Add help() method for most objects

R/BenchmarkResult.R

@@ -98,6 +98,10 @@
 #'   Fuses a second [BenchmarkResult] into itself, mutating the [BenchmarkResult] in-place.
 #'   If `bmr` is `NULL`, simply returns `self`.
 #'
+#' * `help()`\cr
+#'   () -> `NULL`\cr
+#'   Opens the help page for this object.
+#'
 #' @section S3 Methods:
 #' * `as.data.table(bmr)`\cr
 #'   [BenchmarkResult] -> [data.table::data.table()]\cr
@@ -158,6 +162,10 @@ BenchmarkResult = R6Class("BenchmarkResult",
       self$rr_data = data[, list(uhash = unique(uhash))]
     },
 
+    help = function() {
+      open_help("mlr3::BenchmarkResult")
+    },
+
     format = function() {
       sprintf("<%s>", class(self)[1L])
     },

R/Learner.R

@@ -66,6 +66,9 @@
 #'   Set of required packages.
 #'   Note that these packages will be loaded via [requireNamespace()], and are not attached.
 #'
+#' * `man` :: `character(1)`\cr
+#'   String in the format `[pkg]::[topic]` pointing to a manual page for this object.
+#'
 #' @section Fields:
 #' * `id` :: `character(1)`\cr
 #'   Identifier of the learner.
@@ -147,6 +150,10 @@
 #'   Uses the data stored during `$train()` in `$state` to create a new [Prediction] based on the new data in `newdata`.
 #'   Object `task` is the task used during `$train()` and required for conversions of `newdata`.
 #'
+#' * `help()`\cr
+#'   () -> `NULL`\cr
+#'   Opens the corresponding help page referenced by `$man`.
+#'
 #' @section Optional Extractors:
 #'
 #' Specific learner implementations are free to implement additional getters to ease the access of certain parts
@@ -202,9 +209,10 @@ Learner = R6Class("Learner",
     packages = NULL,
     predict_sets = "test",
     fallback = NULL,
+    man = NULL,
 
-    initialize = function(id, task_type, param_set = ParamSet$new(), predict_types = character(),
-      feature_types = character(), properties = character(), data_formats = "data.table", packages = character()) {
+    initialize = function(id, task_type, param_set = ParamSet$new(), predict_types = character(), feature_types = character(),
+      properties = character(), data_formats = "data.table", packages = character(), man = NA_character_) {
 
       self$id = assert_string(id, min.chars = 1L)
       self$task_type = assert_choice(task_type, mlr_reflections$task_types$type)
@@ -213,9 +221,14 @@ Learner = R6Class("Learner",
       self$feature_types = assert_subset(feature_types, mlr_reflections$task_feature_types)
       self$predict_types = assert_subset(predict_types, names(mlr_reflections$learner_predict_types[[task_type]]), empty.ok = FALSE)
       private$.predict_type = predict_types[1L]
-      self$packages = assert_set(packages)
       self$properties = sort(assert_subset(properties, mlr_reflections$learner_properties[[task_type]]))
       self$data_formats = assert_subset(data_formats, mlr_reflections$data_formats)
+      self$packages = assert_set(packages)
+      self$man = assert_string(man, na.ok = TRUE)
+    },
+
+    help = function() {
+      open_help(self$man)
     },
 
     format = function() {

R/LearnerClassif.R

@@ -12,7 +12,7 @@
 #' @section Construction:
 #' ```
 #' l = LearnerClassif$new(id, param_set = ParamSet$new(), predict_types = character(), feature_types = character(),
-#'     properties = character(), data_formats = "data.table", packages = character())
+#'     properties = character(), data_formats = "data.table", packages = character(), man = NA_character_)
 #' ```
 #' For a description of the arguments, see [Learner].
 #' `task_type` is set to `"classif"`.
@@ -53,9 +53,9 @@
 #' lrn$predict(task, 121:150)$confusion
 LearnerClassif = R6Class("LearnerClassif", inherit = Learner,
   public = list(
-    initialize = function(id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character()) {
+    initialize = function(id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character(), man = NA_character_) {
       super$initialize(id = id, task_type = "classif", param_set = param_set, predict_types = predict_types,
-        feature_types = feature_types, properties = properties, data_formats = data_formats, packages = packages)
+        feature_types = feature_types, properties = properties, data_formats = data_formats, packages = packages, man = man)
     }
   )
 )

R/LearnerClassifDebug.R

@@ -67,7 +67,8 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
             ParamDbl$new("x", lower = 0, upper = 1, tags = "train")
           )
         ),
-        properties = c("twoclass", "multiclass", "missings")
+        properties = c("twoclass", "multiclass", "missings"),
+        man = "mlr3::mlr_learners_classif.debug"
       )
     },
 

R/LearnerClassifFeatureless.R

@@ -32,7 +32,8 @@ LearnerClassifFeatureless = R6Class("LearnerClassifFeatureless", inherit = Learn
         feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass", "missings", "importance", "selected_features")
+        properties = c("twoclass", "multiclass", "missings", "importance", "selected_features"),
+        man = "mlr3::mlr_learners_classif.featureless"
       )
     },
 

R/LearnerClassifRpart.R

@@ -40,7 +40,8 @@ LearnerClassifRpart = R6Class("LearnerClassifRpart", inherit = LearnerClassif,
         feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass", "weights", "missings", "importance", "selected_features")
+        properties = c("twoclass", "multiclass", "weights", "missings", "importance", "selected_features"),
+        man = "mlr3::mlr_learners_classif.rpart"
       )
     },
 

R/LearnerRegr.R

@@ -12,7 +12,7 @@
 #' @section Construction:
 #' ```
 #' l = LearnerRegr$new(id, param_set = ParamSet$new(), predict_types = character(), feature_types = character(),
-#'     properties = character(), data_formats = "data.table", packages = character())
+#'     properties = character(), data_formats = "data.table", packages = character(), man = NA_character_)
 #' ```
 #' For a description of the arguments, see [Learner].
 #' `task_type` is set to `"regr"`.
@@ -41,9 +41,9 @@
 #' lrn("classif.featureless")
 LearnerRegr = R6Class("LearnerRegr", inherit = Learner,
   public = list(
-    initialize = function(id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character()) {
+    initialize = function(id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character(), man = NA_character_) {
       super$initialize(id = id, task_type = "regr", param_set = param_set, feature_types = feature_types,
-        predict_types = predict_types, properties = properties, data_formats = data_formats, packages = packages)
+        predict_types = predict_types, properties = properties, data_formats = data_formats, packages = packages, man = man)
     }
   )
 )

R/LearnerRegrFeatureless.R

@@ -35,7 +35,8 @@ LearnerRegrFeatureless = R6Class("LearnerRegrFeatureless", inherit = LearnerRegr
         predict_types = c("response", "se"),
         param_set = ps,
         properties = c("missings", "importance", "selected_features"),
-        packages = "stats"
+        packages = "stats",
+        man = "mlr3::mlr_learners_regr.featureless"
       )
     },
 

R/LearnerRegrRpart.R

@@ -40,7 +40,8 @@ LearnerRegrRpart = R6Class("LearnerRegrRpart", inherit = LearnerRegr,
         predict_types = "response",
         packages = "rpart",
         param_set = ps,
-        properties = c("weights", "missings", "importance", "selected_features")
+        properties = c("weights", "missings", "importance", "selected_features"),
+        man = "mlr3::mlr_learners_regr.rpart"
       )
     },
 

R/Measure.R

@@ -23,8 +23,8 @@
 #' Note: This object is typically constructed via a derived classes, e.g. [MeasureClassif] or [MeasureRegr].
 #'
 #' ```
-#' m = Measure$new(id, task_type = NA, range = c(-Inf, Inf), minimize = NA, aggregator = NULL, properties = character(), predict_type = "response", predict_sets = "test",
-#'      task_properties = character(), packages = character())
+#' m = Measure$new(id, task_type = NA, range = c(-Inf, Inf), minimize = NA, aggregator = NULL, properties = character(), predict_type = "response",
+#'     predict_sets = "test", task_properties = character(), packages = character(), man = NA_character_)
 #' ```
 #'
 #' * `id` :: `character(1)`\cr
@@ -73,6 +73,9 @@
 #'   Set of required packages.
 #'   Note that these packages will be loaded via [requireNamespace()], and are not attached.
 #'
+#' * `man` :: `character(1)`\cr
+#'   String in the format `[pkg]::[topic]` pointing to a manual page for this object.
+#'
 #' @section Fields:
 #' All variables passed to the constructor.
 #'
@@ -90,6 +93,10 @@
 #'   pass the respective [Task], the trained [Learner] or the training set indices.
 #'   This is handled internally during [resample()]/[benchmark()].
 #'
+#' * `help()`\cr
+#'   () -> `NULL`\cr
+#'   Opens the corresponding help page referenced by `$man`.
+#'
 #' @family Measure
 #' @export
 Measure = R6Class("Measure",
@@ -105,8 +112,10 @@ Measure = R6Class("Measure",
     properties = NULL,
     minimize = NULL,
     packages = NULL,
+    man = NULL,
 
-    initialize = function(id, task_type = NA, range = c(-Inf, Inf), minimize = NA, aggregator = NULL, properties = character(), predict_type = "response", predict_sets = "test", task_properties = character(), packages = character()) {
+    initialize = function(id, task_type = NA, range = c(-Inf, Inf), minimize = NA, aggregator = NULL, properties = character(), predict_type = "response",
+      predict_sets = "test", task_properties = character(), packages = character(), man = NA_character_) {
 
       self$id = assert_string(id, min.chars = 1L)
       self$task_type = task_type
@@ -126,6 +135,11 @@ Measure = R6Class("Measure",
       self$predict_sets = assert_subset(predict_sets, mlr_reflections$predict_sets, empty.ok = FALSE)
       self$task_properties = assert_subset(task_properties, mlr_reflections$task_properties[[task_type]])
       self$packages = assert_set(packages)
+      self$man = assert_string(man, na.ok = TRUE)
+    },
+
+    help = function() {
+      open_help(self$man)
     },
 
     format = function() {

R/MeasureClassif.R

@@ -10,8 +10,8 @@
 #'
 #' @section Construction:
 #' ```
-#' m = MeasureClassif$new(id, range, minimize = NA, aggregator = NULL, properties = character(),
-#'      predict_type = "response", task_properties = character(), packages = character())
+#' m = MeasureClassif$new(id, range, minimize = NA, aggregator = NULL, properties = character(), predict_type = "response",
+#'     predict_sets = "test", task_properties = character(), packages = character(), man = NA_character_)
 #' ```
 #' For a description of the arguments, see [Measure].
 #' The `task_type` is set to `"classif"`.
@@ -29,9 +29,11 @@
 #' @export
 MeasureClassif = R6Class("MeasureClassif", inherit = Measure, cloneable = FALSE,
   public = list(
-    initialize = function(id, range, minimize = NA, aggregator = NULL, properties = character(), predict_type = "response", task_properties = character(), packages = character()) {
+    initialize = function(id, range, minimize = NA, aggregator = NULL, properties = character(), predict_type = "response",
+      predict_sets = "test", task_properties = character(), packages = character(), man = NA_character_) {
       super$initialize(id, task_type = "classif", range = range, minimize = minimize, aggregator = aggregator,
-        properties = properties, predict_type = predict_type, task_properties = task_properties, packages = packages)
+        properties = properties, predict_type = predict_type, predict_sets = predict_sets,
+        task_properties = task_properties, packages = packages, man = man)
     }
   )
 )

R/MeasureClassifACC.R

@@ -25,7 +25,8 @@ MeasureClassifACC = R6Class("MeasureClassifACC",
         id = "classif.acc",
         range = 0:1,
         minimize = FALSE,
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_classif.acc"
       )
     },
 

R/MeasureClassifAUC.R

@@ -27,7 +27,8 @@ MeasureClassifAUC = R6Class("MeasureClassifAUC",
         minimize = FALSE,
         predict_type = "prob",
         task_properties = "twoclass",
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_classif.auc"
       )
     },
 

R/MeasureClassifCE.R

@@ -25,7 +25,8 @@ MeasureClassifCE = R6Class("MeasureClassifCE",
         id = "classif.ce",
         range = 0:1,
         minimize = TRUE,
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_classif.auc"
       )
     },
 

R/MeasureClassifConfusion.R

@@ -114,7 +114,8 @@ MeasureClassifConfusion = R6Class("MeasureClassifConfusion",
         properties = if (row$na_score) "na_score" else character(),
         minimize = row$minimize,
         predict_type = "response",
-        task_properties = "twoclass"
+        task_properties = "twoclass",
+        man = "mlr3::mlr_measures_classif.confusion"
       )
     },
 

R/MeasureClassifCosts.R

@@ -56,7 +56,8 @@ MeasureClassifCosts = R6Class("MeasureClassifCosts",
         id = "classif.costs",
         properties = "requires_task",
         range = c(-Inf, Inf),
-        minimize = TRUE
+        minimize = TRUE,
+        man = "mlr3::mlr_measures_classif.costs"
       )
 
       if (!is.null(costs)) {

R/MeasureClassifFScore.R

@@ -37,7 +37,8 @@ MeasureClassifFScore = R6Class("MeasureClassifFScore",
         minimize = FALSE,
         predict_type = "response",
         task_properties = "twoclass",
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_classif.f_score"
       )
 
       self$beta = assert_number(beta, lower = 0)

R/MeasureDebug.R

@@ -40,7 +40,8 @@ MeasureDebug = R6Class("MeasureDebug",
         id = "debug",
         predict_type = "response",
         range = c(0, Inf),
-        properties = "na_score"
+        properties = "na_score",
+        man = "mlr3::mlr_measures_debug"
       )
       self$na_ratio = assert_number(na_ratio, lower = 0, upper = 1)
     },

R/MeasureElapsedTime.R

@@ -1,7 +1,8 @@
 #' @title Elapsed Time Measure
 #'
 #' @usage NULL
-#' @aliases mlr_measures_time_train
+#' @aliases mlr_measures_elapsed_time
+#'   mlr_measures_time_train
 #'   mlr_measures_time_predict
 #'   mlr_measures_time_both
 #' @format [R6::R6Class()] inheriting from [Measure].
@@ -42,7 +43,8 @@ MeasureElapsedTime = R6Class("MeasureElapsedTime",
         task_type = NA_character_,
         predict_type = "response",
         range = c(0, Inf),
-        minimize = TRUE
+        minimize = TRUE,
+        man = "mlr3::mlr_measures_elapsed_time"
       )
       self$stages = assert_subset(stages, c("train", "predict"), empty.ok = FALSE)
     },

R/MeasureOOBError.R

@@ -31,7 +31,8 @@ MeasureOOBError = R6Class("MeasureOOBError",
         properties = c("na_score", "requires_learner"),
         predict_type = "response",
         range = c(-Inf, Inf),
-        minimize = NA
+        minimize = NA,
+        man = "mlr3::mlr_measures_oob_error"
       )
     },
 

R/MeasureRegr.R

@@ -10,8 +10,8 @@
 #'
 #' @section Construction:
 #' ```
-#' m = MeasureRegr$new(id, range, minimize = NA, aggregator = NULL, properties = character(),
-#'      predict_type = "response", task_properties = character(), packages = character())
+#' m = MeasureRegr$new(id, range, minimize = NA, aggregator = NULL, properties = character(), predict_type = "response",
+#'     predict_sets = "test", task_properties = character(), packages = character())
 #' ```
 #' For a description of the arguments, see [Measure].
 #' The `task_type` is set to `"regr"`.
@@ -29,9 +29,11 @@
 #' @export
 MeasureRegr = R6Class("MeasureRegr", inherit = Measure, cloneable = FALSE,
   public = list(
-    initialize = function(id, range, minimize = NA, aggregator = NULL, properties = character(), predict_type = "response", task_properties = character(), packages = character()) {
+    initialize = function(id, range, minimize = NA, aggregator = NULL, properties = character(), predict_type = "response",
+      predict_sets = "test", task_properties = character(), packages = character(), man = NA_character_) {
       super$initialize(id, task_type = "regr", range = range, minimize = minimize, aggregator = aggregator,
-        properties = properties, predict_type = predict_type, task_properties = task_properties, packages = packages)
+        properties = properties, predict_type = predict_type, predict_sets = predict_sets,
+        task_properties = task_properties, packages = packages)
     }
   )
 )

R/MeasureRegrMAE.R

@@ -25,7 +25,8 @@ MeasureRegrMAE = R6Class("MeasureRegrMAE",
         id = "regr.mae",
         range = c(0, Inf),
         minimize = TRUE,
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_regr.mae"
       )
     },
 

R/MeasureRegrMSE.R

@@ -25,7 +25,8 @@ MeasureRegrMSE = R6Class("MeasureRegrMSE",
         id = "regr.mse",
         range = c(0, Inf),
         minimize = TRUE,
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_regr.mse"
       )
     },
 

R/MeasureRegrRMSE.R

@@ -25,7 +25,8 @@ MeasureRegrRMSE = R6Class("MeasureRegrRMSE",
         id = "regr.rmse",
         range = c(0, Inf),
         minimize = TRUE,
-        packages = "Metrics"
+        packages = "Metrics",
+        man = "mlr3::mlr_measures_regr.rmse"
       )
     },