test_classif_rknn.R

test_that("classif_rknn", {
  requirePackagesOrSkip("rknn", default.method = "load")

  k = c(2L, 4L)
  r = c(100L, 100L)
  mtry = c(2L, 3L)
  parset.grid = expand.grid(k = k, r = r, mtry = mtry)
  parset.list = apply(parset.grid, MARGIN = 1L, as.list)
  # rknn needs integer seed for reproducibility
  parset.list = lapply(parset.list, function(x) c(x, seed = 2015L))
  parset.list = c(parset.list, list(list(seed = 2015L)))

  old.predicts.list = list()

  for (i in seq_along(parset.list)) {
    parset = parset.list[[i]]
    train = multiclass.train
    target = train[, multiclass.target]
    train[, multiclass.target] = NULL
    test = multiclass.test
    test[, multiclass.target] = NULL
    pars = list(data = train, y = target, newdata = test)
    pars = c(pars, parset)
    p = do.call(rknn::rknn, pars)$pred
    old.predicts.list[[i]] = p
  }

  testSimpleParsets("classif.rknn", multiclass.df, multiclass.target,
    multiclass.train.inds,
    old.predicts.list, parset.list)

  tt = function(formula, data, k = 1L, r = 500L, mtry = 2L, seed = 2015L,
    cluster = NULL) {
    return(list(formula = formula, data = data, k = k, r = r, mtry = mtry,
      seed = seed, cluster = cluster))
  }

  tp = function(model, newdata) {
    target = as.character(model$formula)[2L]
    train = model$data
    y = train[, target]
    train[, target] = NULL
    newdata[, target] = NULL
    rknn::rknn(data = train, y = y, newdata = newdata, k = model$k, r = model$r,
      mtry = model$mtry, seed = model$seed, cluster = model$cluster)$pred
  }

  testCVParsets(t.name = "classif.rknn", df = multiclass.df,
    target = multiclass.target, tune.train = tt, tune.predict = tp,
    parset.list = parset.list)
})