Feature filtering using a mlr3filters::Filter object, see the mlr3filters package.

If a Filter can only operate on a subset of columns based on column type, then only these features are considered and filtered. nfeat and frac will count for the features of the type that the Filter can operate on; this means e.g. that setting nfeat to 0 will only remove features of the type that the Filter can work with.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpFilter$new(filter, id = filter$id, param_vals = list())
  • filter :: Filter
    Filter used for feature filtering.

  • id :: character(1) Identifier of the resulting object, defaulting to the id of the Filter being used.

  • param_vals :: named list
    List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with features removed that were filtered out.

State

The $state is a named list with the $state elements inherited from PipeOpTaskPreproc, as well as:

  • scores :: named numeric
    Scores calculated for all features of the training Task which are being used as cutoff for feature filtering. If frac or nfeat is given, the underlying Filter may choose to not calculate scores for all features that are given. This only includes features on which the Filter can operate; e.g. if the Filter can only operate on numeric features, then scores for factorial features will not be given.

  • features :: character
    Names of features that are being kept. Features of types that the Filter can not operate on are always being kept.

Parameters

The parameters are the parameters inherited from the PipeOpTaskPreproc, as well as the parameters of the Filter used by this object. Besides, parameters introduced are:

  • filter.nfeat :: numeric(1)
    Number of features to select. Mutually exclusive with frac and cutoff.

  • filter.frac :: numeric(1)
    Fraction of features to keep. Mutually exclusive with nfeat and cutoff.

  • filter.cutoff :: numeric(1)
    Minimum value of filter heuristic for which to keep features. Mutually exclusive with nfeat and frac.

Note that at least one of filter.nfeat, filter.frac, or filter.cutoff must be given.

Internals

This does not use the $select_cols feature of PipeOpTaskPreproc to select only features compatible with the Filter; instead the whole Task is used by $get_state() and subset internally.

Fields

Fields inherited from PipeOpTaskPreproc, as well as:

  • filter :: Filter
    Filter that is being used for feature filtering. Do not use this slot to get to the feature filtering scores after training; instead, use $state$scores. Read-only.

Methods

Methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See also

Examples

library("mlr3") library("mlr3filters") # setup PipeOpFilter to keep the 5 most important # features of the spam task w.r.t. their AUC task = tsk("spam") filter = flt("auc") po = po("filter", filter = filter) po$param_set
#> ParamSet: auc #> id class lower upper levels default value #> 1: filter.nfeat ParamInt 0 Inf <NoDefault> #> 2: filter.frac ParamDbl 0 1 <NoDefault> #> 3: filter.cutoff ParamDbl -Inf Inf <NoDefault> #> 4: affect_columns ParamUty NA NA <NoDefault>
po$param_set$values$filter.nfeat = 5 # filter the task filtered_task = po$train(list(task))[[1]] # filtered task + extracted AUC scores filtered_task$feature_names
#> [1] "capitalAve" "capitalLong" "charDollar" "charExclamation" #> [5] "your"
head(po$state$scores, 10)
#> charExclamation capitalLong capitalAve your charDollar #> 0.3290461 0.3041626 0.2882004 0.2801659 0.2721394 #> capitalTotal free our you remove #> 0.2622801 0.2327285 0.2109325 0.2104681 0.2031303
# feature selection embedded in a 3-fold cross validation # keep 30% of features based on their AUC score task = tsk("spam") gr = po("filter", filter = flt("auc"), filter.frac = 0.5) %>>% po("learner", lrn("classif.rpart")) learner = GraphLearner$new(gr) rr = resample(task, learner, rsmp("holdout"), store_models = TRUE) rr$learners[[1]]$model$auc$scores
#> charExclamation capitalLong capitalAve your #> 0.334104675 0.303172295 0.284178501 0.274073473 #> charDollar capitalTotal free our #> 0.268829241 0.260534520 0.238240723 0.212845684 #> you remove money all #> 0.202178359 0.193070040 0.177594683 0.176178991 #> hp num000 business internet #> 0.175957873 0.150730517 0.144906996 0.138119186 #> george over mail hpl #> 0.135077639 0.133729691 0.133660830 0.131520523 #> receive email address order #> 0.126980784 0.122406838 0.118232647 0.115468784 #> num1999 make charHash credit #> 0.105703146 0.100847045 0.099345723 0.093536735 #> people labs will addresses #> 0.085092887 0.073494139 0.071127796 0.068573648 #> num85 num650 lab edu #> 0.067434521 0.067365883 0.062974375 0.059673479 #> technology meeting telnet data #> 0.057667319 0.053994836 0.051205037 0.047252189 #> report pm charSquarebracket num857 #> 0.042756495 0.042534929 0.037635231 0.035629518 #> project num415 original re #> 0.035013562 0.034052179 0.033806915 0.031376629 #> conference cs charSemicolon font #> 0.028086464 0.026428190 0.022944631 0.020986316 #> charRoundbracket num3d direct table #> 0.014926251 0.011405130 0.006168500 0.002078599 #> parts #> 0.001845631