cpoApplyFun: Apply a Function Element-Wise
In mlrCPO: Composable Preprocessing Operators and Pipelines for Machine Learning

cpoApplyFun

R Documentation

Apply a Function Element-Wise

Description

This is a CPOConstructor to be used to create a CPO. It is called like any R function and returns the created CPO.

The function must either vectorize over the given data, or will be applied to each data element on its own.

It must not change the type of the data, i.e. numeric data must remain numeric etc.

If the function can only handle a subset of the given columns, e.g. only a certain type, use affect.* arguments.

Usage

cpoApplyFun(
  fun,
  param = NULL,
  vectorize = TRUE,
  make.factors = TRUE,
  id,
  export = "export.default",
  affect.type = NULL,
  affect.index = integer(0),
  affect.names = character(0),
  affect.pattern = NULL,
  affect.invert = FALSE,
  affect.pattern.ignore.case = FALSE,
  affect.pattern.perl = FALSE,
  affect.pattern.fixed = FALSE
)

Arguments

`fun`	[`function`] The function to apply. If `vectorize` is `TRUE`, the argument is a vector of the whole column, `fun` must vectorize over it and return a vector of the same length; otherwise, the function gets called once for every data item, and both the function argument and the return value must have length 1. The function must take one or two arguments. If it takes two arguments, the second argument will be `param`.
`param`	[any] Optional argument to be given to `fun`. If `fun` only takes one argument, this is ignored. Default is `NULL`.
`vectorize`	[`logical(1)`] Whether to call `fun` once for each column, or once for each element. If `fun` vectorizes, it is recommended to have this set to `TRUE` for better performance. Default is `TRUE`.
`make.factors`	[`logical(1)`] Whether to turn resulting `logical` and `character` columns into `factor` columns (which are preferred by `mlr`). Default is `TRUE`.
`id`	[`character(1)`] id to use as prefix for the CPO's hyperparameters. this must be used to avoid name clashes when composing two CPOs of the same type, or with learners or other CPOS with hyperparameters with clashing names.
`export`	[`character`] Either a character vector indicating the parameters to export as hyperparameters, or one of the special values “export.all” (export all parameters), “export.default” (export all parameters that are exported by default), “export.set” (export all parameters that were set during construction), “export.default.set” (export the intersection of the “default” and “set” parameters), “export.unset” (export all parameters that were not set during construction) or “export.default.unset” (export the intersection of the “default” and “unset” parameters). Default is “export.default”.
`affect.type`	[`character` \| `NULL`] Type of columns to affect. A subset of “numeric”, “factor”, “ordered”, “other”, or `NULL` to not match by column type. Default is `NULL`.
`affect.index`	[`numeric`] Indices of feature columns to affect. The order of indices given is respected. Target column indices are not counted (since target columns are always included). Default is `integer(0)`.
`affect.names`	[`character`] Feature names of feature columns to affect. The order of names given is respected. Default is `character(0)`.
`affect.pattern`	[`character(1)` \| `NULL`] `grep` pattern to match feature names by. Default is `NULL` (no pattern matching)
`affect.invert`	[`logical(1)`] Whether to affect all features not matched by other `affect.*` parameters.
`affect.pattern.ignore.case`	[`logical(1)`] Ignore case when matching features with `affect.pattern`; see `grep`. Default is `FALSE`.
`affect.pattern.perl`	[`logical(1)`] Use Perl-style regular expressions for `affect.pattern`; see `grep`. Default is `FALSE`.
`affect.pattern.fixed`	[`logical(1)`] Use fixed matching instead of regular expressions for `affect.pattern`; see `grep`. Default is `FALSE`.

Value

[CPO].

CPOTrained State

The created state is empty.

General CPO info

This function creates a CPO object, which can be applied to Tasks, data.frames, link{Learner}s and other CPO objects using the %>>% operator.

The parameters of this object can be changed after creation using the function setHyperPars. The other hyper-parameter manipulating functins, getHyperPars and getParamSet similarly work as one expects.

If the “id” parameter is given, the hyperparameters will have this id as aprefix; this will, however, not change the parameters of the creator function.

Calling a `CPOConstructor`

CPO constructor functions are called with optional values of parameters, and additional “special” optional values. The special optional values are the id parameter, and the affect.* parameters. The affect.* parameters enable the user to control which subset of a given dataset is affected. If no affect.* parameters are given, all data features are affected by default.

Other CPOs: cpoApplyFunRegrTarget(), cpoAsNumeric(), cpoCache(), cpoCbind(), cpoCollapseFact(), cpoDropConstants(), cpoDropMostlyConstants(), cpoDummyEncode(), cpoFilterAnova(), cpoFilterCarscore(), cpoFilterChiSquared(), cpoFilterFeatures(), cpoFilterGainRatio(), cpoFilterInformationGain(), cpoFilterKruskal(), cpoFilterLinearCorrelation(), cpoFilterMrmr(), cpoFilterOneR(), cpoFilterPermutationImportance(), cpoFilterRankCorrelation(), cpoFilterRelief(), cpoFilterRfCImportance(), cpoFilterRfImportance(), cpoFilterRfSRCImportance(), cpoFilterRfSRCMinDepth(), cpoFilterSymmetricalUncertainty(), cpoFilterUnivariate(), cpoFilterVariance(), cpoFixFactors(), cpoIca(), cpoImpactEncodeClassif(), cpoImpactEncodeRegr(), cpoImputeConstant(), cpoImputeHist(), cpoImputeLearner(), cpoImputeMax(), cpoImputeMean(), cpoImputeMedian(), cpoImputeMin(), cpoImputeMode(), cpoImputeNormal(), cpoImputeUniform(), cpoImpute(), cpoLogTrafoRegr(), cpoMakeCols(), cpoMissingIndicators(), cpoModelMatrix(), cpoOversample(), cpoPca(), cpoProbEncode(), cpoQuantileBinNumerics(), cpoRegrResiduals(), cpoResponseFromSE(), cpoSample(), cpoScaleMaxAbs(), cpoScaleRange(), cpoScale(), cpoSelect(), cpoSmote(), cpoSpatialSign(), cpoTransformParams(), cpoWrap(), makeCPOCase(), makeCPOMultiplex()