(task_missing_data)})

task_complete_data <- sl3_Task$new(mtcars, covariates = covs, outcome = Y)
test_that("missingness indicators in prediction task works", {
  expect_vector(glm_fit$predict(task_complete_data))
})