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Move pkg function calls within ParamSets to helper file #2730

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Merged
merged 11 commits into from
Feb 26, 2020
Merged

Move pkg function calls within ParamSets to helper file #2730

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982995b
outsource pkg functions of param set to helper file
pat-s Feb 8, 2020
26820c0
use jakobs approach
pat-s Feb 10, 2020
0a3adac
Merge branch 'master' into fix-param-sets
pat-s Feb 10, 2020
a9d4440
discreteParam -> untypedParam
pat-s Feb 17, 2020
db7a182
Merge branch 'fix-param-sets' of github.com:mlr-org/mlr into fix-para…
pat-s Feb 17, 2020
b459b98
ups
pat-s Feb 17, 2020
5a46307
values -> special.vals
pat-s Feb 18, 2020
daba184
special.vals and DiscreLearnerParam
pat-s Feb 20, 2020
85c939b
discrete -> untyped
pat-s Feb 21, 2020
b2bcb99
Merge branch 'master' into fix-param-sets
pat-s Feb 24, 2020
4db31b8
alternative solution
jakob-r Feb 26, 2020
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10 changes: 7 additions & 3 deletions R/RLearner_classif_mda.R
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Expand Up @@ -11,13 +11,17 @@ makeRLearner.classif.mda = function() {
makeIntegerLearnerParam(id = "dimension", lower = 1L),
makeNumericLearnerParam(id = "eps", default = .Machine$double.eps, lower = 0),
makeIntegerLearnerParam(id = "iter", default = 5L, lower = 1L),
makeDiscreteLearnerParam(id = "method", default = mda::polyreg,
values = list(polyreg = mda::polyreg, mars = mda::mars, bruto = mda::bruto, gen.ridge = mda::gen.ridge)),
# see helpers_mda.R for objects
makeUntypedLearnerParam(id = "method", default = function(...) mda::polyreg(...),
values = list(polyreg = function(...) mda::polyreg(...),
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for an untyped Param you cannot set values.

mars = function(...) mda::mars(...),
bruto = function(...) mda::bruto(...),
gen.ridge = function(...) mda::gen.ridge(...))),
makeLogicalLearnerParam(id = "keep.fitted", default = TRUE),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "start.method", default = "kmeans", values = c("kmeans", "lvq")),
makeIntegerLearnerParam(id = "tries", default = 5L, lower = 1L),
makeDiscreteLearnerParam(id = "criterion", default = "misclassification", values = c("misclassification", "deviance"))
makeDiscreteLearnerParam(id = "criterion", default = "misclassification", special.vals = c("misclassification", "deviance"))
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Suggested change
makeDiscreteLearnerParam(id = "criterion", default = "misclassification", special.vals = c("misclassification", "deviance"))
makeDiscreteLearnerParam(id = "criterion", default = "misclassification", values = c("misclassification", "deviance"))

special.vals are meant for values that do not match the param type. It also does not make much sense to define a DiscreteParam without discrete values.

),
par.vals = list(keep.fitted = FALSE, start.method = "lvq"),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
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5 changes: 3 additions & 2 deletions R/RLearner_regr_mob.R
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Expand Up @@ -10,8 +10,9 @@ makeRLearner.regr.mob = function() {
makeNumericLearnerParam(id = "trim", default = 0.1, lower = 0, upper = 1),
makeLogicalLearnerParam(id = "breakties", default = FALSE),
makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "model", default = modeltools::glinearModel,
values = list(glinearModel = modeltools::glinearModel, linearModel = modeltools::linearModel)),
# see helpers_modeltools.R for `glinearModel`
makeUntypedLearnerParam(id = "model", default = function(...) modeltools::glinearModel(...),
special.vals = list(glinearModel = function(...) modeltools::glinearModel(...), linearModel = function(...) modeltools::glinearModel(...))),
makeUntypedLearnerParam(id = "part.feats"),
makeUntypedLearnerParam(id = "term.feats")
),
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