mlr: extractFDAFeatures – R documentation

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mlr

extractFDAFeatures

Extract features from functional data.

Description

Extract non-functional features from functional features using various methods.

The function extractFDAFeatures performs the extraction for all functional features via the methods specified in feat.methods and transforms all mentioned functional (matrix) features into regular data.frame columns. Additionally, a “extractFDAFeatDesc” object which contains learned coefficients and other helpful data for re-extraction during the predict-phase is returned. This can be used with reextractFDAFeatures in order to extract features during the prediction phase.

Usage

extractFDAFeatures(obj, target = character(0L), feat.methods = list(), ...)

Arguments

`obj`	(Task \| data.frame) Task or data.frame to extract functional features from. Must contain functional features as matrix columns.
`target`	(`character(1)`) Task target column. Only neccessary for data.frames Default is `character(0)`.
`feat.methods`	(named list) List of functional features along with the desired methods for each functional feature. “all” applies the extractFDAFeatures method to each functional feature. Names of `feat.methods` must match column names of functional features. Available feature extraction methods are available under family `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 `list()` which does nothing.
`...`	(any) Further hyperparameters passed on to the `feat.methods` specified above.

Details

The description object contains these slots:

target character: See argument.
coln character: Colum names of data.
fd.cols character: Functional feature names.
extractFDAFeat list: Contains feature.methods and relevant parameters for reextraction.

Value

(list)

`data\|task (data.frame \| Task)`	Extracted features, same type as obj.
`desc (extracFDAFeatDesc)`	Description object. See description for details.

Examples

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)

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release