Add crate to custom_checks

R/LearnerAvg.R

@@ -61,8 +61,10 @@ LearnerClassifAvg = R6Class("LearnerClassifAvg", inherit = LearnerClassif,
       ps = ps(
         measure = p_uty(custom_check = check_class_or_character("MeasureClassif", mlr_measures), tags = "train"),
         optimizer = p_uty(custom_check = check_optimizer, tags = "train"),
-        log_level = p_uty(tags = "train",
-          function(x) check_string(x) %check||% check_integerish(x))
+        log_level = p_uty(
+          custom_check = crate(function(x) check_string(x) %check||% check_integerish(x), .parent = topenv()),
+          tags = "train"
+        )
       )
       ps$values = list(measure = "classif.ce", optimizer = "nloptr", log_level = "warn")
       super$initialize(

R/PipeOpColRoles.R

@@ -58,17 +58,20 @@ PipeOpColRoles = R6Class("PipeOpColRoles",
     initialize = function(id = "colroles", param_vals = list()) {
       ps = ps(
         # named list, each entry with a vector of roles
-        new_role = p_uty(tags = c("train", "predict"), custom_check = function(x) {
-          first_check = check_list(x, types = "character", any.missing = FALSE, min.len = 1L, names = "named")
-          # return the error directly if this failed
-          if (is.character(first_check)) {
-            return(first_check)
-          }
-          # changing anything target related is not supported
-          # a value of "character()" will lead to the column being dropped
-          all_col_roles = unique(unlist(mlr3::mlr_reflections$task_col_roles))
-          check_subset(unlist(x), all_col_roles[all_col_roles != "target"])
-        })
+        new_role = p_uty(
+          tags = c("train", "predict"),
+          custom_check = crate(function(x) {
+            first_check = check_list(x, types = "character", any.missing = FALSE, min.len = 1L, names = "named")
+            # return the error directly if this failed
+            if (is.character(first_check)) {
+              return(first_check)
+            }
+            # changing anything target related is not supported
+            # a value of "character()" will lead to the column being dropped
+            all_col_roles = unique(unlist(mlr3::mlr_reflections$task_col_roles))
+            check_subset(unlist(x), all_col_roles[all_col_roles != "target"])
+          }, .parent = topenv())
+        )
       )
       super$initialize(id, param_set = ps, param_vals = param_vals, can_subset_cols = FALSE)
     }

R/PipeOpMutate.R

@@ -123,7 +123,7 @@ PipeOpMutate = R6Class("PipeOpMutate",
 # checks that `mutation` is
 # * a named list of `formula`
 # * that each element has only a lhs
-check_mutation_formulae = function(x) {
+check_mutation_formulae = crate(function(x) {
   check_list(x, types = "formula", names = "unique") %check&&%
     Reduce(`%check&&%`, lapply(x, function(xel) {
       if (length(xel) != 2) {
@@ -132,6 +132,6 @@ check_mutation_formulae = function(x) {
       }
       TRUE
     }), TRUE)
-}
+}, .parent = topenv())
 
 mlr_pipeops$add("mutate", PipeOpMutate)

R/PipeOpProxy.R

@@ -88,23 +88,26 @@ PipeOpProxy = R6Class("PipeOpProxy",
       # input can be a vararg input channel
       inname = if (innum) rep_suffix("input", innum) else "..."
       ps = ps(
-        content = p_uty(tags = c("train", "predidct", "required"), custom_check = function(x) {
-          # content must be an object that can be coerced to a Graph and the output number must match
-          tryCatch({
-            graph = as_graph(x)
-            # graph$output access may be slow, so we cache it here
-            graph_outnum = nrow(graph$output)
-            graph_input = nrow(graph$input)
-            if (graph_outnum != 1 && graph_outnum != outnum) {
-              "Graph's output number must either be 1 or match `outnum`"
-            } else if (innum > 1 && graph_input != innum && (graph_input > innum || "..." %nin% graph$input$name)) {
-              "Graph's input number when `innum` > 1 must either match `innum` or the Graph must contain a '...' (vararg) channel."
-            } else {
-              TRUE
-            }
-          },
-          error = function(error_condition) "`content` must be an object that can be converted to a Graph")
-        })
+        content = p_uty(
+          custom_check = crate(function(x) {
+            # content must be an object that can be coerced to a Graph and the output number must match
+            tryCatch({
+              graph = as_graph(x)
+              # graph$output access may be slow, so we cache it here
+              graph_outnum = nrow(graph$output)
+              graph_input = nrow(graph$input)
+              if (graph_outnum != 1 && graph_outnum != outnum) {
+                "Graph's output number must either be 1 or match `outnum`"
+              } else if (innum > 1 && graph_input != innum && (graph_input > innum || "..." %nin% graph$input$name)) {
+                "Graph's input number when `innum` > 1 must either match `innum` or the Graph must contain a '...' (vararg) channel."
+              } else {
+                TRUE
+              }
+            },
+            error = function(error_condition) "`content` must be an object that can be converted to a Graph")
+          }, innum, outnum, .parent = topenv()),
+          tags = c("train", "predidct", "required")
+        )
       )
       ps$values = list(content = PipeOpFeatureUnion$new(innum = innum))
       super$initialize(id, param_set = ps, param_vals = param_vals,

R/PipeOpRenameColumns.R

@@ -61,10 +61,11 @@ PipeOpRenameColumns = R6Class("PipeOpRenameColumns",
   public = list(
     initialize = function(id = "renamecolumns", param_vals = list()) {
       ps = ps(
-        renaming = p_uty(tags = c("train", "predict", "required"), custom_check = function(x) {
-          check_character(x, any.missing = FALSE, names = "strict") %check&&%
-            check_names(x, type = "strict")
-        }),
+        renaming = p_uty(
+          custom_check = crate(function(x) check_character(x, any.missing = FALSE, names = "strict") %check&&% check_names(x, type = "strict"),
+                               .parent = topenv()),
+          tags = c("train", "predict", "required")
+        ),
         ignore_missing = p_lgl(tags = c("train", "predict", "required"))
       )
       ps$values = list(renaming = character(0), ignore_missing = FALSE)

R/PipeOpTrafo.R

@@ -351,8 +351,8 @@ 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$type, null.ok = TRUE)
       ps = ps(
-        trafo = p_uty(tags = c("train", "predict"), custom_check = function(x) check_function(x, nargs = 1L)),
-        inverter = p_uty(tags = "predict", custom_check = function(x) check_function(x, nargs = 1L))
+        trafo = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_function(x, nargs = 1L), .parent = topenv())),
+        inverter = p_uty(tags = "predict", custom_check = crate(function(x) check_function(x, nargs = 1L), .parent = topenv()))
       )
       # We could add a condition here for new_task_type on trafo and inverter when mlr-org/paradox#278 has an answer.
       # HOWEVER conditions are broken in paradox, it is a terrible idea to use them in PipeOps,
@@ -573,8 +573,8 @@ PipeOpUpdateTarget = R6Class("PipeOpUpdateTarget",
     initialize = function(id = "update_target", param_vals = list()) {
       ps = ps(
         trafo = p_uty(tags = c("train", "predict"), custom_check = function(x) check_function(x, nargs = 1L)),
-        new_target_name = p_uty(tags = c("train", "predict"), custom_check = function(x) check_character(x, any.missing = FALSE, len = 1L)),
-        new_task_type = p_uty(tags = c("train", "predict"), custom_check = function(x) check_choice(x, choices = mlr_reflections$task_types$type)),
+        new_target_name = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_character(x, any.missing = FALSE, len = 1L), .parent = topenv())),
+        new_task_type = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_choice(x, choices = mlr_reflections$task_types$type), .parent = topenv())),
         drop_original_target = p_lgl(tags = c("train", "predict"))
       )
       ps$values = list(trafo = identity, drop_original_target = TRUE)

R/PipeOpTuneThreshold.R

@@ -84,8 +84,10 @@ PipeOpTuneThreshold = R6Class("PipeOpTuneThreshold",
       ps = ps(
         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(tags = "train",
-          function(x) check_string(x) %check||% check_integerish(x))
+        log_level = p_uty(
+          custom_check = crate(function(x) check_string(x) %check||% check_integerish(x), .parent = topenv()),
+          tags = "train"
+        )
       )
       ps$values = list(measure = "classif.ce", optimizer = "gensa", log_level = "warn")
       super$initialize(id, param_set = ps, param_vals = param_vals, packages = "bbotk",

R/PipeOpVtreat.R

@@ -140,26 +140,55 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
         rareSig = p_dbl(lower = 0, upper = 1, special_vals = list(NULL), tags = c("train", "regression", "classification", "multinomial")),  # default NULL for regression, classification, 1 for multinomial
         collarProb = p_dbl(lower = 0, upper = 1, default = 0, tags = c("train", "regression", "classification", "multinomial"), depends = quote(doCollar == TRUE)),
         doCollar = p_lgl(default = FALSE, tags = c("train", "regression", "classification", "multinomial")),
-        codeRestriction = p_uty(default = NULL, custom_check = 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 = function(x) checkmate::check_list(x, null.ok = TRUE), tags = c("train", "regression", "classification", "multinomial")),
-        splitFunction = p_uty(default = NULL, custom_check = function(x) checkmate::check_function(x, args = c("nSplits", "nRows", "dframe", "y"), null.ok = TRUE),
-          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()),
+          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()),
+          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()),
+          tags = c("train", "regression", "classification", "multinomial")
+        ),
         ncross = p_int(lower = 2L, upper = Inf, default = 3L, tags = c("train", "regression", "classification", "multinomial")),
         forceSplit = p_lgl(default = FALSE, tags = c("train", "regression", "classification", "multinomial")),
         catScaling = p_lgl(tags = c("train", "regression", "classification", "multinomial")),  # default TRUE for regression, classification, FALSE for multinomial
         verbose = p_lgl(default = FALSE, tags = c("train", "regression", "classification", "multinomial")),
         use_paralell = p_lgl(default = TRUE, tags = c("train", "regression", "classification", "multinomial")),
-        missingness_imputation = p_uty(default = NULL, custom_check = function(x) checkmate::check_function(x, args = c("values", "weights"), null.ok = 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()),
+          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 = function(x) checkmate::check_list(x, null.ok = TRUE), tags = c("train", "regression", "classification")),
-        trackedValues = p_uty(default = NULL, custom_check = function(x) checkmate::check_list(x, null.ok = TRUE), tags = c("train", "regression", "classification")),
+        varRestriction = p_uty(
+          default = NULL,
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          tags = c("train", "regression", "classification")
+        ),
+        trackedValues = p_uty(
+          default = NULL,
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          tags = c("train", "regression", "classification")
+        ),
         # NOTE: check_for_duplicate_frames not needed
-        y_dependent_treatments = p_uty(default = "catB", custom_check = function(x) checkmate::check_character(x, any.missing = FALSE), tags = c("train", "multinomial")),
+        y_dependent_treatments = p_uty(
+          default = "catB",
+          custom_check = crate(function(x) checkmate::check_character(x, any.missing = FALSE), .parent = topenv()),
+          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 = function(x) checkmate::check_list(x, null.ok = TRUE), tags = c("train", "predict"))
+        imputation_map = p_uty(
+          default = NULL,
+          custom_check = crate(function(x) checkmate::check_list(x, null.ok = TRUE), .parent = topenv()),
+          tags = c("train", "predict")
+        )
         # NOTE: parallelCluster missing intentionally and will be set to NULL
       )
       ps$values = list(recommended = TRUE, cols_to_copy = selector_none())