% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PipeOpChunk.R
\name{mlr_pipeops_chunk}
\alias{mlr_pipeops_chunk}
\alias{PipeOpChunk}
\title{Chunk Input into Multiple Outputs}
\format{
\code{\link[R6:R6Class]{R6Class}} object inheriting from \code{\link{PipeOp}}.
}
\description{
Chunks its input into \code{outnum} chunks.
Creates \code{outnum} \code{\link[mlr3:Task]{Task}}s during training, and
simply passes on the input during \code{outnum} times during prediction.
}
\section{Construction}{


\if{html}{\out{<div class="sourceCode">}}\preformatted{PipeOpChunk$new(outnum, id = "chunk", param_vals = list())
}\if{html}{\out{</div>}}
\itemize{
\item \code{outnum} :: \code{numeric(1)}\cr
Number of output channels, and therefore number of chunks created.
\item \code{id} :: \code{character(1)}\cr
Identifier of resulting object, default \code{"chunk"}.
\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}{

\code{\link{PipeOpChunk}} has one input channel named \code{"input"}, taking a \code{\link[mlr3:Task]{Task}} both during training and prediction.

\code{\link{PipeOpChunk}} has multiple output channels depending on the \code{options} construction argument, named \code{"output1"}, \code{"output2"}, ...
All output channels produce (respectively disjoint, random) subsets of the input \code{\link[mlr3:Task]{Task}} during training, and
pass on the original \code{\link[mlr3:Task]{Task}} during prediction.
}

\section{State}{

The \verb{$state} is left empty (\code{list()}).
}

\section{Parameters}{

\itemize{
\item \code{shuffle} :: \code{logical(1)} \cr
Should the data be shuffled before chunking? Initialized to \code{TRUE}.
}
}

\section{Internals}{

Uses the \code{\link[mlr3misc:chunk_vector]{mlr3misc::chunk_vector()}} function.
}

\section{Fields}{

Only fields inherited from \code{\link{PipeOp}}.
}

\section{Methods}{

Only methods inherited from \code{\link{PipeOp}}.
}

\examples{
library("mlr3")

task = tsk("wine")
opc = mlr_pipeops$get("chunk", 2)

# watch the row number: 89 during training (task is chunked)...
opc$train(list(task))

# ... 178 during predict (task is copied)
opc$predict(list(task))
}
\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_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_randomprojection}},
\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}
