Add predict parameters as argument

imbs-hl · Nov 5, 2024 · 2e56455 · 2e56455
1 parent 7040c0d
commit 2e56455
Showing 1 changed file with 29 additions and 11 deletions.
diff --git a/R/Lrner.R b/R/Lrner.R
@@ -19,10 +19,12 @@ Lrner <- R6Class("Lrner",
 
                    #' @param package (`character(1)`) \cr
                    #' Package that implements the learn function. If NULL, the
-                   #' learn function is called from the current environment.
                    #' @param lrn_fct (`character(1)`) \cr
-                   #' Learn function name.
-                   #' @param param (`ParamLrner(1)`) \cr
+                   #' learn function is called from the current environment.
+                   #' @param param_train_list \cr
+                   #' List of parameter for training.
+                   #' @param param_pred_list \cr
+                   #' List of parameter for testing.
                    #' Learn parameters.
                    #' @param train_layer (`TrainLayer(1)`) \cr
                    #' Layer on which the learner is stored.
@@ -31,13 +33,15 @@ Lrner <- R6Class("Lrner",
                    initialize = function (id,
                                           package = NULL,
                                           lrn_fct,
-                                          param,
+                                          param_train_list,
+                                          param_pred_list = list(),
                                           train_layer,
                                           na_rm = TRUE) {
                      private$id = id
                      private$package = package
                      private$lrn_fct = lrn_fct
-                     private$param = param
+                     private$param_train = param_train_list
+                     private$param_pred = param_pred_list
                      if (!any(c("TrainLayer", "TrainMetaLayer") %in% class(train_layer))) {
                        stop("A Lrner can only belong to a TrainLayer or a TrainMetaLayer object.")
                      }
@@ -67,7 +71,6 @@ Lrner <- R6Class("Lrner",
                      cat(sprintf("TrainLayer       : %s\n", private$train_layer$getId()))
                      cat(sprintf("Package          : %s\n", private$package))
                      cat(sprintf("Learn function   : %s\n", private$lrn_fct))
-                     cat(sprintf("Param id         : %s\n", private$param$id))
                    },
                    #' @description
                    #' Printer
@@ -78,7 +81,12 @@ Lrner <- R6Class("Lrner",
                      cat(sprintf("      TrainLayer       : %s\n", private$train_layer$getId()))
                      cat(sprintf("      Package          : %s\n", private$package))
                      cat(sprintf("      Learn function   : %s\n", private$lrn_fct))
-                     cat(sprintf("      Param id         : %s\n", private$param$id))
+                     cat("Predicting parameter\n")
+                     print(expand.grid(private$param_train))
+                     if (!length(private$param_pred)) {
+                       cat("Predicting parameter\n")
+                       print(expand.grid(private$param_pred))
+                     }
                    },
                    #' @description
                    #' Tains the current learner (from class [Lrner]) on the current training data (from class [TrainData]).
@@ -109,8 +117,7 @@ Lrner <- R6Class("Lrner",
                      } else {
                        lrn = sprintf('%s::%s', private$package, private$lrn_fct)
                      }
-                     lrn_param = private$param$getParamLrner()[1L, ]
-                     lrn_param = as.list(lrn_param)
+                     lrn_param = private$param_train
                      # Prepare training dataset: extract individual subset
                      if (!is.null(ind_subset)) {
                        train_data = train_data$getIndSubset(
@@ -208,6 +215,15 @@ Lrner <- R6Class("Lrner",
                    #'
                    getVarSubset = function () {
                      return(private$ind_subset)
+                   },
+                   #' @description
+                   #' Getter predicting parameter list.
+                   #'
+                   #' @return
+                   #' The list of predicting parameters.
+                   #'
+                   getParamPred = function () {
+                     return(private$param_pred)
                    }
                  ),
                  private = list(
@@ -217,8 +233,10 @@ Lrner <- R6Class("Lrner",
                    package = NULL,
                    # Learn function name (like \code{ranger}).
                    lrn_fct = NULL,
-                   # Parameters (from class [Param]) of the learn function.
-                   param = NULL,
+                   # Parameters of the learn function.
+                   param_train = list(0L),
+                   # Parameters of the predict function.
+                   param_pred = list(0L),
                    na_rm = NULL,
                    # Training layer (from class [TainLayer] or [TrainMetaLayer]) of the current learner.
                    train_layer = NULL,