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#' Pre-process data
#'
#' Function aggregates all pre-processing algorithms for bias mitigation. User passes unified arguments and specifies type to receive transformed \code{data.frame}
#'
#' @param data \code{data.frame}
#' @param protected factor, protected attribute (sensitive variable) containing information about gender, race etc...
#' @param y numeric, numeric values of predicted variable. 1 should denote favorable outcome.
#' @param type character, type of pre-processing algorithm to be used, one of:
#' \itemize{
#' \item{resample_uniform}
#' \item{resample_preferential}
#' \item{reweight}
#' \item{disparate_impact_remover}
#' }
#' @param ... other parameters passed to pre-processing algorithms
#'
#' @return modified data (\code{data.frame}). In case of type = 'reweight' data has feature `_weights_` containing weights that need to be passed to model.
#' In other cases data is ready to be passed as training data to a model.
#' @export
#'
#' @examples
#' data("german")
#'
#' pre_process_data(german,
#' german$Sex,
#' as.numeric(german$Risk) - 1,
#' type = "disparate_impact_remover",
#' features_to_transform = "Age"
#' )
pre_process_data <- function(data, protected, y, type = "resample_uniform", ...) {
switch(type,
resample_uniform = {
return(data[resample(protected, y, ...), ])
},
resample_preferential = {
return(data[resample(protected, y, type = "preferential", ...), ])
},
reweight = {
data$`_weights_` <- reweight(protected, y)
return(data)
},
disparate_impact_remover = {
return(disparate_impact_remover(data, protected, ...))
}
)
# if not in switch:
stop("type must be equal to one of supported types, see documentation: ?pre_process_data")
}
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