test_that("awe_explore summarises a data frame", {
  e <- awe_explore(mtcars)
  expect_s3_class(e, "AWEExplore")
  expect_equal(e$n_rows, nrow(mtcars))
  expect_equal(e$n_cols, ncol(mtcars))
})

test_that("awe_preprocess imputes and splits", {
  d <- mtcars
  d$mpg[1:3] <- NA
  sp <- awe_preprocess(d, target = "mpg", test_prop = 0.2, seed = 1)
  expect_false(anyNA(sp$train))
  expect_false(anyNA(sp$test))
  expect_equal(nrow(sp$train) + nrow(sp$test), nrow(d))
})

test_that("awe_preprocess stratified split preserves class proportions", {
  sp <- awe_preprocess(iris, target = "Species", test_prop = 0.3, stratify = TRUE, seed = 1)
  train_prop <- prop.table(table(sp$train$Species))
  test_prop <- prop.table(table(sp$test$Species))
  expect_equal(as.numeric(train_prop), as.numeric(test_prop), tolerance = 0.05)
  expect_setequal(levels(droplevels(sp$test$Species)), levels(iris$Species))
})

test_that("awe_preprocess stratify = FALSE still returns a valid split", {
  sp <- awe_preprocess(iris, target = "Species", test_prop = 0.3, stratify = FALSE, seed = 1)
  expect_equal(nrow(sp$train) + nrow(sp$test), nrow(iris))
})

test_that("awe_model selects a regression model", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  expect_s3_class(m, "AWEModel")
  expect_equal(m$target_type, "continuous")
  expect_true(m$best_name %in% names(m$candidates_fitted))
  expect_true(all(c("linear_regression", "decision_tree") %in% m$leaderboard$model))
})

test_that("awe_model selects a classification model", {
  d <- iris
  m <- awe_model(d, target = "Species", k = 3, seed = 1)
  expect_s3_class(m, "AWEModel")
  expect_equal(m$target_type, "multiclass")
})

test_that("awe_wrap + predict works end to end", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  w <- awe_wrap(m)
  expect_s3_class(w, "AWEWrapper")
  p <- predict(w)
  expect_length(p, nrow(mtcars))
})

test_that("awe_explain computes importance and pdp", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  w <- awe_wrap(m)
  ex <- awe_explain(w, n_permutations = 2)
  expect_s3_class(ex, "AWEExplainer")
  expect_true(nrow(ex$importance) > 0)
  expect_true(length(ex$pdp) > 0)
})

test_that("awe_evaluate computes regression and classification metrics", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  w <- awe_wrap(m)
  ev <- awe_evaluate(w, newdata = mtcars)
  expect_s3_class(ev, "AWEEvaluation")
  expect_true(is.finite(ev$metrics$RMSE))

  d <- iris
  mc <- awe_model(d, target = "Species", k = 3, seed = 1)
  wc <- awe_wrap(mc)
  evc <- awe_evaluate(wc, newdata = d)
  expect_true(evc$metrics$accuracy >= 0 && evc$metrics$accuracy <= 1)
})

test_that("full awe() pipeline runs end to end on a regression target", {
  fit <- awe(mtcars, target = "mpg", k = 3, explain = TRUE, seed = 1)
  expect_s3_class(fit, "awe")
  expect_true(!is.null(fit$story))
  p <- predict(fit)
  expect_length(p, nrow(fit$preprocess$train))
})

test_that("full awe() pipeline runs end to end on a classification target", {
  fit <- awe(iris, target = "Species", k = 3, explain = TRUE, seed = 1)
  expect_s3_class(fit, "awe")
  expect_equal(fit$model$target_type, "multiclass")
})

test_that("awe_report writes a markdown report", {
  fit <- awe(mtcars, target = "mpg", k = 3, explain = TRUE, seed = 1)
  path <- awe_report(fit, file = file.path(tempdir(), "test_report"), format = "md")
  expect_true(file.exists(path))
})

test_that("awe_select_features filters and scores predictors", {
  fs <- awe_select_features(mtcars, target = "mpg", top_n = 5)
  expect_s3_class(fs, "AWEFeatureSelection")
  expect_length(fs$selected, 5)
  expect_true(all(fs$selected %in% names(mtcars)))
  expect_true(nrow(fs$scores) >= 5)
})

test_that("awe_select_features drops near-zero-variance and correlated predictors", {
  d <- mtcars
  d$const <- 1  # zero variance
  fs <- awe_select_features(d, target = "mpg", nzv = TRUE, correlation = TRUE, corr_cutoff = 0.9)
  expect_true("const" %in% fs$dropped$near_zero_variance || !"const" %in% fs$selected)
})

test_that("awe() with select_features = TRUE runs end to end", {
  fit <- awe(mtcars, target = "mpg", k = 3, select_features = TRUE, top_n_features = 5, seed = 1)
  expect_s3_class(fit, "awe")
  expect_s3_class(fit$feature_selection, "AWEFeatureSelection")
  expect_true(length(fit$model$predictors) <= 5)
})

test_that("awe_model respects the models argument", {
  m <- awe_model(mtcars, target = "mpg", k = 3, models = c("linear_regression", "decision_tree"), seed = 1)
  expect_true(all(m$leaderboard$model %in% c("linear_regression", "decision_tree")))
})

test_that("awe_wrap can wrap a specific named candidate", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  w <- awe_wrap(m, which = "decision_tree")
  expect_equal(w$model_name, "decision_tree")
  expect_true(inherits(w$model, "rpart"))
})

test_that("varImp works for a wrapped model", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  w <- awe_wrap(m, which = "decision_tree")
  vi <- varImp(w)
  expect_s3_class(vi, "AWEVarImp")
  expect_true(all(c("variable", "importance") %in% names(vi)))
})

test_that("plot_tree runs without error on a decision tree candidate", {
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  expect_error(plot_tree(m, which = "decision_tree"), NA)
})

test_that("plotting helpers run without error", {
  expect_error(plot_correlation(mtcars), NA)
  expect_error(plot_missingness(mtcars), NA)
  m <- awe_model(mtcars, target = "mpg", k = 3, seed = 1)
  expect_error(plot_leaderboard(m), NA)
  fs <- awe_select_features(mtcars, target = "mpg", top_n = 5)
  expect_error(plot_feature_selection(fs), NA)
})

test_that("classification model bank includes svm/naive_bayes/knn when engines are installed", {
  m <- awe_model(iris, target = "Species", k = 3, seed = 1)
  expect_true(nrow(m$leaderboard) >= 3)
})