% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PipeOpRandomProjection.R
\name{mlr_pipeops_randomprojection}
\alias{mlr_pipeops_randomprojection}
\alias{PipeOpRandomProjection}
\title{Project Numeric Features onto a Randomly Sampled Subspace}
\format{
\code{\link[R6:R6Class]{R6Class}} object inheriting from \code{\link{PipeOpTaskPreprocSimple}}/\code{\link{PipeOpTaskPreproc}}/\code{\link{PipeOp}}.
}
\description{
Projects numeric features onto a randomly sampled subspace. All numeric features
(or the ones selected by \code{affect_columns}) are replaced by numeric features
\code{PR1}, \code{PR2}, ... \code{PRn}

Samples with features that contain missing values result in all \code{PR1}..\code{PRn} being
NA for that sample, so it is advised to do imputation \emph{before} random projections
if missing values can be expected.
}
\section{Construction}{


\if{html}{\out{<div class="sourceCode">}}\preformatted{PipeOpRandomProjection$new(id = "randomprojection", param_vals = list())
}\if{html}{\out{</div>}}
\itemize{
\item \code{id} :: \code{character(1)}\cr
Identifier of resulting object, default \code{"randomprojection"}.
\item \code{param_vals} :: named \code{list}\cr
List of hyperparameter settings, overwriting the hyperparameter settings that
would otherwise be set during construction. Default \code{list()}.
}
}

\section{Input and Output Channels}{

Input and output channels are inherited from \code{\link{PipeOpTaskPreproc}}.

The output is the input \code{\link[mlr3:Task]{Task}} with affected numeric features
projected onto a random subspace.
}

\section{State}{

The \verb{$state} is a named \code{list} with the \verb{$state} elements inherited from \code{\link{PipeOpTaskPreproc}},
as well as an element \verb{$projection}, a \code{matrix}.
}

\section{Parameters}{

The parameters are the parameters inherited from \code{\link{PipeOpTaskPreproc}}, as well as:
\itemize{
\item \code{rank} :: \code{integer(1)}\cr
The dimension of the subspace to project onto. Initialized to 1.
}
}

\section{Internals}{

If there are \code{n} (affected) numeric features in the input \code{\link[mlr3:Task]{Task}},
then \verb{$state$projection} is a \code{rank} x \code{m} \code{matrix}. The output is calculated as
\code{input \%*\% state$projection}.

The random projection matrix is obtained through Gram-Schmidt orthogonalization
from a matrix with values standard normally distributed, which gives a distribution
that is rotation invariant, as per Eaton: Multivariate Statistics, A Vector Space
Approach, Pg. 234.
}

\section{Methods}{

Only methods inherited from \code{\link{PipeOpTaskPreprocSimple}}/\code{\link{PipeOpTaskPreproc}}/\code{\link{PipeOp}}.
}

\examples{
library("mlr3")

task = tsk("iris")
pop = po("randomprojection", rank = 2)

task$data()
pop$train(list(task))[[1]]$data()

pop$state
}
\seealso{
https://mlr-org.com/pipeops.html

Other PipeOps: 
\code{\link{PipeOp}},
\code{\link{PipeOpEnsemble}},
\code{\link{PipeOpImpute}},
\code{\link{PipeOpTargetTrafo}},
\code{\link{PipeOpTaskPreproc}},
\code{\link{PipeOpTaskPreprocSimple}},
\code{\link{mlr_pipeops}},
\code{\link{mlr_pipeops_adas}},
\code{\link{mlr_pipeops_blsmote}},
\code{\link{mlr_pipeops_boxcox}},
\code{\link{mlr_pipeops_branch}},
\code{\link{mlr_pipeops_chunk}},
\code{\link{mlr_pipeops_classbalancing}},
\code{\link{mlr_pipeops_classifavg}},
\code{\link{mlr_pipeops_classweights}},
\code{\link{mlr_pipeops_colapply}},
\code{\link{mlr_pipeops_collapsefactors}},
\code{\link{mlr_pipeops_colroles}},
\code{\link{mlr_pipeops_copy}},
\code{\link{mlr_pipeops_datefeatures}},
\code{\link{mlr_pipeops_encode}},
\code{\link{mlr_pipeops_encodeimpact}},
\code{\link{mlr_pipeops_encodelmer}},
\code{\link{mlr_pipeops_featureunion}},
\code{\link{mlr_pipeops_filter}},
\code{\link{mlr_pipeops_fixfactors}},
\code{\link{mlr_pipeops_histbin}},
\code{\link{mlr_pipeops_ica}},
\code{\link{mlr_pipeops_imputeconstant}},
\code{\link{mlr_pipeops_imputehist}},
\code{\link{mlr_pipeops_imputelearner}},
\code{\link{mlr_pipeops_imputemean}},
\code{\link{mlr_pipeops_imputemedian}},
\code{\link{mlr_pipeops_imputemode}},
\code{\link{mlr_pipeops_imputeoor}},
\code{\link{mlr_pipeops_imputesample}},
\code{\link{mlr_pipeops_kernelpca}},
\code{\link{mlr_pipeops_learner}},
\code{\link{mlr_pipeops_missind}},
\code{\link{mlr_pipeops_modelmatrix}},
\code{\link{mlr_pipeops_multiplicityexply}},
\code{\link{mlr_pipeops_multiplicityimply}},
\code{\link{mlr_pipeops_mutate}},
\code{\link{mlr_pipeops_nearmiss}},
\code{\link{mlr_pipeops_nmf}},
\code{\link{mlr_pipeops_nop}},
\code{\link{mlr_pipeops_ovrsplit}},
\code{\link{mlr_pipeops_ovrunite}},
\code{\link{mlr_pipeops_pca}},
\code{\link{mlr_pipeops_proxy}},
\code{\link{mlr_pipeops_quantilebin}},
\code{\link{mlr_pipeops_randomresponse}},
\code{\link{mlr_pipeops_regravg}},
\code{\link{mlr_pipeops_removeconstants}},
\code{\link{mlr_pipeops_renamecolumns}},
\code{\link{mlr_pipeops_replicate}},
\code{\link{mlr_pipeops_rowapply}},
\code{\link{mlr_pipeops_scale}},
\code{\link{mlr_pipeops_scalemaxabs}},
\code{\link{mlr_pipeops_scalerange}},
\code{\link{mlr_pipeops_select}},
\code{\link{mlr_pipeops_smote}},
\code{\link{mlr_pipeops_smotenc}},
\code{\link{mlr_pipeops_spatialsign}},
\code{\link{mlr_pipeops_subsample}},
\code{\link{mlr_pipeops_targetinvert}},
\code{\link{mlr_pipeops_targetmutate}},
\code{\link{mlr_pipeops_targettrafoscalerange}},
\code{\link{mlr_pipeops_textvectorizer}},
\code{\link{mlr_pipeops_threshold}},
\code{\link{mlr_pipeops_tomek}},
\code{\link{mlr_pipeops_tunethreshold}},
\code{\link{mlr_pipeops_unbranch}},
\code{\link{mlr_pipeops_updatetarget}},
\code{\link{mlr_pipeops_vtreat}},
\code{\link{mlr_pipeops_yeojohnson}}
}
\concept{PipeOps}
