R/RLearner_classif_gausspr.R

In guillermozbta/s2: Machine Learning in R

#' @export
makeRLearner.classif.gausspr = function() {
  makeRLearnerClassif(
    cl = "classif.gausspr",
    package = "kernlab",
    # FIXME: stringdot pars and check order, scale and offset limits
    par.set = makeParamSet(
      makeLogicalLearnerParam(id = "scaled", default = TRUE),
      makeDiscreteLearnerParam(id = "kernel", default = "rbfdot",
        values = c("vanilladot", "polydot", "rbfdot", "tanhdot", "laplacedot",
          "besseldot", "anovadot", "splinedot")),
      makeNumericLearnerParam(id = "sigma",
        lower = 0, requires = quote(kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"))),
      makeIntegerLearnerParam(id = "degree", default = 3L, lower = 1L,
        requires = quote(kernel %in% c("polydot", "anovadot", "besseldot"))),
      makeNumericLearnerParam(id = "scale", default = 1, lower = 0,
        requires = quote(kernel %in% c("polydot", "tanhdot"))),
      makeNumericLearnerParam(id = "offset", default = 1,
        requires = quote(kernel %in% c("polydot", "tanhdot"))),
      makeIntegerLearnerParam(id = "order", default = 1L,
        requires = quote(kernel == "besseldot")),
      makeNumericLearnerParam(id = "tol", default = 0.001, lower = 0),
      makeLogicalLearnerParam(id = "fit", default = TRUE)
    ),
    par.vals = list(fit = FALSE),
    properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
    name = "Gaussian Processes",
    short.name = "gausspr",
    note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `gausspr`.
    Note that `fit` has been set to `FALSE` by default for speed.",
    callees = "gausspr"
  )
}

#' @export
trainLearner.classif.gausspr = function(.learner, .task, .subset, .weights = NULL,
  degree, offset, scale, sigma, order, length, lambda, normalized,  ...) {
  kpar = learnerArgsToControl(list, degree, offset, scale, sigma, order, length, lambda, normalized)
  f = getTaskFormula(.task)
  pm = .learner$predict.type == "prob"
  if (base::length(kpar) > 0L)
    kernlab::gausspr(f, data = getTaskData(.task, .subset), kpar = kpar, prob.model = pm, ...)
  else
    kernlab::gausspr(f, data = getTaskData(.task, .subset), prob.model = pm, ...)
}

#' @export
predictLearner.classif.gausspr = function(.learner, .model, .newdata, ...) {
  type = switch(.learner$predict.type, prob = "probabilities", "response")
  kernlab::predict(.model$learner.model, newdata = .newdata, type = type, ...)
}