remove superfluous crate argument specification (now default in misc)

mlr-org · Dec 18, 2024 · 8866b6e · 8866b6e
1 parent b1042d7
commit 8866b6e
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Showing 7 changed files with 18 additions and 18 deletions.
diff --git a/R/LearnerAvg.R b/R/LearnerAvg.R
@@ -62,7 +62,7 @@ LearnerClassifAvg = R6Class("LearnerClassifAvg", inherit = LearnerClassif,
         measure = p_uty(custom_check = check_class_or_character("MeasureClassif", mlr_measures), tags = c("train", "required")),
         optimizer = p_uty(custom_check = check_optimizer, tags = c("train", "required")),
         log_level = p_uty(
-          custom_check = crate(function(x) check_string(x) %check||% check_integerish(x), .parent = topenv()),
+          custom_check = crate(function(x) check_string(x) %check||% check_integerish(x)),
           tags = c("train", "required")
         )
       )

diff --git a/R/PipeOpColRoles.R b/R/PipeOpColRoles.R
@@ -95,7 +95,7 @@ PipeOpColRoles = R6Class("PipeOpColRoles",
             # A value of character() or NULL is accepted.
             all_col_roles = unique(unlist(mlr3::mlr_reflections$task_col_roles))
             check_subset(unlist(x), all_col_roles[all_col_roles != "target"])
-          }, .parent = topenv())
+          })
         ),
         # named list, each with a vector of columns, names are column roles
         new_role_direct = p_uty(
@@ -115,7 +115,7 @@ PipeOpColRoles = R6Class("PipeOpColRoles",
             # A value of character() or NULL is accepted.
             all_col_roles = unique(unlist(mlr3::mlr_reflections$task_col_roles))
             check_subset(names(x), all_col_roles[all_col_roles != "target"])
-          }, .parent = topenv())
+          })
         )
       )
       super$initialize(id, param_set = ps, param_vals = param_vals, can_subset_cols = FALSE)

diff --git a/R/PipeOpMutate.R b/R/PipeOpMutate.R
@@ -132,6 +132,6 @@ check_mutation_formulae = crate(function(x) {
       }
       TRUE
     }), TRUE)
-}, .parent = topenv())
+})
 
 mlr_pipeops$add("mutate", PipeOpMutate)
diff --git a/R/PipeOpProxy.R b/R/PipeOpProxy.R
@@ -103,7 +103,7 @@ PipeOpProxy = R6Class("PipeOpProxy",
               }
             },
             error = function(error_condition) "`content` must be an object that can be converted to a Graph")
-          }, innum, outnum, .parent = topenv()),
+          }, innum, outnum),
           tags = c("train", "predidct", "required")
         )
       )

diff --git a/R/PipeOpTrafo.R b/R/PipeOpTrafo.R
@@ -349,8 +349,8 @@ PipeOpTargetMutate = R6Class("PipeOpTargetMutate",
     initialize = function(id = "targetmutate", param_vals = list(), new_task_type = NULL) {
       private$.new_task_type = assert_choice(new_task_type, mlr_reflections$task_types$type, null.ok = TRUE)
       ps = ps(
-        trafo = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_function(x, nargs = 1L), .parent = topenv())),
-        inverter = p_uty(tags = "predict", custom_check = crate(function(x) check_function(x, nargs = 1L), .parent = topenv()))
+        trafo = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_function(x, nargs = 1L))),
+        inverter = p_uty(tags = "predict", custom_check = crate(function(x) check_function(x, nargs = 1L)))
       )
       # We could add a condition here for new_task_type on trafo and inverter when mlr-org/paradox#278 has an answer.
       # HOWEVER conditions are broken in paradox, it is a terrible idea to use them in PipeOps,
@@ -567,8 +567,8 @@ PipeOpUpdateTarget = R6Class("PipeOpUpdateTarget",
     initialize = function(id = "update_target", param_vals = list()) {
       ps = ps(
         trafo = p_uty(tags = c("train", "predict"), custom_check = function(x) check_function(x, nargs = 1L)),
-        new_target_name = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_character(x, any.missing = FALSE, len = 1L), .parent = topenv())),
-        new_task_type = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_choice(x, choices = mlr_reflections$task_types$type), .parent = topenv())),
+        new_target_name = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_character(x, any.missing = FALSE, len = 1L))),
+        new_task_type = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_choice(x, choices = mlr_reflections$task_types$type))),
         drop_original_target = p_lgl(tags = c("train", "predict"))
       )
       ps$values = list(trafo = identity, drop_original_target = TRUE)

diff --git a/R/PipeOpTuneThreshold.R b/R/PipeOpTuneThreshold.R
@@ -86,7 +86,7 @@ PipeOpTuneThreshold = R6Class("PipeOpTuneThreshold",
         measure = p_uty(custom_check = check_class_or_character("Measure", mlr_measures), tags = "train"),
         optimizer = p_uty(custom_check = check_optimizer, tags = "train"),
         log_level = p_uty(
-          custom_check = crate(function(x) check_string(x) %check||% check_integerish(x), .parent = topenv()),
+          custom_check = crate(function(x) check_string(x) %check||% check_integerish(x)),
           tags = "train"
         )
       )

diff --git a/R/PipeOpVtreat.R b/R/PipeOpVtreat.R
@@ -140,17 +140,17 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
         doCollar = p_lgl(default = FALSE, tags = c("train", "regression", "classification", "multinomial")),
         codeRestriction = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_character(x, any.missing = FALSE, null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_character(x, any.missing = FALSE, null.ok = TRUE)),
           tags = c("train", "regression", "classification", "multinomial")
         ),
         customCoders = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE)),
           tags = c("train", "regression", "classification", "multinomial")
         ),
         splitFunction = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_function(x, args = c("nSplits", "nRows", "dframe", "y"), null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_function(x, args = c("nSplits", "nRows", "dframe", "y"), null.ok = TRUE)),
           tags = c("train", "regression", "classification", "multinomial")
         ),
         ncross = p_int(lower = 2L, upper = Inf, default = 3L, tags = c("train", "regression", "classification", "multinomial")),
@@ -160,31 +160,31 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
         use_paralell = p_lgl(default = TRUE, tags = c("train", "regression", "classification", "multinomial")),
         missingness_imputation = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_function(x, args = c("values", "weights"), null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_function(x, args = c("values", "weights"), null.ok = TRUE)),
           tags = c("train", "regression", "classification", "multinomial")
         ),
         pruneSig = p_dbl(lower = 0, upper = 1, special_vals = list(NULL), default = NULL, tags = c("train", "regression", "classification")),
         scale = p_lgl(default = FALSE, tags = c("train", "regression", "classification", "multinomial")),
         varRestriction = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE)),
           tags = c("train", "regression", "classification")
         ),
         trackedValues = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE)),
           tags = c("train", "regression", "classification")
         ),
         # NOTE: check_for_duplicate_frames not needed
         y_dependent_treatments = p_uty(
           default = "catB",
-          custom_check = crate(function(x) checkmate::check_character(x, any.missing = FALSE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_character(x, any.missing = FALSE)),
           tags = c("train", "multinomial")
         ),
         # NOTE: imputation_map is also in multinomial_parameters(); this is redundant so only include it here
         imputation_map = p_uty(
           default = NULL,
-          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE)),
           tags = c("train", "predict")
         )
         # NOTE: parallelCluster missing intentionally and will be set to NULL