feat: Add PipeOpFDASmooth

DESCRIPTION

@@ -23,7 +23,10 @@ Imports:
     R6,
     tf
 Suggests:
-    testthat (>= 3.0.0)
+    rpart,
+    testthat (>= 3.0.0),
+    pracma,
+    zoo
 Remotes:
     tidyfun/tf
 Config/testthat/edition: 3
@@ -36,6 +39,7 @@ Collate:
     'PipeOpFDAExtract.R'
     'PipeOpFDAFlatten.R'
     'PipeOpFDAInterpol.R'
+    'PipeOpFDASmooth.R'
     'TaskClassif_phoneme.R'
     'TaskRegr_dti.R'
     'TaskRegr_fuel.R'

NAMESPACE

@@ -3,6 +3,7 @@
 export(PipeOpFDAExtract)
 export(PipeOpFDAFlatten)
 export(PipeOpFDAInterpol)
+export(PipeOpFDASmooth)
 import(R6)
 import(checkmate)
 import(data.table)

R/PipeOpFDASmooth.R

@@ -0,0 +1,81 @@
+#' @title Smoothing Functional Data
+#' @name mlr_pipeops_fda.smooth
+#'
+#' @description
+#' Smoothes functional data using [`tf::tf_smooth()`].
+#' This preprocessing operator is similar to [`PipeOpFDAInterpol`], however it does not interpolate to unobserved
+#' x-values, but rather smooths the observed values.
+#'
+#' @section Parameters:
+#' The parameters are the parameters inherited from [`PipeOpTaskPreprocSimple`], as well as the following
+#' parameters:
+#' * `method` :: `character(1)`\cr
+#'   One of:
+#'   * "lowess": locally weighted scatterplot smoothing (default)
+#'   * "rollmean": rolling mean
+#'   * "rollmedian": rolling meadian
+#'   * "savgol":  Savitzky-Golay filtering
+#'
+#'   All methods but "lowess" ignore non-equidistant arg values.
+#' * `args` :: named `list()`\cr
+#'   List of named arguments that is passed to `tf_smooth()`. See the help page of `tf_smooth()` for
+#'   default values.
+#' * `verbose` :: `logical(1)`\cr
+#'   Whether to print messages during the transformation.
+#'   Is initialized to `FALSE`.
+#'
+#' @export
+#' @examples
+#' library(mlr3pipelines)
+#' task = tsk("fuel")
+#' po_smooth = po("fda.smooth", method = "rollmean", args = list(k = 5))
+#' task_smooth = po_smooth$train(list(task))[[1L]]
+#' task_smooth
+#' task_smooth$data(cols = c("NIR", "UVVIS"))
+PipeOpFDASmooth = R6Class("PipeOpFDASmooth",
+  inherit = mlr3pipelines::PipeOpTaskPreprocSimple,
+  public = list(
+    #' @description Initializes a new instance of this Class.
+    #' @param id (`character(1)`)\cr
+    #'   Identifier of resulting object, default `"fda.smooth"`.
+    #' @param param_vals (named `list`)\cr
+    #'   List of hyperparameter settings, overwriting the hyperparameter settings that would
+    #'   otherwise be set during construction. Default `list()`.
+    initialize = function(id = "fda.smooth", param_vals = list()) {
+      param_set = ps(
+        method = p_fct(default = "lowess", c("lowess", "rollmean", "rollmedian", "savgol"), tags = c("train", "predict")), # nolint
+        args = p_uty(tags = c("train", "predict", "required"),
+          custom_check = crate(function(x) check_list(x, names = "unique"))),
+        verbose = p_lgl(tags = c("train", "predict", "required"))
+      )
+
+      param_set$set_values(args = list(), verbose = FALSE)
+
+      super$initialize(
+        id = id,
+        param_set = param_set,
+        param_vals = param_vals,
+        packages = c("mlr3fda", "mlr3pipelines", "tf", "stats"),
+        feature_types = c("tfd_reg", "tfd_irreg")
+      )
+    }
+  ),
+  private = list(
+    .transform_dt = function(dt, levels) {
+      pars = self$param_set$get_values()
+
+      if (pars$verbose) {
+        map_dtc(dt, function(x) {
+          invoke(tf::tf_smooth, x = x, method = pars$method, .args = pars$args)
+        })
+      } else {
+        map_dtc(dt, function(x) {
+          suppressMessages(invoke(tf::tf_smooth, x = x, method = pars$method, .args = pars$args))
+        })
+      }
+
+    }
+  )
+)
+#' @include zzz.R
+register_po("fda.smooth", PipeOpFDASmooth)

R/zzz.R

@@ -9,7 +9,7 @@
 #' @importFrom tf tf_approx_linear tf_approx_spline tf_approx_fill_extend tf_approx_locf tf_approx_nocb
 #'
 #' @section Data types:
-#' To extend mlr3 to functional data, two data types from the {tf} package are added:
+#' To extend mlr3 to functional data, two data types from the tf package are added:
 #' * `tfd_irreg` - Irregular functional data, i.e. the functions are observed for
 #'   potentiall different inputs for each observation.
 #' * `tfd_reg` - Regular functional data, i.e. the functions are observed for the same input