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#' @title Create control structures for feature selection.
#'
#' @description
#' Feature selection method used by [selectFeatures].\cr
#' The methods used here follow a wrapper approach, described in
#' Kohavi and John (1997) (see references).
#'
#' The following optimization algorithms are available:
#' \describe{
#' \item{FeatSelControlExhaustive}{Exhaustive search. All feature sets (up to a certain number
#' of features `max.features`) are searched.}
#' \item{FeatSelControlRandom}{Random search. Features vectors are randomly drawn,
#' up to a certain number of features `max.features`.
#' A feature is included in the current set with probability `prob`.
#' So we are basically drawing (0,1)-membership-vectors, where each element
#' is Bernoulli(`prob`) distributed.}
#' \item{FeatSelControlSequential}{Deterministic forward or backward search. That means extending
#' (forward) or shrinking (backward) a feature set.
#' Depending on the given `method` different approaches are taken.\cr
#' `sfs` Sequential Forward Search: Starting from an empty model, in each step the feature increasing
#' the performance measure the most is added to the model.\cr
#' `sbs` Sequential Backward Search: Starting from a model with all features, in each step the feature
#' decreasing the performance measure the least is removed from the model.\cr
#' `sffs` Sequential Floating Forward Search: Starting from an empty model, in each step the algorithm
#' chooses the best model from all models with one additional feature and from all models with one
#' feature less.\cr
#' `sfbs` Sequential Floating Backward Search: Similar to `sffs` but starting with a full model.}
#' \item{FeatSelControlGA}{Search via genetic algorithm.
#' The GA is a simple (`mu`, `lambda`) or (`mu` + `lambda`) algorithm,
#' depending on the `comma` setting.
#' A comma strategy selects a new population of size `mu` out of the
#' `lambda` > `mu` offspring.
#' A plus strategy uses the joint pool of `mu` parents and `lambda` offspring
#' for selecting `mu` new candidates.
#' Out of those `mu` features, the new `lambda` features are generated
#' by randomly choosing pairs of parents. These are crossed over and `crossover.rate`
#' represents the probability of choosing a feature from the first parent instead of
#' the second parent.
#' The resulting offspring is mutated, i.e., its bits are flipped with
#' probability `mutation.rate`. If `max.features` is set, offspring are
#' repeatedly generated until the setting is satisfied.}
#' }
#'
#' @param same.resampling.instance (`logical(1)`)\cr
#' Should the same resampling instance be used for all evaluations to reduce variance?
#' Default is `TRUE`.
#' @template arg_imputey
#' @param maxit (`integer(1)`)\cr
#' Maximal number of iterations. Note, that this is usually not equal to the number
#' of function evaluations.
#' @param max.features (`integer(1)`)\cr
#' Maximal number of features.
#' @param tune.threshold (`logical(1)`)\cr
#' Should the threshold be tuned for the measure at hand, after each feature set evaluation,
#' via [tuneThreshold]?
#' Only works for classification if the predict type is \dQuote{prob}.
#' Default is `FALSE`.
#' @param tune.threshold.args ([list])\cr
#' Further arguments for threshold tuning that are passed down to [tuneThreshold].
#' Default is none.
#' @template arg_log_fun
#' @param prob (`numeric(1)`)\cr
#' Parameter of the random feature selection. Probability of choosing a feature.
#' @param method (`character(1)`)\cr
#' Parameter of the sequential feature selection. A character representing the method. Possible
#' values are `sfs` (forward search), `sbs` (backward search), `sffs`
#' (floating forward search) and `sfbs` (floating backward search).
#' @param alpha (`numeric(1)`)\cr
#' Parameter of the sequential feature selection.
#' Minimal required value of improvement difference for a forward / adding step.
#' Default is 0.01.
#' @param beta (`numeric(1)`)\cr
#' Parameter of the sequential feature selection.
#' Minimal required value of improvement difference for a backward / removing step.
#' Negative values imply that you allow a slight decrease for the removal of a feature.
#' Default is -0.001.
#' @param mu (`integer(1)`)\cr
#' Parameter of the GA feature selection. Size of the parent population.
#' @param lambda (`integer(1)`)\cr
#' Parameter of the GA feature selection. Size of the children population (should be smaller
#' or equal to `mu`).
#' @param crossover.rate (`numeric(1)`)\cr
#' Parameter of the GA feature selection. Probability of choosing a bit from the first parent
#' within the crossover mutation.
#' @param mutation.rate (`numeric(1)`)\cr
#' Parameter of the GA feature selection. Probability of flipping a feature bit, i.e. switch
#' between selecting / deselecting a feature.
#' @param comma (`logical(1)`)\cr
#' Parameter of the GA feature selection, indicating whether to use a (`mu`, `lambda`)
#' or (`mu` + `lambda`) GA. The default is `FALSE`.
#' @return ([FeatSelControl]). The specific subclass is one of
#' [FeatSelControlExhaustive], [FeatSelControlRandom],
#' [FeatSelControlSequential], [FeatSelControlGA].
#' @references Ron Kohavi and George H. John,
#' Wrappers for feature subset selection, Artificial Intelligence Volume 97, 1997, 273-324.
#' <http://ai.stanford.edu/~ronnyk/wrappersPrint.pdf>.\cr
#' @family featsel
#' @name FeatSelControl
#' @rdname FeatSelControl
#' @aliases FeatSelControlExhaustive FeatSelControlRandom FeatSelControlSequential FeatSelControlGA
NULL
makeFeatSelControl = function(same.resampling.instance, impute.val = NULL, maxit, max.features,
tune.threshold = FALSE, tune.threshold.args = list(), log.fun = "default", ..., cl) {
maxit = asCount(maxit, na.ok = TRUE, positive = TRUE)
max.features = asCount(max.features, na.ok = TRUE, positive = TRUE)
if (identical(log.fun, "default")) {
log.fun = logFunFeatSel
} else if (identical(log.fun, "memory")) {
log.fun = logFunTuneMemory
}
x = makeOptControl(same.resampling.instance, impute.val, tune.threshold, tune.threshold.args, log.fun, ...)
x$maxit = maxit
x$max.features = max.features
addClasses(x, c(cl, "FeatSelControl"))
}
#' @export
print.FeatSelControl = function(x, ...) {
catf("FeatSel control: %s", class(x)[1])
catf("Same resampling instance: %s", x$same.resampling.instance)
catf("Imputation value: %s", ifelse(is.null(x$impute.val), "<worst>", sprintf("%g", x$impute.val)))
if (is.na(x$max.features)) {
catf("Max. features: <not used>")
} else {
catf("Max. features: %i", x$max.features)
}
catf("Max. iterations: %i", x$maxit)
catf("Tune threshold: %s", x$tune.threshold)
if (length(x$extra.args)) {
catf("Further arguments: %s", convertToShortString(x$extra.args))
} else {
catf("Further arguments: <not used>")
}
}
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