reworked hyperparameters

Author: advieser

R/PipeOpSplines.R

@@ -9,7 +9,6 @@
 #'
 #' Depending on the type parameter, constructs polynomial B-splines [`splines::bs()`] or natural cubic splines [`splines::ns()`] for the respective column.
 #'
-#'
 #' @section Construction:
 #' ```
 #' po("splines", param_vals = list())
@@ -31,32 +30,31 @@
 #'
 #' @section Parameters:
 #' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
-#' * `type`  :: `character(1)` \cr
-#'   `polynomial` when polynomial splines are applied [`splines::bs`] or
-#'   `natural` when natural splines are applied [`splines::ns`].
-#'   Default is `natural`.
+#' * `type` :: `character(1)` \cr
+#'   Controls the type of splines that are to be created. Can be either `polynomial` ([`splines::bs`]) 
+#'   or `natural` ([`splines::ns`]). Initializied to `natural`.
 #' * `df`  :: `integer(1)` \cr
-#'   Number of degrees of freedom for calculation of splines basis matrix.
-#'   Default is `NULL`.
-#'   For further information look up [`splines::bs()`] or [`splines::ns()`].
+#'   Number of degrees of freedom for calculation of the spline basis matrix. Initialized to `NULL`.
+#'   Depending on `type`, see either [`splines::bs()`] or [`splines::ns()`].
 #' * `knots` :: named `list` \cr
-#'   The internal breakpoints that define the spline. Parameter has to be passed as a named list.
-#'   Default is `NULL`. For further information consult [`splines::bs()`] or [`splines::ns()`].
+#'   Internal breakpoints that define the spline, given as a named list of numeric vectors,
+#'   where each name corresponds to a feature and its value specifies the knots for that feature.
+#'   Initialized to `NULL`. Depending on `type`, see either [`splines::bs()`] or [`splines::ns()`].
 #' * `intercept` :: `logical(1)` \cr
 #'   If `TRUE`, an intercept is included in the basis. Default is `FALSE`.
-#'   For further information look up [`splines::bs()`] or [`splines::ns()`].
+#'   Depending on `type`, see either [`splines::bs()`] or [`splines::ns()`].
 #' * `degree` :: `integer(1)` \cr
-#'   This parameter depends on type = "polynomial". Degree of the polynomial used to compute B-splines.
-#'   Default is `3`. For further information look up [`splines::bs()`].
+#'   Degree of the polynomial used to compute polynomial splines. Only used if `type` is `"polynomial"`.
+#'   Default is `3`. See [`splines::bs()`].
 #' * `Boundary.knots` :: named `list` \cr
-#'   Boundary points at which to anchor the spline basis. Parameter has to be passed as a named list.
-#'   Default is `NULL`.
-#'   For further information look up [`splines::bs()`] or [`splines::ns()`].
+#'   Boundary points at which to anchor the spline basis, given as a named list of numeric vectors,
+#'   where each name corresponds to a feature and its value specifies the boundary points for that feature.
+#'   Initialized to `NULL`. Depending on `type`, see either [`splines::bs()`] or [`splines::ns()`].
 #'
 #' @section Internals:
-#' Creates spline basis via [`splines::bs`]/[`splines::ns`] function depending on `type`.
-#' After training, the `Boundary.knots` that are either defined in the Parameter Set
-#' or have been calculated during training will be passed to the `$state` of the PipeOp.
+#' Creates a spline basis using either [`splines::bs`] or [`splines::ns`] depending on the hyperparameter `type`.
+#' After training, the `Boundary.knots` that were either provided by the user or calculated during training are
+#' stored in the `PipeOp`'s `$state`.
 #'
 #' @section Fields:
 #' Only fields inherited from [`PipeOp`].
@@ -72,25 +70,28 @@
 #'
 #' pop$train(list(task))[[1]]$data()
 #'
-#' pobk = po("splines", param_vals = list(Boundary.knots = list("Petal.Length" = c(0, 4), "Petal.Width" = c(4, 7), "Sepal.Length" = c(1, 5), "Sepal.Width" = c(3, 6))))
+#' pobk = po("splines", Boundary.knots = list(
+#'   Petal.Length = c(0, 4), Petal.Width = c(4, 7), Sepal.Length = c(1, 5), Sepal.Width = c(3, 6))
+#' )
 #' pobk$train(list(task))[[1]]$data()
 #'
 #' @family PipeOps
 #' @template seealso_pipeopslist
 #' @include PipeOpTaskPreproc.R
 #' @export
-
 PipeOpSplines = R6Class("PipeOpSplines",
   inherit = PipeOpTaskPreproc,
   public = list(
     initialize = function(id = "splines", param_vals = list()) {
       ps = ps(
         type = p_fct(levels = c("polynomial", "natural"), init = "natural", tags = c("train", "splines", "required")),
-        df = p_int(lower = 1, upper = Inf, special_vals = list(NULL), init = NULL, tags = c("train", "splines")),
-        knots = p_uty(special_vals = list(NULL), init = NULL, tags = c("train", "splines")),
-        degree = p_int(lower = 1, upper = Inf, default = 3, depends = type == "polynomial", tags = c("train", "splines")),
-        intercept = p_lgl(init = FALSE, tags = c("train", "splines")),
-        Boundary.knots = p_uty(tags = c("train", "splines"))
+        df = p_int(lower = 1, upper = Inf, special_vals = list(NULL), default = NULL, tags = c("train", "splines")),
+        knots = p_uty(special_vals = list(NULL), init = NULL, custom_check = function(x) check_list(x, any.missing = FALSE, null.ok = TRUE, names = "named"),
+          tags = c("train", "splines")),
+        degree = p_int(lower = 1, upper = Inf, default = 3, depends = quote(type == "polynomial"), tags = c("train", "splines")),
+        intercept = p_lgl(default = FALSE, tags = c("train", "splines")),
+        Boundary.knots = p_uty(special_vals = list(NULL), init = NULL, custom_check = function(x) check_list(x, any.missing = FALSE, null.ok = TRUE, names = "named"), 
+          tags = c("train", "splines"), )
       )
       super$initialize(id = id, param_set = ps, param_vals = param_vals, packages = c("splines", "stats"))
     }
@@ -100,25 +101,27 @@ PipeOpSplines = R6Class("PipeOpSplines",
       result = list()
       bk = list()
       pv = self$param_set$get_values(tags = "splines")
-        for (i in colnames(dt)) {
-          args = pv
-          args$type = NULL
-          args$knots = pv$knots[[i]]
-          args$Boundary.knots = pv$Boundary.knots[[i]]
-          if (pv$type == "polynomial") {
-            result[[i]] = invoke(splines::bs, .args = args, x = dt[[i]], warn.outside = FALSE)
-          } else {
-            result[[i]] = invoke(splines::ns, .args = args, x = dt[[i]])
-          }
-          colnames(result[[i]]) = paste0("splines.", seq_len(ncol(result[[i]])))
-          bk[[i]] = attributes(result[[i]])$Boundary.knots
+
+      for (i in colnames(dt)) {
+        args = pv
+        args$type = NULL
+        args$knots = pv$knots[[i]]
+        args$Boundary.knots = pv$Boundary.knots[[i]]
+        if (pv$type == "polynomial") {
+          result[[i]] = invoke(splines::bs, .args = args, x = dt[[i]], warn.outside = FALSE)
+        } else {
+          result[[i]] = invoke(splines::ns, .args = args, x = dt[[i]])
         }
-        self$state$Boundary.knots = bk
+        colnames(result[[i]]) = paste0("splines.", seq_len(ncol(result[[i]])))
+        bk[[i]] = attributes(result[[i]])$Boundary.knots
+      }
+      self$state$Boundary.knots = bk
       result
     },
     .predict_dt = function(dt, levels) {
       result = list()
       pv = self$param_set$get_values(tags = "splines")
+
       for (i in colnames(dt)) {
         args = pv
         args$type = NULL