如何创建 RLearner_regr_QRNN.R？

Question

原题

我想为分位数回归神经网络创建一个新的学习器。它不在已与 "mlr" 集成的学习方法列表中。它的格式必须是这样的 "RLearner_regr_QRNN.R"

答案被接受后添加

我想将 "quantile regression neural network" 定义为一种新型学习器，它具有特殊属性并且不适合现有方案之一。我的代码如下。代码正在运行，但是当我将它用作我的数据的学习器时，它给出了一个错误，即 'qrnn' 不是从 'namespace:qrnn' 导出的对象。非常感谢您，期待您的回音。

    makeRLearner.regr.qrnn = function() {
  makeRLearnerRegr(
    cl = "regr.qrnn",
    package = "qrnn",
    par.set = makeParamSet(
      makeIntegerLearnerParam(id = "n.hidden", default = 2L, lower = 1L),
      makeUntypedLearnerParam(id = "n.hidden2", default = NULL),
      makeUntypedLearnerParam(id = "w", default = NULL),
      makeNumericVectorLearnerParam(id = "tau",  default = c(0.1, 0.5, 0.9)),
      makeIntegerLearnerParam(id = "iter.max", default = 5000L),
      makeIntegerLearnerParam(id = "n.trials", default = 5L),
      makeNumericLearnerParam(id = "lower", default = 0),
      makeNumericVectorLearnerParam(id = "init.range", default = c(-0.5, 0.5, -0.5, 0.5, -0.5, 0.5)),
      makeUntypedLearnerParam(id = "monotone", default = NULL),
      makeNumericVectorLearnerParam(id = "eps.seq", default =c(2^(-8),2^(-12),2^(-16),2^(-20),2^(-24),2^(-28),2^(-32))),
      makeDiscreteLearnerParam(id = "Th", values =c("sigmoid", "elu", "softplus"),default = "sigmoid"),
      makeDiscreteLearnerParam(id = "Th.prime", values=c("sigmoid.prime", "elu.prime","softplus.prime", default = "sigmoid.prime")),
      makeNumericLearnerParam(id = "penalty", default = 0),
      makeIntegerLearnerParam(id = "n.errors.max", default = 10),
      makeLogicalLearnerParam(id = "trace", default = TRUE),
      makeDiscreteLearnerParam(id = "method", values =c("nlm","adam"), default = "nlm")
    ),
    par.vals = list(n.hidden=5L, penalty=0),
    properties = c("numerics", "factors", "ordered", "oobpreds", "featimp", "se", "weights"),
    name = "QRNN",
    short.name = "qrnn",
    callees = "qrnn"
  )
}

#' @export
trainLearner.regr.qrnn = function(.learner, .task, .subset, .weights = NULL, ...) {
  if (is.null(.weights)) {
    f = getTaskFormula(.task)
    qrnn::qrnn(f, data = getTaskData(.task, .subset), linout = TRUE, ...)
  } else {
    f = getTaskFormula(.task)
    qrnn::qrnn(f, data = getTaskData(.task, .subset), linout = TRUE, weights = .weights, ...)
  }
}

#' @export
predictLearner.regr.qrnn = function(.learner, .model, .newdata, ...) {
  predict(.model$learner.model, newdata = .newdata, ...)[, 1L]
}

Answer 1

您可以在 our website 上找到有关如何创建自定义学习器的说明。

此外，您可能需要考虑为新的 mlr3 包创建该学习器。指令是 here.

如何创建 RLearner_regr_QRNN.R？

How can I create RLearner_regr_QRNN.R?

mlr

原题

答案被接受后添加