library(testthat)

# Create a test for the main function
test_that("Testing main function", {
  # Using the iris dataset for testing
  formula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width

  # Run the main function
  results <- main(iris, formula, choose_imput = TRUE, k = 5)

  # Check if the results are a list
  expect_type(results, "list")

  # Check if all prediction results are returned
  expect_true("rf_predictions" %in% names(results))
  expect_true("svm_predictions" %in% names(results))
  expect_true("decision_tree_predictions" %in% names(results))
  expect_true("logistic_predictions" %in% names(results))

  # Check if the best model information is returned
  expect_true("best_model_info" %in% names(results))

  # Check the structure of the best model information
  expect_type(results$best_model_info, "list")
  expect_true("best_model" %in% names(results$best_model_info))
  expect_true("error" %in% names(results$best_model_info))

  # Check that the best model's name and error are not NULL
  expect_true(!is.null(results$best_model_info$best_model))
  expect_true(!is.null(results$best_model_info$error))

  # Check that the error is a valid numeric value between 0 and 1
  expect_true(is.numeric(results$best_model_info$error))
  expect_true(results$best_model_info$error >= 0 && results$best_model_info$error <= 1)
})