library(testthat)
library(recipes)

skip_if_not_installed("modeldata")
data(biomass, package = "modeldata")

# ------------------------------------------------------------------------------

test_that("correct nonnegative functions", {
  skip_if_not_installed("splines2")

  biomass_tr <- biomass[biomass$dataset == "Training", ]
  biomass_te <- biomass[biomass$dataset == "Testing", ]

  rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
                data = biomass_tr
  )

  with_ns <- rec %>%
    step_poly_bernstein(carbon, hydrogen, degree = 5)

  with_ns <- prep(with_ns, training = biomass_tr, verbose = FALSE)

  with_ns_pred_tr <- bake(with_ns, new_data = biomass_tr)
  with_ns_pred_te <- bake(with_ns, new_data = biomass_te)

  carbon_ns_tr_exp <- splines2::bernsteinPoly(biomass_tr$carbon, degree = 5)
  hydrogen_ns_tr_exp <- splines2::bernsteinPoly(biomass_tr$hydrogen, degree = 5)
  carbon_ns_te_exp <- predict(carbon_ns_tr_exp, biomass_te$carbon)
  hydrogen_ns_te_exp <- predict(hydrogen_ns_tr_exp, biomass_te$hydrogen)

  expect_equal(
    unname(attr(carbon_ns_tr_exp, "degree")),
    with_ns$steps[[1]]$results$carbon$degree
  )
  expect_equal(
    unname(attr(carbon_ns_tr_exp, "Boundary.knots")),
    with_ns$steps[[1]]$results$carbon$Boundary.knots
  )
  expect_equal(
    unname(attr(hydrogen_ns_tr_exp, "degree")),
    with_ns$steps[[1]]$results$hydrogen$degree
  )
  expect_equal(
    unname(attr(hydrogen_ns_tr_exp, "Boundary.knots")),
    with_ns$steps[[1]]$results$hydrogen$Boundary.knots
  )

  carbon_ns_tr_res <- as.matrix(with_ns_pred_tr[, grep("carbon", names(with_ns_pred_tr))])
  colnames(carbon_ns_tr_res) <- NULL
  hydrogen_ns_tr_res <- as.matrix(with_ns_pred_tr[, grep("hydrogen", names(with_ns_pred_tr))])
  colnames(hydrogen_ns_tr_res) <- NULL

  carbon_ns_te_res <- as.matrix(with_ns_pred_te[, grep("carbon", names(with_ns_pred_te))])
  colnames(carbon_ns_te_res) <- 1:ncol(carbon_ns_te_res)
  hydrogen_ns_te_res <- as.matrix(with_ns_pred_te[, grep("hydrogen", names(with_ns_pred_te))])
  colnames(hydrogen_ns_te_res) <- 1:ncol(hydrogen_ns_te_res)

  ## remove attributes
  carbon_ns_tr_exp <- matrix(carbon_ns_tr_exp, ncol = 5)
  carbon_ns_te_exp <- matrix(carbon_ns_te_exp, ncol = 5)
  hydrogen_ns_tr_exp <- matrix(hydrogen_ns_tr_exp, ncol = 5)
  hydrogen_ns_te_exp <- matrix(hydrogen_ns_te_exp, ncol = 5)
  dimnames(carbon_ns_tr_res) <- NULL
  dimnames(carbon_ns_te_res) <- NULL
  dimnames(hydrogen_ns_tr_res) <- NULL
  dimnames(hydrogen_ns_te_res) <- NULL

  expect_equal(carbon_ns_tr_res, carbon_ns_tr_exp)
  expect_equal(carbon_ns_te_res, carbon_ns_te_exp)
  expect_equal(hydrogen_ns_tr_res, hydrogen_ns_tr_exp)
  expect_equal(hydrogen_ns_te_res, hydrogen_ns_te_exp)
})

test_that("check_name() is used", {
  dat <- mtcars
  dat$mpg_01 <- dat$mpg

  rec <- recipe(~ ., data = dat) %>%
    step_poly_bernstein(mpg)

  expect_snapshot(
    error = TRUE,
    prep(rec, training = dat)
  )
})

test_that("tunable", {

  biomass_tr <- biomass[biomass$dataset == "Training", ]
  biomass_te <- biomass[biomass$dataset == "Testing", ]

  rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
                data = biomass_tr
  )

  rec <-
    recipe(~., data = iris) %>%
    step_poly_bernstein(all_predictors())
  rec_param <- tunable.step_poly_bernstein(rec$steps[[1]])
  expect_equal(rec_param$name, c("degree"))
  expect_true(all(rec_param$source == "recipe"))
  expect_true(is.list(rec_param$call_info))
  expect_equal(nrow(rec_param), 1)
  expect_equal(
    names(rec_param),
    c("name", "call_info", "source", "component", "component_id")
  )
})

# Infrastructure ---------------------------------------------------------------

test_that("bake method errors when needed non-standard role columns are missing", {
  rec <- recipe(mtcars) %>%
    step_poly_bernstein(disp) %>%
    update_role(disp, new_role = "potato") %>%
    update_role_requirements(role = "potato", bake = FALSE)

  rec_trained <- prep(rec, training = mtcars)

  expect_error(bake(rec_trained, new_data = mtcars[, -3]),
               class = "new_data_missing_column")
})

test_that("empty printing", {
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_poly_bernstein(rec)

  expect_snapshot(rec)

  rec <- prep(rec, mtcars)

  expect_snapshot(rec)
})

test_that("empty selection prep/bake is a no-op", {
  rec1 <- recipe(mpg ~ ., mtcars)
  rec2 <- step_poly_bernstein(rec1)

  rec1 <- prep(rec1, mtcars)
  rec2 <- prep(rec2, mtcars)

  baked1 <- bake(rec1, mtcars)
  baked2 <- bake(rec2, mtcars)

  expect_identical(baked1, baked2)
})

test_that("empty selection tidy method works", {
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_poly_bernstein(rec)

  expect <- tibble(terms = character(), id = character())

  expect_identical(tidy(rec, number = 1), expect)

  rec <- prep(rec, mtcars)

  expect_identical(tidy(rec, number = 1), expect)
})

test_that("keep_original_cols works", {
  new_names <- paste0("mpg_", formatC(1:10, width = 2, flag = "0"))

  rec <- recipe(~ mpg, mtcars) %>%
    step_poly_bernstein(all_predictors(), keep_original_cols = FALSE)

  rec <- prep(rec)
  res <- bake(rec, new_data = NULL)

  expect_equal(
    colnames(res),
    new_names
  )

  rec <- recipe(~ mpg, mtcars) %>%
    step_poly_bernstein(all_predictors(), keep_original_cols = TRUE)

  rec <- prep(rec)
  res <- bake(rec, new_data = NULL)

  expect_equal(
    colnames(res),
    c("mpg", new_names)
  )
})

test_that("keep_original_cols - can prep recipes with it missing", {
  # step_poly_bernstein() was added after keep_original_cols
  # Making this test case unlikely
  expect_true(TRUE)
})

test_that("printing", {
  rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
                data = biomass) %>%
    step_poly_bernstein(carbon, hydrogen)

  expect_snapshot(print(rec))
  expect_snapshot(prep(rec))
})

test_that("tunable is setup to work with extract_parameter_set_dials", {
  skip_if_not_installed("dials")
  rec <- recipe(~., data = mtcars) %>%
    step_poly_bernstein(
      all_predictors(),
      degree = hardhat::tune()
    )

  params <- extract_parameter_set_dials(rec)

  expect_s3_class(params, "parameters")
  expect_identical(nrow(params), 1L)
})