R: Fuse learner with a feature filter method.

makeFilterWrapper {mlr} | R Documentation

## Fuse learner with a feature filter method.

### Description

Fuses a base learner with a filter method. Creates a learner object, which can be used like any other learner object. Internally uses filterFeatures before every model fit.

### Usage

    makeFilterWrapper(
      learner,
      fw.method = "randomForestSRC_importance",
      fw.base.methods = NULL,
      fw.perc = NULL,
      fw.abs = NULL,
      fw.threshold = NULL,
      fw.fun = NULL,
      fw.fun.args = NULL,
      fw.mandatory.feat = NULL,
      cache = FALSE,
      ...
    )

### Arguments

`learner` | (Learner | `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`fw.method` | (`character(1)`) Filter method. See listFilterMethods. Default is “randomForestSRC_importance”.
`fw.base.methods` | (`character(1)`) Simple Filter methods for ensemble filters. See listFilterMethods. Can only be used in combination with ensemble filters. See listFilterEnsembleMethods.
`fw.perc` | (`numeric(1)`) If set, select `fw.perc`*100 top scoring features. Mutually exclusive with arguments `fw.abs`, `fw.threshold` and 'fw.fun.
`fw.abs` | (`numeric(1)`) If set, select `fw.abs` top scoring features. Mutually exclusive with arguments `fw.perc`, `fw.threshold` and `fw.fun`.
`fw.threshold` | (`numeric(1)`) If set, select features whose score exceeds `fw.threshold`. Mutually exclusive with arguments `fw.perc`, `fw.abs` and `fw.fun`.
`fw.fun` | (`function)`) If set, select features via a custom thresholding function, which must return the number of top scoring features to select. Mutually exclusive with arguments `fw.perc`, `fw.abs` and `fw.threshold`.
`fw.fun.args` | (any) Arguments passed to the custom thresholding function
`fw.mandatory.feat` | (character) Mandatory features which are always included regardless of their scores
`cache` | (`character(1)` | logical) Whether to use caching during filter value creation. See details.
`...` | (any) Additional parameters passed down to the filter. If you are using more than one filter method, you need to pass the arguments in a named list via `more.args`. For example `more.args = list("FSelectorRcpp_information.gain" = list(equal = TRUE))`.

### Details

If `ensemble = TRUE`, ensemble feature selection using all methods specified in `fw.method` is performed. At least two methods need to be selected.

After training, the selected features can be retrieved with getFilteredFeatures.

Note that observation weights do not influence the filtering and are simply passed down to the next learner.

### Value

Learner.

### Caching

If `cache = TRUE`, the default mlr cache directory is used to cache filter values. The directory is operating system dependent and can be checked with `getCacheDir()`. Alternatively a custom directory can be passed to store the cache. The cache can be cleared with `deleteCacheDir()`. Caching is disabled by default. Care should be taken when operating on large clusters due to possible write conflicts to disk if multiple workers try to write the same cache at the same time.

### See Also

Other filter: `filterFeatures()`, `generateFilterValuesData()`, `getFilteredFeatures()`, `listFilterEnsembleMethods()`, `listFilterMethods()`, `makeFilterEnsemble()`, `makeFilter()`, `plotFilterValues()`

Other wrapper: `makeBaggingWrapper()`, `makeClassificationViaRegressionWrapper()`, `makeConstantClassWrapper()`, `makeCostSensClassifWrapper()`, `makeCostSensRegrWrapper()`, `makeDownsampleWrapper()`, `makeDummyFeaturesWrapper()`, `makeExtractFDAFeatsWrapper()`, `makeFeatSelWrapper()`, `makeImputeWrapper()`, `makeMulticlassWrapper()`, `makeMultilabelBinaryRelevanceWrapper()`, `makeMultilabelClassifierChainsWrapper()`, `makeMultilabelDBRWrapper()`, `makeMultilabelNestedStackingWrapper()`, `makeMultilabelStackingWrapper()`, `makeOverBaggingWrapper()`, `makePreprocWrapperCaret()`, `makePreprocWrapper()`, `makeRemoveConstantFeaturesWrapper()`, `makeSMOTEWrapper()`, `makeTuneWrapper()`, `makeUndersampleWrapper()`, `makeWeightedClassesWrapper()`

### Examples

    task = makeClassifTask(data = iris, target = "Species")
    lrn = makeLearner("classif.lda")
    inner = makeResampleDesc("Holdout")
    outer = makeResampleDesc("CV", iters = 2)
    lrn = makeFilterWrapper(lrn, fw.perc = 0.5)
    mod = train(lrn, task)
    print(getFilteredFeatures(mod))
    # now nested resampling, where we extract the features that the filter method selected
    r = resample(lrn, task, outer, extract = function(model) {
      getFilteredFeatures(model)
    })
    print(r$extract)

    # usage of an ensemble filter
    lrn = makeLearner("classif.lda")
    lrn = makeFilterWrapper(lrn, fw.method = "E-Borda",
      fw.base.methods = c("FSelectorRcpp_gain.ratio", "FSelectorRcpp_information.gain"),
      fw.perc = 0.5)
    r = resample(lrn, task, outer, extract = function(model) {
      getFilteredFeatures(model)
    })
    print(r$extract)

    # usage of a custom thresholding function
    biggest_gap = function(values, diff) {
      gap_size = 0
      gap_location = 0

      for (i in (diff + 1):length(values)) {
        gap = values[[i - diff]] - values[[i]]
        if (gap > gap_size) {
          gap_size = gap
          gap_location = i - 1
        }
      }
      return(gap_location)
    }

    lrn = makeLearner("classif.lda")
    lrn = makeFilterWrapper(lrn, fw.method = "randomForestSRC_importance",
      fw.fun = biggest_gap, fw.fun.args = list("diff" = 1))
    r = resample(lrn, task, outer, extract = function(model) {
      getFilteredFeatures(model)
    })
    print(r$extract)

[Package mlr version 2.17.1 Index]