tests/testthat/test_classif_glmnet.R

In riebetob/mlr: Machine Learning in R

context("classif_glmnet")

test_that("classif_glmnet", {
  requirePackagesOrSkip("glmnet", default.method = "load")
  parset.list = list(
    list(),
    list(alpha = 0.5),
    list(devmax = 0.8),
    list(s = 0.1)
  )

  old.predicts.list = list()
  old.probs.list = list()

  for (i in seq_along(parset.list)) {
    parset = parset.list[[i]]
    s = parset[["s"]]
    if (is.null(s))
      s = 0.01
    parset[["s"]] = NULL
    x = binaryclass.train
    y = x[, binaryclass.class.col]
    x[, binaryclass.class.col] = NULL
    pars = list(x = as.matrix(x), y = y, family = "binomial")
    pars = c(pars, parset)
    glmnet::glmnet.control(factory = TRUE)
    ctrl.args = names(formals(glmnet::glmnet.control))
    set.seed(getOption("mlr.debug.seed"))
    if (any(names(pars) %in% ctrl.args)) {
      on.exit(glmnet::glmnet.control(factory = TRUE))
      do.call(glmnet::glmnet.control, pars[names(pars) %in% ctrl.args])
      m = do.call(glmnet::glmnet, pars[!names(pars) %in% ctrl.args])
    } else {
      m = do.call(glmnet::glmnet, pars)
    }
    newx = binaryclass.test
    newx[, binaryclass.class.col] = NULL
    p = factor(predict(m, as.matrix(newx), type = "class", s = s)[, 1])
    p2 = predict(m, as.matrix(newx), type = "response", s = s)[, 1]
    old.predicts.list[[i]] = p
    old.probs.list[[i]] = 1 - p2
  }

  testSimpleParsets("classif.glmnet", binaryclass.df, binaryclass.target,
    binaryclass.train.inds, old.predicts.list, parset.list)
  testProbParsets("classif.glmnet", binaryclass.df, binaryclass.target,
    binaryclass.train.inds, old.probs.list, parset.list)

})