Impute factorial features by adding a new level ".MISSING".
Impute numerical features by constant values shifted below the minimum or above the maximum by using \(min(x) - offset - multiplier * diff(range(x))\) or \(max(x) + offset + multiplier * diff(range(x))\).
This type of imputation is especially sensible in the context of tree-based methods, see also Ding & Simonoff (2010).
If a factor is missing during prediction, but not during training, this adds an unseen level
".MISSING", which would be a problem for most models. This is why it is recommended to use
po("fixfactors") and
po("imputesample", affect_columns = selector_type(types = c("factor", "ordered")))
(or some other imputation method) after this imputation method, if missing values are expected during prediction
in factor columns that had no missing values during training.
Format
R6Class object inheriting from PipeOpImpute/PipeOp.
Construction
id::character(1)
Identifier of resulting object, default"imputeoor".param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
Input and Output Channels
Input and output channels are inherited from PipeOpImpute.
The output is the input Task with all affected features having missing values imputed as described above.
State
The $state is a named list with the $state elements inherited from PipeOpImpute.
The $state$model contains either ".MISSING" used for character and factor (also
ordered) features or numeric(1) indicating the constant value used for imputation of
integer and numeric features.
Parameters
The parameters are the parameters inherited from PipeOpImpute, as well as:
min::logical(1)
Shouldintegerandnumericfeatures be shifted below the minimum? Initialized to TRUE. If FALSE they are shifted above the maximum. See also the description above.offset::numeric(1)
Numerical non-negative offset as used in the description above forintegerandnumericfeatures. Initialized to 1.multiplier::numeric(1)
Numerical non-negative multiplier as used in the description above forintegerandnumericfeatures. Initialized to 1.
Internals
Adds an explicit new level() to factor and ordered features, but not to character features.
For integer and numeric features uses the min, max, diff and range functions.
integer and numeric features that are entirely NA are imputed as 0.
Methods
Only methods inherited from PipeOpImpute/PipeOp.
References
Ding Y, Simonoff JS (2010). “An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data.” Journal of Machine Learning Research, 11(6), 131-170. https://jmlr.org/papers/v11/ding10a.html.
See also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEnsemble,
PipeOpImpute,
PipeOpTargetTrafo,
PipeOpTaskPreproc,
PipeOpTaskPreprocSimple,
mlr_pipeops,
mlr_pipeops_adas,
mlr_pipeops_blsmote,
mlr_pipeops_boxcox,
mlr_pipeops_branch,
mlr_pipeops_chunk,
mlr_pipeops_classbalancing,
mlr_pipeops_classifavg,
mlr_pipeops_classweights,
mlr_pipeops_colapply,
mlr_pipeops_collapsefactors,
mlr_pipeops_colroles,
mlr_pipeops_copy,
mlr_pipeops_datefeatures,
mlr_pipeops_encode,
mlr_pipeops_encodeimpact,
mlr_pipeops_encodelmer,
mlr_pipeops_featureunion,
mlr_pipeops_filter,
mlr_pipeops_fixfactors,
mlr_pipeops_histbin,
mlr_pipeops_ica,
mlr_pipeops_imputeconstant,
mlr_pipeops_imputehist,
mlr_pipeops_imputelearner,
mlr_pipeops_imputemean,
mlr_pipeops_imputemedian,
mlr_pipeops_imputemode,
mlr_pipeops_imputesample,
mlr_pipeops_kernelpca,
mlr_pipeops_learner,
mlr_pipeops_learner_pi_cvplus,
mlr_pipeops_learner_quantiles,
mlr_pipeops_missind,
mlr_pipeops_modelmatrix,
mlr_pipeops_multiplicityexply,
mlr_pipeops_multiplicityimply,
mlr_pipeops_mutate,
mlr_pipeops_nearmiss,
mlr_pipeops_nmf,
mlr_pipeops_nop,
mlr_pipeops_ovrsplit,
mlr_pipeops_ovrunite,
mlr_pipeops_pca,
mlr_pipeops_proxy,
mlr_pipeops_quantilebin,
mlr_pipeops_randomprojection,
mlr_pipeops_randomresponse,
mlr_pipeops_regravg,
mlr_pipeops_removeconstants,
mlr_pipeops_renamecolumns,
mlr_pipeops_replicate,
mlr_pipeops_rowapply,
mlr_pipeops_scale,
mlr_pipeops_scalemaxabs,
mlr_pipeops_scalerange,
mlr_pipeops_select,
mlr_pipeops_smote,
mlr_pipeops_smotenc,
mlr_pipeops_spatialsign,
mlr_pipeops_subsample,
mlr_pipeops_targetinvert,
mlr_pipeops_targetmutate,
mlr_pipeops_targettrafoscalerange,
mlr_pipeops_textvectorizer,
mlr_pipeops_threshold,
mlr_pipeops_tomek,
mlr_pipeops_tunethreshold,
mlr_pipeops_unbranch,
mlr_pipeops_updatetarget,
mlr_pipeops_vtreat,
mlr_pipeops_yeojohnson
Other Imputation PipeOps:
PipeOpImpute,
mlr_pipeops_imputeconstant,
mlr_pipeops_imputehist,
mlr_pipeops_imputelearner,
mlr_pipeops_imputemean,
mlr_pipeops_imputemedian,
mlr_pipeops_imputemode,
mlr_pipeops_imputesample
Examples
library("mlr3")
set.seed(2409)
data = tsk("pima")$data()
data$y = factor(c(NA, sample(letters, size = 766, replace = TRUE), NA))
data$z = ordered(c(NA, sample(1:10, size = 767, replace = TRUE)))
task = TaskClassif$new("task", backend = data, target = "diabetes")
task$missings()
#> diabetes age glucose insulin mass pedigree pregnant pressure
#> 0 0 5 374 11 0 0 35
#> triceps y z
#> 227 2 1
po = po("imputeoor")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
#> diabetes age pedigree pregnant glucose insulin mass pressure
#> 0 0 0 0 0 0 0 0
#> triceps y z
#> 0 0 0
new_task$data()
#> diabetes age pedigree pregnant glucose insulin mass pressure triceps
#> <fctr> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: pos 50 0.627 6 148 -819 33.6 72 35
#> 2: neg 31 0.351 1 85 -819 26.6 66 29
#> 3: pos 32 0.672 8 183 -819 23.3 64 -86
#> 4: neg 21 0.167 1 89 94 28.1 66 23
#> 5: pos 33 2.288 0 137 168 43.1 40 35
#> ---
#> 764: neg 63 0.171 10 101 180 32.9 76 48
#> 765: neg 27 0.340 2 122 -819 36.8 70 27
#> 766: neg 30 0.245 5 121 112 26.2 72 23
#> 767: pos 47 0.349 1 126 -819 30.1 60 -86
#> 768: neg 23 0.315 1 93 -819 30.4 70 31
#> y z
#> <fctr> <ord>
#> 1: .MISSING .MISSING
#> 2: l 9
#> 3: q 6
#> 4: f 3
#> 5: l 3
#> ---
#> 764: o 7
#> 765: n 5
#> 766: e 6
#> 767: c 8
#> 768: .MISSING 9
# recommended use when missing values are expected during prediction on
# factor columns that had no missing values during training
gr = po("imputeoor") %>>%
po("fixfactors") %>>%
po("imputesample", affect_columns = selector_type(types = c("factor", "ordered")))
t1 = as_task_classif(data.frame(l = as.ordered(letters[1:3]), t = letters[1:3]), target = "t")
t2 = as_task_classif(data.frame(l = as.ordered(c("a", NA, NA)), t = letters[1:3]), target = "t")
gr$train(t1)[[1]]$data()
#> t l
#> <fctr> <ord>
#> 1: a a
#> 2: b b
#> 3: c c
# missing values during prediction are sampled randomly
gr$predict(t2)[[1]]$data()
#> t l
#> <fctr> <ord>
#> 1: a a
#> 2: b c
#> 3: c c