Nothing
#' @title Kernelized Principle Component Analysis
#'
#' @usage NULL
#' @name mlr_pipeops_kernelpca
#' @format [`R6Class`][R6::R6Class] object inheriting from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @description
#' Extracts kernel principle components from data. Only affects numerical features.
#' See [kernlab::kpca] for details.
#'
#' @section Construction:
#' ```
#' PipeOpKernelPCA$new(id = "kernelpca", param_vals = list())
#' ```
#'
#' * `id` :: `character(1)`\cr
#' Identifier of resulting object, default `"kernelpca"`.
#' * `param_vals` :: named `list`\cr
#' List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default `list()`.
#'
#' @section Input and Output Channels:
#' Input and output channels are inherited from [`PipeOpTaskPreproc`].
#'
#' The output is the input [`Task`][mlr3::Task] with all affected numeric parameters replaced by their principal components.
#'
#' @section State:
#' The `$state` is a named `list` with the `$state` elements inherited from [`PipeOpTaskPreproc`],
#' as well as the returned [`S4`] object of the function [kernlab::kpca()].
#'
#' The `@rotated` slot of the `"kpca"` object is overwritten with an empty matrix for memory efficiency.
#'
#' The slots of the [`S4`] object can be accessed by accessor function. See [kernlab::kpca].
#'
#' @section Parameters:
#' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
#' * `kernel` :: `character(1)`\cr
#' The standard deviations of the principal components. See [`kpca()`][kernlab::kpca].
#' * `kpar` :: `list`\cr
#' List of hyper-parameters that are used with the kernel function. See [`kpca()`][kernlab::kpca].
#' * `features` :: `numeric(1)`\cr
#' Number of principal components to return. Default 0 means that all
#' principal components are returned. See [`kpca()`][kernlab::kpca].
#' * `th` :: `numeric(1)`\cr
#' The value of eigenvalue under which principal components are ignored. Default is 0.0001. See [`kpca()`][kernlab::kpca].
#' * `na.action` :: `function`\cr
#' Function to specify NA action. Default is [`na.omit`]. See [`kpca()`][kernlab::kpca].
#'
#' @section Internals:
#' Uses the [`kpca()`][kernlab::kpca] function.
#'
#' @section Methods:
#' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @examples
#' \dontshow{ if (requireNamespace("kernlab")) \{ }
#' library("mlr3")
#'
#' task = tsk("iris")
#' pop = po("kernelpca", features = 3) # only keep top 3 components
#'
#' task$data()
#' pop$train(list(task))[[1]]$data()
#' \dontshow{ \} }
#' @family PipeOps
#' @template seealso_pipeopslist
#' @include PipeOpTaskPreproc.R
#' @export
PipeOpKernelPCA = R6Class("PipeOpKernelPCA",
inherit = PipeOpTaskPreproc,
public = list(
initialize = function(id = "kernelpca", param_vals = list()) {
ps = ps(
kernel = p_fct(default = "rbfdot", levels = c("rbfdot", "polydot",
"vanilladot", "tanhdot", "laplacedot", "besseldot", "anovadot", "splinedot"), tags = c("train", "kpca")),
kpar = p_uty(tags = c("train", "kpca")),
features = p_int(default = 0, lower = 0, tags = c("train", "kpca")),
th = p_dbl(default = 1e-04, lower = 0, tags = c("train", "kpca")),
na.action = p_uty(default = stats::na.omit, tags = c("train", "kpca"))
)
super$initialize(id, param_set = ps, param_vals = param_vals,
packages = "kernlab", feature_types = c("numeric", "integer"))
}
),
private = list(
.train_dt = function(dt, levels, target) {
pcr = invoke(kernlab::kpca, as.matrix(dt), .args = self$param_set$get_values(tags = "kpca"))
self$state$pcr = pcr
self$state$pcr@rotated = matrix(numeric(0))
kernlab::rotated(pcr)
},
.predict_dt = function(dt, levels) {
kernlab::predict(self$state$pcr, as.matrix(dt))
}
)
)
mlr_pipeops$add("kernelpca", PipeOpKernelPCA)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.