Nothing

```
#' @title Yeo-Johnson Transformation of Numeric Features
#'
#' @usage NULL
#' @name mlr_pipeops_yeojohnson
#' @format [`R6Class`] object inheriting from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @description
#' Conducts a Yeo-Johnson transformation on numeric features. It therefore estimates
#' the optimal value of lambda for the transformation.
#' See [`bestNormalize::yeojohnson()`] for details.
#'
#' @section Construction:
#' ```
#' PipeOpYeoJohnson$new(id = "yeojohnson", param_vals = list())
#' ```
#'
#' * `id` :: `character(1)`\cr
#' Identifier of resulting object, default `"yeojohnson"`.
#' * `param_vals` :: named `list`\cr
#' List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default `list()`.
#'
#' @section Input and Output Channels:
#' Input and output channels are inherited from [`PipeOpTaskPreproc`].
#'
#' The output is the input [`Task`][mlr3::Task] with all affected numeric features replaced by their transformed versions.
#'
#' @section State:
#' The `$state` is a named `list` with the `$state` elements inherited from [`PipeOpTaskPreproc`],
#' as well as a list of class `yeojohnson` for each column, which is transformed.
#'
#' @section Parameters:
#' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
#' * `eps` :: `numeric(1)` \cr
#' Tolerance parameter to identify the lambda parameter as zero.
#' For details see [`yeojohnson()`][bestNormalize::yeojohnson].
#' * `standardize` :: `logical` \cr
#' Whether to center and scale the transformed values to attempt a standard
#' normal distribution. For details see [`yeojohnson()`][bestNormalize::yeojohnson].
#' * `lower` :: `numeric(1)` \cr
#' Lower value for estimation of lambda parameter.
#' For details see [`yeojohnson()`][bestNormalize::yeojohnson].
#' * `upper` :: `numeric(1)` \cr
#' Upper value for estimation of lambda parameter.
#' For details see [`yeojohnson()`][bestNormalize::yeojohnson].
#'
#' @section Internals:
#' Uses the [`bestNormalize::yeojohnson`] function.
#'
#' @section Methods:
#' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @examples
#' library("mlr3")
#'
#' task = tsk("iris")
#' pop = po("yeojohnson")
#'
#' task$data()
#' pop$train(list(task))[[1]]$data()
#'
#' pop$state
#' @family PipeOps
#' @seealso https://mlr3book.mlr-org.com/list-pipeops.html
#' @include PipeOpTaskPreproc.R
#' @export
PipeOpYeoJohnson = R6Class("PipeOpYeoJohnson",
inherit = PipeOpTaskPreproc,
public = list(
initialize = function(id = "yeojohnson", param_vals = list()) {
ps = ParamSet$new(params = list(
ParamDbl$new("eps", default = 0.001, lower = 0, tags = c("train", "yj")),
ParamLgl$new("standardize", default = TRUE, tags = c("train", "yj")),
ParamDbl$new("lower", tags = c("train", "yj")),
ParamDbl$new("upper", tags = c("train", "yj"))
))
super$initialize(id, param_set = ps, param_vals = param_vals,
packages = "bestNormalize", feature_types = c("numeric", "integer"))
}
),
private = list(
.train_dt = function(dt, levels, target) {
bc = lapply(dt, FUN = function(x) {
invoke(bestNormalize::yeojohnson, x, .args = self$param_set$get_values(tags = "yj"))
})
for (j in names(bc)) {
set(dt, j = j, value = bc[[j]]$x.t)
bc[[j]]$x.t = NULL
bc[[j]]$x = NULL
}
self$state = list(bc = bc)
dt
},
.predict_dt = function(dt, levels) {
for (j in colnames(dt)) {
set(dt, j = j,
value = stats::predict(self$state$bc[[j]], newdata = dt[[j]]))
}
dt
}
)
)
mlr_pipeops$add("yeojohnson", PipeOpYeoJohnson)
```

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