feat: add configure method to set parameters and fields of the learner

R/Learner.R

@@ -504,6 +504,50 @@ Learner = R6Class("Learner",
       private$.encapsulation = c(train = method, predict = method)
       private$.fallback = fallback
 
+      return(invisible(self))
+    },
+
+    #' @description
+    #' Sets parameter values and fields of the learner.
+    #' All arguments whose names match the name of a parameter of the [paradox::ParamSet] are set as parameters.
+    #' All remaining arguments are assumed to be regular fields.
+    #'
+    #' @param ... (named `any`)\cr
+    #'   Named arguments to set parameter values and fields.
+    #' @param .values (named `any`)\cr
+    #'   Named list of parameter values and fields.
+    configure = function(..., .values = list()) {
+      dots = list(...)
+      assert_list(dots, names = "unique")
+      assert_list(.values, names = "unique")
+      assert_disjunct(names(dots), names(.values))
+      new_values = insert_named(dots, .values)
+
+      # set params in ParamSet
+      if (length(new_values)) {
+        param_ids = self$param_set$ids()
+        ii = names(new_values) %in% param_ids
+        if (any(ii)) {
+          self$param_set$values = insert_named(self$param_set$values, new_values[ii])
+          new_values = new_values[!ii]
+        }
+      } else {
+        param_ids = character()
+      }
+
+      # remaining args go into fields
+      if (length(new_values)) {
+        ndots = names(new_values)
+        for (i in seq_along(new_values)) {
+          nn = ndots[[i]]
+          if (!exists(nn, envir = self, inherits = FALSE)) {
+            stopf("Cannot set argument '%s' for '%s' (not a parameter, not a field).%s",
+              nn, class(self)[1L], did_you_mean(nn, c(param_ids, setdiff(names(self), ".__enclos_env__")))) # nolint
+          }
+          self[[nn]] = new_values[[i]]
+        }
+      }
+
       return(invisible(self))
     }
   ),

tests/testthat/test_Learner.R

@@ -629,3 +629,37 @@ test_that("predict time is cumulative", {
   t2 = learner$timings["predict"]
   expect_true(t1 > t2)
 })
+
+test_that("configure method works", {
+  learner = lrn("classif.rpart")
+
+  expect_learner(learner$configure())
+  expect_learner(learner$configure(.values = list()))
+
+  # set new hyperparameter value
+  learner$configure(cp = 0.1)
+  expect_equal(learner$param_set$values$cp, 0.1)
+
+  # overwrite existing hyperparameter value
+  learner$configure(xval = 10)
+  expect_equal(learner$param_set$values$xval, 10)
+
+  # set field
+  learner$configure(predict_sets = "train")
+  expect_equal(learner$predict_sets, "train")
+
+  # hyperparameter and field
+  learner$configure(minbucket = 2, parallel_predict = TRUE)
+  expect_equal(learner$param_set$values$minbucket, 2)
+  expect_true(learner$parallel_predict)
+
+  # unknown hyperparameter and field
+  expect_error(learner$configure(xvald = 1), "Cannot set argument")
+
+  # use .values
+  learner = lrn("classif.rpart")
+  learner$configure(.values = list(cp = 0.1, xval = 10, predict_sets = "train"))
+  expect_equal(learner$param_set$values$cp, 0.1)
+  expect_equal(learner$param_set$values$xval, 10)
+  expect_equal(learner$predict_sets, "train")
+})