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Remove superfluous crate argument specification .parent = topenv() #859

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Remove superfluous crate argument specification .parent = topenv() #859

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2 changes: 1 addition & 1 deletion R/LearnerAvg.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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")
)
)
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4 changes: 2 additions & 2 deletions R/PipeOpColRoles.R
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Expand Up @@ -95,7 +95,7 @@ PipeOpColRoles = R6Class("PipeOpColRoles",
# A value of character() or NULL is accepted.
all_col_roles = unique(unlist(mlr3::mlr_reflections$task_col_roles))
check_subset(unlist(x), all_col_roles[all_col_roles != "target"])
}, .parent = topenv())
})
),
# named list, each with a vector of columns, names are column roles
new_role_direct = p_uty(
Expand All @@ -115,7 +115,7 @@ PipeOpColRoles = R6Class("PipeOpColRoles",
# A value of character() or NULL is accepted.
all_col_roles = unique(unlist(mlr3::mlr_reflections$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)
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2 changes: 1 addition & 1 deletion R/PipeOpMutate.R
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Expand Up @@ -132,6 +132,6 @@ check_mutation_formulae = crate(function(x) {
}
TRUE
}), TRUE)
}, .parent = topenv())
})

mlr_pipeops$add("mutate", PipeOpMutate)
2 changes: 1 addition & 1 deletion R/PipeOpProxy.R
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Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ PipeOpProxy = R6Class("PipeOpProxy",
}
},
error = function(error_condition) "`content` must be an object that can be converted to a Graph")
}, innum, outnum, .parent = topenv()),
}, innum, outnum),
tags = c("train", "predidct", "required")
)
)
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8 changes: 4 additions & 4 deletions R/PipeOpTrafo.R
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Expand Up @@ -349,8 +349,8 @@ PipeOpTargetMutate = R6Class("PipeOpTargetMutate",
initialize = function(id = "targetmutate", param_vals = list(), new_task_type = NULL) {
private$.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,
Expand Down Expand Up @@ -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)
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2 changes: 1 addition & 1 deletion R/PipeOpTuneThreshold.R
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Expand Up @@ -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"
)
)
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16 changes: 8 additions & 8 deletions R/PipeOpVtreat.R
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Expand Up @@ -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")),
Expand All @@ -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
Expand Down
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