# Tests for R/predict.cubist.R

test_that("predict.cubist works for basic prediction", {
  data <- new_cubist_data(n = 100, p = 5, seed = 123)
  mod <- cubist(data$x, data$y)

  # Predict on training data
  preds <- predict(mod, data$x)

  expect_type(preds, "double")
  expect_length(preds, 100)
  expect_false(any(is.na(preds)))
})

test_that("predict.cubist works with neighbors parameter", {
  data <- new_cubist_data(n = 100, p = 5, seed = 123)
  mod <- cubist(data$x, data$y)

  # Test neighbors from 1 to 9
  for (k in c(1, 5, 9)) {
    preds <- predict(mod, data$x, neighbors = k)
    expect_type(preds, "double")
    expect_length(preds, 100)
  }
})

test_that("predict.cubist reorders columns correctly", {
  data <- new_cubist_data(n = 50, p = 5, seed = 123)
  mod <- cubist(data$x, data$y)

  # Reorder columns in newdata
  newdata <- data$x[, c(3, 1, 5, 2, 4)]
  preds <- predict(mod, newdata)

  expect_type(preds, "double")
  expect_length(preds, 50)
})

test_that("predict.cubist adds case weight column when needed", {
  data <- new_cubist_data(n = 100, p = 3, seed = 123)
  weights <- runif(100, 0.5, 2)
  mod <- cubist(data$x, data$y, weights = weights)

  # Model was trained with weights, predict should work
  preds <- predict(mod, data$x)

  expect_type(preds, "double")
  expect_length(preds, 100)
})

test_that("predict.cubist handles sample parameter in model", {
  data <- new_cubist_data(n = 100, p = 3, seed = 123)
  mod <- cubist(data$x, data$y, control = cubistControl(sample = 50, seed = 42))

  preds <- predict(mod, data$x)

  expect_type(preds, "double")
  expect_length(preds, 100)
})

test_that("predict.cubist errors when newdata is NULL", {
  data <- new_cubist_data(n = 50, p = 3)
  mod <- cubist(data$x, data$y)

  expect_snapshot(
    predict(mod, newdata = NULL),
    error = TRUE
  )
})

test_that("predict.cubist errors with multiple neighbors values", {
  data <- new_cubist_data(n = 50, p = 3)
  mod <- cubist(data$x, data$y)

  expect_snapshot(
    predict(mod, data$x, neighbors = c(1, 2)),
    error = TRUE
  )
})

test_that("predict.cubist errors when neighbors > 9", {
  data <- new_cubist_data(n = 50, p = 3)
  mod <- cubist(data$x, data$y)

  expect_snapshot(
    predict(mod, data$x, neighbors = 10),
    error = TRUE
  )
})

test_that("predict.cubist works on new data with same structure", {
  train <- new_cubist_data(n = 80, p = 5, seed = 123)
  test <- new_cubist_data(n = 20, p = 5, seed = 456)

  mod <- cubist(train$x, train$y)
  preds <- predict(mod, test$x)

  expect_type(preds, "double")
  expect_length(preds, 20)
})

test_that("predict.cubist works with factor predictors", {
  set.seed(3194)
  x_train <- data.frame(
    num = rnorm(80),
    fac = factor(sample(letters[1:3], 80, replace = TRUE))
  )
  y_train <- ifelse(x_train$fac == "a", 1, 2) + x_train$num

  x_test <- data.frame(
    num = rnorm(20),
    fac = factor(
      sample(letters[1:3], 20, replace = TRUE),
      levels = letters[1:3]
    )
  )

  mod <- cubist(x_train, y_train)
  preds <- predict(mod, x_test)

  expect_type(preds, "double")
  expect_length(preds, 20)
})

# --- bad_att_index() tests ---

test_that("bad_att_index returns integer(0) when no bad attributes", {
  # Normal output with no errors
  output <- "Some normal output\nNo errors here"

  result <- Cubist:::bad_att_index(output)
  expect_equal(result, integer(0))
})

test_that("bad_att_index parses bad value errors from output", {
  # Simulated output with bad value error (single string with newlines)
  output <- paste(
    "*** line 5 of `undefined.cases':",
    "    bad value of 'unknown' for attribute 'category'",
    sep = "\n"
  )

  result <- Cubist:::bad_att_index(output)
  expect_equal(result, 5L)
})

test_that("bad_att_index handles multiple bad values", {
  output <- paste(
    "*** line 3 of `undefined.cases':",
    "    bad value of 'x' for attribute 'a'",
    "*** line 7 of `undefined.cases':",
    "    bad value of 'y' for attribute 'b'",
    sep = "\n"
  )

  result <- Cubist:::bad_att_index(output)
  expect_equal(result, c(3L, 7L))
})

test_that("neighbors = 0 does not modify model", {
  data <- new_cubist_data(n = 50, p = 3)
  mod <- cubist(data$x, data$y)
  original_model <- mod$model

  preds <- predict(mod, data$x, neighbors = 0)

  # Model should not be modified
  expect_equal(mod$model, original_model)
})

test_that("predict works with tibble newdata", {
  skip_if_not_installed("dplyr")

  data <- new_cubist_data(n = 50, p = 3)
  mod <- cubist(data$x, data$y)

  newdata_tbl <- dplyr::as_tibble(data$x)
  preds <- predict(mod, newdata_tbl)

  expect_type(preds, "double")
  expect_length(preds, 50)
})