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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extractFDAFeatures.R
\name{extractFDAFeatures}
\alias{extractFDAFeatures}
\title{Extract features from functional data.}
\usage{
extractFDAFeatures(obj, target = character(0L), feat.methods = list(), ...)
}
\arguments{
\item{obj}{(\link{Task} | \link{data.frame})\cr
Task or data.frame to extract functional features from.
Must contain functional features as matrix columns.}
\item{target}{(\code{character(1)})\cr
Task target column. Only necessary for data.frames
Default is \code{character(0)}.}
\item{feat.methods}{(named \link{list})\cr
List of functional features along with the desired methods for each functional feature.
\dQuote{all} applies the \link{extractFDAFeatures} method to each
functional feature.
Names of \code{feat.methods} must match column names of functional features.
Available feature extraction methods are available under family \code{fda_featextractor}.
Specifying a functional feature multiple times with different extraction methods allows
for the extraction of different features from the same functional.
Default is \code{\link[=list]{list()}} which does nothing.}
\item{...}{(any)\cr
Further hyperparameters passed on to the \code{feat.methods} specified above.}
}
\value{
(\link{list})
\itemize{
\item data | task (\link{data.frame} | \link{Task}): Extracted features, same type as obj.
\item desc (\code{extracFDAFeatDesc}): Description object. See description for details.
}
}
\description{
Extract non-functional features from functional features using various methods.
The function \link{extractFDAFeatures} performs the extraction for all functional features
via the methods specified in \code{feat.methods} and transforms all mentioned functional
(matrix) features into regular data.frame columns.
Additionally, a \dQuote{\code{extractFDAFeatDesc}} object
which contains learned coefficients and other helpful data for
re-extraction during the predict-phase is returned. This can be used with
\link{reextractFDAFeatures} in order to extract features during the prediction phase.
}
\details{
The description object contains these slots:
\itemize{
\item target (\code{character}): See argument.
\item coln (\code{character}): Colum names of data.
\item fd.cols (\code{character}): Functional feature names.
\item extractFDAFeat (\code{list}): Contains \code{feature.methods} and relevant
parameters for reextraction.
}
}
\examples{
\dontshow{ if (requireNamespace("wavelets")) \{ }
df = data.frame(x = matrix(rnorm(24), ncol = 8), y = factor(c("a", "a", "b")))
fdf = makeFunctionalData(df, fd.features = list(x1 = 1:4, x2 = 5:8), exclude.cols = "y")
task = makeClassifTask(data = fdf, target = "y")
extracted = extractFDAFeatures(task,
feat.methods = list("x1" = extractFDAFourier(), "x2" = extractFDAWavelets(filter = "haar")))
print(extracted$task)
reextractFDAFeatures(task, extracted$desc)
\dontshow{ \} }
}
\seealso{
Other fda:
\code{\link{makeExtractFDAFeatMethod}()},
\code{\link{makeExtractFDAFeatsWrapper}()}
}
\concept{fda}
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