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#' @title SMOTE Balancing
#'
#' @usage NULL
#' @name mlr_pipeops_smote
#' @format [`R6Class`][R6::R6Class] object inheriting from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @description
#' Generates a more balanced data set by creating
#' synthetic instances of the minority class using the SMOTE algorithm.
#' The algorithm samples for each minority instance a new data point based on the `K` nearest
#' neighbors of that data point.
#' It can only be applied to tasks with purely numeric features.
#' See [`smotefamily::SMOTE`] for details.
#'
#' @section Construction:
#' ```
#' PipeOpSmote$new(id = "smote", param_vals = list())
#' ```
#'
#' * `id` :: `character(1)`\cr
#' Identifier of resulting object, default `"smote"`.
#' * `param_vals` :: named `list`\cr
#' List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default `list()`.
#'
#' @section Input and Output Channels:
#' Input and output channels are inherited from [`PipeOpTaskPreproc`].
#'
#' The output during training is the input [`Task`][mlr3::Task] with added synthetic rows for the minority class.
#' The output during prediction is the unchanged input.
#'
#' @section State:
#' The `$state` is a named `list` with the `$state` elements inherited from [`PipeOpTaskPreproc`].
#'
#' @section Parameters:
#' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
#' * `K` :: `numeric(1)` \cr
#' The number of nearest neighbors used for sampling new values.
#' See [`SMOTE()`][`smotefamily::SMOTE`].
#' * `dup_size` :: `numeric` \cr
#' Desired times of synthetic minority instances over the original number of
#' majority instances. See [`SMOTE()`][`smotefamily::SMOTE`].
#'
#' @section Fields:
#' Only fields inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @section Methods:
#' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @references
#' `r format_bib("chawla_2002")`
#'
#' @family PipeOps
#' @template seealso_pipeopslist
#' @include PipeOpTaskPreproc.R
#' @export
#' @examples
#' \dontshow{ if (requireNamespace("smotefamily")) \{ }
#' library("mlr3")
#'
#' # Create example task
#' data = smotefamily::sample_generator(1000, ratio = 0.80)
#' data$result = factor(data$result)
#' task = TaskClassif$new(id = "example", backend = data, target = "result")
#' task$data()
#' table(task$data()$result)
#'
#' # Generate synthetic data for minority class
#' pop = po("smote")
#' smotedata = pop$train(list(task))[[1]]$data()
#' table(smotedata$result)
#' \dontshow{ \} }
PipeOpSmote = R6Class("PipeOpSmote",
inherit = PipeOpTaskPreproc,
public = list(
initialize = function(id = "smote", param_vals = list()) {
ps = ps(
K = p_int(lower = 1, default = 5, tags = c("train", "smote")),
# dup_size = 0 leads to behaviour different from 1, 2, 3, ..., because it means "autodetect",
# so it is a 'special_vals'.
dup_size = p_int(lower = 1, default = 0, special_vals = list(0), tags = c("train", "smote"))
)
super$initialize(id, param_set = ps, param_vals = param_vals,
packages = "smotefamily", can_subset_cols = FALSE, tags = "imbalanced data")
}
),
private = list(
.train_task = function(task) {
assert_true(all(task$feature_types$type == "numeric"))
cols = private$.select_cols(task)
if (!length(cols)) {
self$state = list(dt_columns = cols)
return(task)
}
dt = task$data(cols = cols)
# calculate synthetic data
st = setDT(invoke(smotefamily::SMOTE, X = dt, target = task$truth(),
.args = self$param_set$get_values(tags = "smote"),
.opts = list(warnPartialMatchArgs = FALSE))$syn_data)
# rename target column and fix character conversion for TaskClassif
if (task$task_type == "classif") {
st[["class"]] = as_factor(st[["class"]], levels = task$class_names)
}
setnames(st, "class", task$target_names)
task$rbind(st)
}
)
)
mlr_pipeops$add("smote", PipeOpSmote)
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