Add crate to custom_check in PipeOpVtreat.R

mlr-org · Aug 8, 2024 · 65bef55 · 65bef55
1 parent 37b7781
commit 65bef55
Showing 1 changed file with 40 additions and 11 deletions.
diff --git a/R/PipeOpVtreat.R b/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())