docs: rewrite examples (#305)

DESCRIPTION

@@ -26,14 +26,14 @@ Authors@R: c(
 Description: Extra learners for use in mlr3.
 License: LGPL-3
 Depends:
-    R (>= 3.1.0)
+    R (>= 3.1.0),
+    mlr3 (>= 0.20.2)
 Imports:
     checkmate,
     data.table,
     methods,
-    mlr3 (>= 0.13.4),
     mlr3misc (>= 0.9.4),
-    paradox,
+    paradox (>= 1.0.1),
     R6
 Suggests:
     abess,
@@ -77,7 +77,6 @@ Suggests:
     mvtnorm,
     nnet,
     np,
-    obliqueRSF,
     param6,
     partykit,
     penalized,

NAMESPACE

@@ -116,15 +116,13 @@ export(LearnerSurvLearnerSurvBART)
 export(LearnerSurvLogisticHazard)
 export(LearnerSurvMBoost)
 export(LearnerSurvNelson)
-export(LearnerSurvObliqueRSF)
 export(LearnerSurvPCHazard)
 export(LearnerSurvParametric)
 export(LearnerSurvPenalized)
 export(LearnerSurvPriorityLasso)
 export(LearnerSurvRandomForestSRC)
 export(LearnerSurvRanger)
 export(LearnerSurvSVM)
-export(LearnerSurvXgboost)
 export(LearnerSurvXgboostAFT)
 export(LearnerSurvXgboostCox)
 export(create_learner)

NEWS.md

@@ -10,7 +10,12 @@
   and `lrn("surv.xgboost.cox")`.
 * Added early stopping and validation to catboost and lightgbm.
 * Added missing `case.depth` parameter to `rfsrc` learners.
+* `mlr3` is now in Depends instead of Imports.
+* Deprecated learner `lrn("surv.xgboost")` was now removed.
+  Use `lrn("surv.xgboost.cox")` or `lrn("surv.xgboost.aft")` instead.
 * Change xgboost default nrounds from 1 to 1000.
+* remove obliqueRSF Learner which was long superseded by aorsf
+* a lot of examples were added to the learners
 
 # mlr3extralearners 0.8.0
 

R/create_learner.R

@@ -36,9 +36,6 @@
 #'   Defaults to `"Unknown"`.
 #' @param label (`character(1)`)\cr
 #'   Label for the learner, default is the value of the parameter `algorithm`.
-#' @param data_formats (`character(1)`)\cr
-#'   The data format the learner can deal with (see `mlr_reflections$data_formats`).
-#'   Default is `"data.table"` and this rarely has to be changes.
 #'
 #' @examples
 #' \dontrun{
@@ -56,14 +53,13 @@
 #'   predict_types = c("response", "prob"),
 #'   properties = c("importance", "missings", "multiclass", "twoclass", "weights"),
 #'   gh_name = "RaphaelS1",
-#'   label = "Regression and Partition Tree",
-#'   data_formats = "data.table"
+#'   label = "Regression and Partition Tree"
 #' )
 #' }
 #' @export
 create_learner = function(path = ".", classname, type, key = tolower(classname), algorithm,
   package, caller,
-  feature_types, predict_types, properties, gh_name = "Unknown", label = toproper(algorithm), data_formats = "data.table") {
+  feature_types, predict_types, properties, gh_name = "Unknown", label = toproper(algorithm)) {
 
   path = normalizePath(path)
   assert_true(dir.exists(path))
@@ -95,7 +91,6 @@ create_learner = function(path = ".", classname, type, key = tolower(classname),
   assert_character(gh_name, any.missing = FALSE, len = 1)
   assert_character(label, any.missing = FALSE, len = 1L)
 
-  assert_choice(data_formats, mlr3::mlr_reflections$data_formats)
   Type = toproper(type)
 
   assert_character(classname, any.missing = FALSE, len = 1)

R/fn_list_mlr3learners.R

@@ -4,10 +4,6 @@
 #' @param select `character()` \cr Passed to [data.table::subset].
 #' @param filter `list()` \cr Named list of conditions to filter on, names correspond to column
 #' names in table.
-#' @examples
-#' list_mlr3learners(
-#'   select = c("id", "properties", "predict_types"),
-#'   filter = list(class = "surv", predict_types = "distr"))
 #' @export
 list_mlr3learners = function(select = NULL, filter = NULL) {
   require_namespaces(c("mlr3learners", "mlr3proba", "mlr3cluster"), quietly = TRUE)

R/learner_BART_surv_bart.R

@@ -37,7 +37,7 @@
 #' `r format_bib("sparapani2021nonparametric", "chipman2010bart")`
 #'
 #' @template seealso_learner
-#' @template example
+#' @template simple_example
 #' @export
 LearnerSurvLearnerSurvBART = R6Class("LearnerSurvLearnerSurvBART",
   inherit = mlr3proba::LearnerSurv,

R/learner_CoxBoost_surv_cv_coxboost.R

@@ -35,8 +35,14 @@
 #' `r format_bib("binder2009boosting")`
 #'
 #' @template seealso_learner
-#' @template example
 #' @export
+#' @examplesIf mlr3misc::require_namespaces(c("coin", "sandwich", "partykit"), quietly = TRUE)
+#' task = tsk("rats")
+#' task$col_roles$feature = c("litter", "rx")
+#' learner = lrn("surv.cv_coxboost", maxstepno = 20)
+#' splits = partition(task)
+#' learner$train(task, splits$train)
+#' pred = learner$predict(task, splits$test)
 LearnerSurvCVCoxboost = R6Class("LearnerSurvCVCoxboost",
   inherit = mlr3proba::LearnerSurv,
 

R/learner_catboost_classif_catboost.R

@@ -40,7 +40,30 @@
 #'
 #' @export
 #' @template seealso_learner
-#' @template example
+#' @examplesIf mlr3misc::require_namespaces("catboost", quietly = TRUE)
+#' # Define the Learner
+#' learner = mlr3::lrn("classif.catboost",
+#'   iterations = 100)
+#'
+#' print(learner)
+#'
+#' # Define a Task
+#' task = tsk("sonar")
+#'
+#' # Create train and test set
+#' ids = mlr3::partition(task)
+#'
+#' # Train the learner on the training ids
+#' learner$train(task, row_ids = ids$train)
+#'
+#' print(learner$model)
+#' print(learner$importance)
+#'
+#' # Make predictions for the test rows
+#' predictions = learner$predict(task, row_ids = ids$test)
+#'
+#' # Score the predictions
+#' predictions$score()
 LearnerClassifCatboost = R6Class("LearnerClassifCatboost",
   inherit = LearnerClassif,
   public = list(

R/learner_flexsurv_surv_flexible.R

@@ -33,7 +33,7 @@
 #' `r format_bib("royston2002flexible")`
 #'
 #' @template seealso_learner
-#' @template example
+#' @template simple_example
 #' @export
 LearnerSurvFlexible = R6Class("LearnerSurvFlexible",
   inherit = mlr3proba::LearnerSurv,

R/learner_gbm_regr_gbm.R

@@ -25,7 +25,10 @@
 #'
 #' @export
 #' @template seealso_learner
-#' @template example
+#' @examples
+#' # Define the Learner
+#' learner = lrn("regr.gbm")
+#' print(learner)
 LearnerRegrGBM = R6Class("LearnerRegrGBM",
   inherit = LearnerRegr,
   public = list(

R/learner_lightgbm_classif_lightgbm.R

@@ -7,8 +7,6 @@
 #' Calls [lightgbm::lightgbm()] from \CRANpkg{lightgbm}.
 #' The list of parameters can be found [here](https://lightgbm.readthedocs.io/en/latest/Parameters.html#)
 #' and in the documentation of [lightgbm::lgb.train()].
-#' Note that lightgbm models have to be saved using `lightgbm::lgb.save`, so you cannot simpliy
-#' save the learner using `saveRDS`. This will change in future versions of lightgbm.
 #'
 #' @template learner
 #' @templateVar id classif.lightgbm

R/learner_lightgbm_regr_lightgbm.R

@@ -7,8 +7,6 @@
 #' Calls [lightgbm::lightgbm()] from \CRANpkg{lightgbm}.
 #' The list of parameters can be found [here](https://lightgbm.readthedocs.io/en/latest/Parameters.html#)
 #' and in the documentation of [lightgbm::lgb.train()].
-#' Note that lightgbm models have to be saved using `lightgbm::lgb.save`, so you cannot simpliy
-#' save the learner using `saveRDS`. This will change in future versions of lightgbm.
 #'
 #' @template learner
 #' @templateVar id regr.lightgbm

R/learner_lme4_classif_glmer.R

@@ -18,7 +18,20 @@
 #' `r format_bib("bates2010lme4")`
 #'
 #' @template seealso_learner
-#' @template example
+#' @examplesIf requireNamespace("lme4", quietly = TRUE)
+#'
+#' # Define the Learner and set parameter values
+#' learner = lrn("classif.glmer",
+#'   formula = credit_risk ~ (1 | credit_history) + job + property + telephone + savings)
+#'
+#' # Define a Task
+#' task = tsk("german_credit")
+#' task$select(c("credit_history", "job", "property", "telephone", "savings"))
+#'
+#' # Train the learner
+#' learner$train(task)
+#'
+#' print(learner$model)
 #' @export
 LearnerClassifGlmer = R6Class("LearnerClassifGlmer",
   inherit = LearnerClassif,

R/learner_lme4_regr_lmer.R

@@ -15,7 +15,15 @@
 #' `r format_bib("bates2010lme4")`
 #'
 #' @template seealso_learner
-#' @template example
+#' @examplesIf requireNamespace("lme4", quietly = TRUE)
+#' # Define the Learner and set parameter values
+#' learner = lrn("regr.lmer", formula = cmedv ~ (1 | town))
+#'
+#' # Define a Task
+#' task = tsk("boston_housing")
+#'
+#' learner$train(task)
+#' print(learner$model)
 #' @export
 LearnerRegrLmer = R6Class("LearnerRegrLmer",
   inherit = LearnerRegr,

R/learner_mboost_regr_gamboost.R

@@ -14,7 +14,9 @@
 #'
 #' @export
 #' @template seealso_learner
-#' @template example
+#' @examples
+#' learner = lrn("regr.gamboost", baselearner = "bols")
+#' learner
 LearnerRegrGAMBoost = R6Class("LearnerRegrGAMBoost",
   inherit = LearnerRegr,
   public = list(

R/learner_mboost_surv_gamboost.R

@@ -17,7 +17,8 @@
 #'
 #' @export
 #' @template seealso_learner
-#' @template example
+#' @examples
+#' @template simple_example
 LearnerSurvGAMBoost = R6Class("LearnerSurvGAMBoost",
   inherit = mlr3proba::LearnerSurv,
   public = list(

R/learner_mboost_surv_mboost.R

@@ -17,7 +17,8 @@
 #'
 #' @export
 #' @template seealso_learner
-#' @template example
+#' @template simple_example
+#' @export
 LearnerSurvMBoost = R6Class("LearnerSurvMBoost",
   inherit = mlr3proba::LearnerSurv,
   public = list(

R/learner_mgcv_classif_gam.R

@@ -23,11 +23,12 @@
 #' `r format_bib("hastie2017generalized", "wood2012mgcv")`
 #'
 #' @examples
-#'
 #' # simple example
-#' t = mlr3::tsk("spam")
+#' t = mlr3::tsk("spam")$filter(1:1000)
 #' l = mlr3::lrn("classif.gam")
-#' l$param_set$values$formula = type ~ s(george) + s(charDollar) + s(edu) + ti(george, edu)
+#' l$param_set$set_values(
+#' formula = type ~ s(george, k = 3) + s(charDollar, k = 3) + s(edu)
+#' ')
 #' l$train(t)
 #' l$model
 #' @export