library(FSelectorRcpp)

context("Param search")

if (require("doParallel")) {

test_that("Exhaustive search", {

  skip_on_cran()
  if(require("rpart")) {
    library(doParallel)
    cl <- makeCluster(2)
    registerDoParallel(cl)

    evaluator <- function(subset, data, dependent = names(iris)[5]) {
      library(rpart)
      k <- 5
      splits <- runif(nrow(data))
      results <- sapply(1:k, function(i) {
        test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
        train.idx <- !test.idx
        test <- data[test.idx, , drop = FALSE]   #nolint
        train <- data[train.idx, , drop = FALSE] #nolint
        tree <- rpart(to_formula(subset, dependent), train)
        error.rate <- sum(test[[dependent]] != predict(tree, test, type = "c")) /
          nrow(test)
        return(1 - error.rate)
      })
      return(mean(results))
    }

    iris <- iris[sample.int(75), ]

    fit1 <- feature_search(
      attributes = names(iris)[-5],
      fun = evaluator, data = iris,
      mode = "exhaustive")

    fit2 <- feature_search(
      attributes = names(iris)[-5],
      fun = evaluator, data = iris,
      mode = "exhaustive", parallel = FALSE)

    fitGreedyForward <- feature_search(
      attributes = names(iris)[-5],
      fun = evaluator, data = iris,
      mode = "greedy", parallel = FALSE, type = "forward")

    fitGreedyBackward <- feature_search(
      attributes = names(iris)[-5],
      fun = evaluator, data = iris,
      mode = "greedy", parallel = FALSE, type = "backward")

    check_best <- function(fit) {
      best <- fit$best[-length(fit$best)]
      best <- names(best)[best == 1]

      list(
        best = evaluator(best, iris),
        fit = tail(as.numeric(unlist(fit1$best)), 1)
      )
    }

    f1 <- check_best(fit1)
    f2 <- check_best(fit2)
    fgf <- check_best(fitGreedyForward)
    fgb <- check_best(fitGreedyForward)

    expect_equal(f1$best, f1$fit)
    expect_equal(f2$best, f2$fit)
    expect_equal(fgb$best, fgb$fit)

    expect_error(feature_search(
      attributes = character(),
      fun = evaluator, data = iris,
      mode = "exhaustive"))

    stopCluster(cl)
    registerDoSEQ()
  }

})

}

# get_children

test_that("get_children works as expected.", {
  x <- FSelectorRcpp:::get_children(c(1, 1, 0, 0), "forward")
  expF <- rbind(c(1, 1, 1, 0), c(1, 1, 0, 1))
  expect_equal(x, expF)

  x <- FSelectorRcpp:::get_children(c(1, 1, 0, 0), "backward")
  expB <- rbind(c(0, 1, 0, 0), c(1, 0, 0, 0))
  expect_equal(x, expB)

  x <- FSelectorRcpp:::get_children(c(1, 1, 0, 0), "both")
  expect_equal(x, rbind(expF, expB))
})