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mllgmllg
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Add help method for filters (#68)
* Filters now have a help method `$help()` which opens the respective help page (#68)
1 parent 64e9a51 commit 12af76f

40 files changed

+155
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lines changed

‎.pre-commit-config.yaml

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@@ -3,10 +3,15 @@ repos:
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rev: v0.0.0.9027
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hooks:
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- id: style-files
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args: [--style_fun=mlr_style]
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args: [--style_pkg=styler, --style_fun=mlr_style]
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- id: parsable-R
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- id: no-browser-statement
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- id: readme-rmd-rendered
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- id: roxygenize
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- id: use-tidy-description
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- id: codemeta-description-updated
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#- id: codemeta-description-updated
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v2.5.0
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hooks:
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- id: check-added-large-files
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args: ['--maxkb=200']

‎DESCRIPTION

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@@ -35,7 +35,7 @@ Imports:
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backports,
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checkmate,
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data.table,
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mlr3 (>= 0.1.2),
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mlr3 (>= 0.1.8),
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mlr3misc,
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paradox,
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R6
@@ -52,7 +52,7 @@ Suggests:
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Encoding: UTF-8
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NeedsCompilation: no
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Roxygen: list(markdown = TRUE, r6 = TRUE)
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RoxygenNote: 7.0.2
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RoxygenNote: 7.1.0
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Collate:
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'Filter.R'
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'mlr_filters.R'

‎R/Filter.R

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@@ -31,6 +31,11 @@ Filter = R6Class("Filter",
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feature_types = NULL,
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packages = NULL,
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#' @field man (`character(1)`)\cr
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#' String in the format `[pkg]::[topic]` pointing to a manual page for this object.
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#' Defaults to `NA`, but can be set by child classes.
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man = NULL,
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#' @field scores
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#' Stores the calculated filter score values as named numeric vector.
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#' The vector is sorted in decreasing order with possible `NA` values
@@ -58,9 +63,13 @@ Filter = R6Class("Filter",
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#' Set of required packages.
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#' Note that these packages will be loaded via [requireNamespace()], and
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#' are not attached.
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#' @param man (`character(1)`)\cr
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#' String in the format `[pkg]::[topic]` pointing to a manual page for
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#' this object. The referenced help package can be opened via method
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#' `$help()`.
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initialize = function(id, task_type, task_properties = character(),
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param_set = ParamSet$new(), feature_types = character(),
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packages = character()) {
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packages = character(), man = NA_character_) {
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self$id = assert_string(id)
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self$task_type = assert_subset(task_type, mlr_reflections$task_types$type,
@@ -73,6 +82,7 @@ Filter = R6Class("Filter",
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self$packages = assert_character(packages, any.missing = FALSE,
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unique = TRUE)
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self$scores = set_names(numeric(), character())
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self$man = assert_string(man, na.ok = TRUE)
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},
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#' @description
@@ -95,6 +105,12 @@ Filter = R6Class("Filter",
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}
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},
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#' @description
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#' Opens the corresponding help page referenced by field `$man`.
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help = function() {
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open_help(self$man) # nocov
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},
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#' @description
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#' Calculates the filter score values for the provided [mlr3::Task] and
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#' stores them in field `scores`. `nfeat` determines the minimum number of

‎R/FilterAUC.R

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@@ -52,7 +52,8 @@ FilterAUC = R6Class("FilterAUC", inherit = Filter,
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task_type = task_type,
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task_properties = task_properties,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_auc"
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)
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}
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),

‎R/FilterAnova.R

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@@ -54,7 +54,8 @@ FilterAnova = R6Class("FilterAnova", inherit = Filter,
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id = id,
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packages = packages,
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feature_types = feature_types,
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task_type = task_type
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task_type = task_type,
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man = "mlr3filters::mlr_filters_anova"
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)
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}
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),

‎R/FilterCMIM.R

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@@ -47,7 +47,8 @@ FilterCMIM = R6Class("FilterCMIM", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_cmim"
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)
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}
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),

‎R/FilterCarScore.R

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@@ -57,7 +57,9 @@ FilterCarScore = R6Class("FilterCarScore", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_carscore"
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)
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self$param_set$values = list(verbose = FALSE)
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}

‎R/FilterCorrelation.R

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@@ -57,7 +57,8 @@ FilterCorrelation = R6Class("FilterCorrelation", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_correlation"
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)
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}
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),

‎R/FilterDISR.R

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@@ -47,7 +47,8 @@ FilterDISR = R6Class("FilterDISR", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_disr"
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)
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}
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),

‎R/FilterFindCorrelation.R

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@@ -65,7 +65,8 @@ FilterFindCorrelation = R6Class("FilterFindCorrelation", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_find_correlation"
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)
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}
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),

‎R/FilterImportance.R

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@@ -55,7 +55,8 @@ FilterImportance = R6Class("FilterImportance", inherit = Filter,
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task_type = task_type,
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feature_types = feature_types,
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packages = packages,
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param_set = param_set
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param_set = param_set,
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man = "mlr3filters::mlr_filters_importance"
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)
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}
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),

‎R/FilterInformationGain.R

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@@ -64,7 +64,8 @@ FilterInformationGain = R6Class("FilterInformationGain", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_information_gain"
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)
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}
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),

‎R/FilterJMI.R

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@@ -47,7 +47,8 @@ FilterJMI = R6Class("FilterJMI", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_jmi"
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)
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}
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),

‎R/FilterJMIM.R

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@@ -47,7 +47,8 @@ FilterJMIM = R6Class("FilterJMIM", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_jmim"
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)
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}
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),

‎R/FilterKruskalTest.R

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@@ -53,7 +53,8 @@ FilterKruskalTest = R6Class("FilterKruskalTest", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_kruskal_test"
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)
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}
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),

‎R/FilterMIM.R

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@@ -47,7 +47,8 @@ FilterMIM = R6Class("FilterMIM", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_mim"
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)
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}
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),

‎R/FilterMRMR.R

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@@ -47,7 +47,8 @@ FilterMRMR = R6Class("FilterMRMR", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_mrmr"
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)
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}
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),

‎R/FilterNJMIM.R

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@@ -47,7 +47,8 @@ FilterNJMIM = R6Class("FilterNJMIM", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_njmim"
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)
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}
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),

‎R/FilterPerformance.R

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@@ -46,29 +46,27 @@ FilterPerformance = R6Class("FilterPerformance", inherit = Filter,
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#' [mlr3::Resampling] to be used within resampling.
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#' @param measure ([mlr3::Measure])\cr
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#' [mlr3::Measure] to be used for evaluating the performance.
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#' @param packages (`character()`)\cr
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#' Set of required packages.
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#' Note that these packages will be loaded via [requireNamespace()], and
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#' are not attached.
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initialize = function(id = "performance",
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task_type = learner$task_type,
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param_set = learner$param_set,
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feature_types = learner$feature_types,
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learner = mlr3::lrn("classif.rpart"),
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resampling = mlr3::rsmp("holdout"),
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measure = mlr3::msr("classif.ce"),
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packages = learner$packages) {
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measure = mlr3::msr("classif.ce")) {
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self$learner = learner = assert_learner(as_learner(learner, clone = TRUE),
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properties = "importance")
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self$resampling = assert_resampling(as_resampling(resampling))
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self$measure = assert_measure(as_measure(measure,
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task_type = learner$task_type, clone = TRUE), learner = learner)
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packages = unique(c(self$learner$packages, self$measure$packages))
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super$initialize(
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id = id,
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task_type = task_type,
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feature_types = feature_types
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feature_types = feature_types,
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packages = packages,
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man = "mlr3filters::mlr_filters_performance"
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)
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}
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),

‎R/FilterVariance.R

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@@ -47,7 +47,8 @@ FilterVariance = R6Class("FilterVariance", inherit = Filter,
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task_type = task_type,
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param_set = param_set,
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feature_types = feature_types,
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packages = packages
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packages = packages,
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man = "mlr3filters::mlr_filters_variance"
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)
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self$param_set$values = list(na.rm = TRUE)
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}

‎codemeta.json

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],
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"issueTracker": "https://github.com/mlr-org/mlr3filters/issues",
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"license": "https://spdx.org/licenses/LGPL-3.0",
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"version": "0.1.1.9002",
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"version": "0.2.0.9000",
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"programmingLanguage": {
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"@type": "ComputerLanguage",
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"name": "R",
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"version": "3.6.2",
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"version": "3.6.3",
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"url": "https://r-project.org"
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},
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"runtimePlatform": "R version 3.6.2 (2019-12-12)",
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"runtimePlatform": "R version 3.6.3 Patched (2020-02-29 r77919)",
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"provider": {
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"@id": "https://cran.r-project.org",
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"@type": "Organization",
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"@type": "SoftwareApplication",
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"identifier": "mlr3",
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"name": "mlr3",
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"version": ">= 0.1.2",
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"version": ">= 0.1.8",
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"provider": {
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"@id": "https://cran.r-project.org",
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"@type": "Organization",
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],
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"releaseNotes": "https://github.com/mlr-org/mlr3filters/blob/master/NEWS.md",
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"readme": "https://github.com/mlr-org/mlr3filters/blob/master/README.md",
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"fileSize": "22.613KB",
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"fileSize": "23.59KB",
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"contIntegration": "https://codecov.io/github/mlr-org/mlr3filters?branch=master",
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"keywords": [
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"filter",

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