Pipelines to Imports refactoring

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3proba
 Title: Probabilistic Supervised Learning for 'mlr3'
-Version: 0.6.9
+Version: 0.7.0
 Authors@R:
     c(person(given = "Raphael",
              family = "Sonabend",
@@ -60,6 +60,7 @@ Imports:
     distr6 (>= 1.8.4),
     ggplot2,
     mlr3misc (>= 0.7.0),
+    mlr3pipelines (>= 0.7.0),
     mlr3viz,
     paradox (>= 1.0.0),
     R6,
@@ -71,7 +72,6 @@ Suggests:
     knitr,
     lgr,
     lifecycle,
-    mlr3pipelines (>= 0.3.4),
     param6 (>= 0.2.4),
     pracma,
     rpart,

NAMESPACE

@@ -30,7 +30,6 @@ S3method(pecs,list)
 S3method(plot,LearnerSurv)
 S3method(plot,TaskDens)
 S3method(plot,TaskSurv)
-export(.c_get_unique_times)
 export(.c_weight_survival_score)
 export(.surv_return)
 export(LearnerDens)
@@ -115,13 +114,18 @@ import(paradox)
 importFrom(R6,R6Class)
 importFrom(Rcpp,sourceCpp)
 importFrom(graphics,plot)
+importFrom(mlr3pipelines,"%>>%")
+importFrom(mlr3pipelines,Graph)
+importFrom(mlr3pipelines,as_graph)
+importFrom(mlr3pipelines,gunion)
+importFrom(mlr3pipelines,pipeline_greplicate)
+importFrom(mlr3pipelines,po)
+importFrom(mlr3pipelines,ppl)
 importFrom(mlr3viz,fortify)
-importFrom(stats,complete.cases)
 importFrom(stats,density)
 importFrom(stats,model.frame)
 importFrom(stats,model.matrix)
 importFrom(stats,predict)
-importFrom(stats,reformulate)
 importFrom(stats,sd)
 importFrom(survival,Surv)
 importFrom(utils,data)

NEWS.md

@@ -1,3 +1,10 @@
+# mlr3proba 0.7.0
+
+* Add `mlr3pipelines` to `Imports` and set minimum latest version from CRAN (`0.7.0`)
+* Refactor code to minimize namespace calling and imports such as `mlr3pipelines::` or `R6::`
+* Add experimental badge in the documentation of a few more PipeOps
+* Add argument `scale_lp` for AFT `distrcompose` pipeop + respective pipeline
+
 # mlr3proba 0.6.9
 
 * New `PipeOp`s: `PipeOpTaskSurvClassifIPCW`, `PipeOpPredClassifSurvIPCW`

R/LearnerDens.R

@@ -30,7 +30,7 @@
 #' # get a specific learner from mlr_learners:
 #' mlr_learners$get("dens.hist")
 #' lrn("dens.hist")
-LearnerDens = R6::R6Class("LearnerDens",
+LearnerDens = R6Class("LearnerDens",
   inherit = Learner,
   public = list(
     #' @description Creates a new instance of this [R6][R6::R6Class] class.

R/LearnerDensHistogram.R

@@ -4,7 +4,7 @@
 #' @templateVar caller [graphics::hist()]
 #'
 #' @export
-LearnerDensHistogram = R6::R6Class("LearnerDensHistogram",
+LearnerDensHistogram = R6Class("LearnerDensHistogram",
   inherit = LearnerDens,
   public = list(
     #' @description

R/LearnerDensKDE.R

@@ -11,7 +11,7 @@
 #' `r format_bib("silverman_1986")`
 #'
 #' @export
-LearnerDensKDE = R6::R6Class("LearnerDensKDE",
+LearnerDensKDE = R6Class("LearnerDensKDE",
   inherit = LearnerDens,
   public = list(
     #' @description

R/MeasureDensLogloss.R

@@ -15,7 +15,7 @@
 #'
 #' @family Density estimation measures
 #' @export
-MeasureDensLogloss = R6::R6Class("MeasureDensLogloss",
+MeasureDensLogloss = R6Class("MeasureDensLogloss",
   inherit = MeasureDens,
   public = list(
     #' @description

R/MeasureRegrLogloss.R

@@ -14,7 +14,7 @@
 #' \deqn{L(f, y) = -\log(f(y))}{L(f, y) = -log(f(y))}
 #'
 #' @export
-MeasureRegrLogloss = R6::R6Class("MeasureRegrLogloss",
+MeasureRegrLogloss = R6Class("MeasureRegrLogloss",
   inherit = MeasureRegr,
   public = list(
     #' @description

R/MeasureSurvGraf.R

@@ -53,7 +53,7 @@
 #' @family Probabilistic survival measures
 #' @family distr survival measures
 #' @export
-MeasureSurvGraf = R6::R6Class("MeasureSurvGraf",
+MeasureSurvGraf = R6Class("MeasureSurvGraf",
   inherit = MeasureSurv,
   public = list(
     #' @description

R/MeasureSurvIntLogloss.R

@@ -51,7 +51,7 @@
 #' @family Probabilistic survival measures
 #' @family distr survival measures
 #' @export
-MeasureSurvIntLogloss = R6::R6Class("MeasureSurvIntLogloss",
+MeasureSurvIntLogloss = R6Class("MeasureSurvIntLogloss",
   inherit = MeasureSurv,
   public = list(
     #' @description

R/MeasureSurvLogloss.R

@@ -36,7 +36,7 @@
 #' @family Probabilistic survival measures
 #' @family distr survival measures
 #' @export
-MeasureSurvLogloss = R6::R6Class("MeasureSurvLogloss",
+MeasureSurvLogloss = R6Class("MeasureSurvLogloss",
   inherit = MeasureSurv,
   public = list(
     #' @description

R/MeasureSurvMAE.R

@@ -15,7 +15,7 @@
 #'
 #' @family response survival measures
 #' @export
-MeasureSurvMAE = R6::R6Class("MeasureSurvMAE",
+MeasureSurvMAE = R6Class("MeasureSurvMAE",
   inherit = MeasureSurv,
   public = list(
     #' @description Creates a new instance of this [R6][R6::R6Class] class.

R/MeasureSurvMSE.R

@@ -15,7 +15,7 @@
 #'
 #' @family response survival measures
 #' @export
-MeasureSurvMSE = R6::R6Class("MeasureSurvMSE",
+MeasureSurvMSE = R6Class("MeasureSurvMSE",
   inherit = MeasureSurv,
   public = list(
     #' @description Creates a new instance of this [R6][R6::R6Class] class.

R/MeasureSurvRCLL.R

@@ -30,7 +30,7 @@
 #' @family Probabilistic survival measures
 #' @family distr survival measures
 #' @export
-MeasureSurvRCLL = R6::R6Class("MeasureSurvRCLL",
+MeasureSurvRCLL = R6Class("MeasureSurvRCLL",
   inherit = MeasureSurv,
   public = list(
     #' @description

R/MeasureSurvRMSE.R

@@ -15,7 +15,7 @@
 #'
 #' @family response survival measures
 #' @export
-MeasureSurvRMSE = R6::R6Class("MeasureSurvRMSE",
+MeasureSurvRMSE = R6Class("MeasureSurvRMSE",
   inherit = MeasureSurv,
   public = list(
     #' @description Creates a new instance of this [R6][R6::R6Class] class.

R/MeasureSurvSchmid.R

@@ -57,7 +57,7 @@
 #' @family Probabilistic survival measures
 #' @family distr survival measures
 #' @export
-MeasureSurvSchmid = R6::R6Class("MeasureSurvSchmid",
+MeasureSurvSchmid = R6Class("MeasureSurvSchmid",
   inherit = MeasureSurv,
   public = list(
     #' @description

R/PipeOpBreslow.R

@@ -41,9 +41,8 @@
 #' @seealso [pipeline_distrcompositor]
 #' @export
 #' @family survival compositors
-#' @examples
+#' @examplesIf mlr3misc::require_namespaces(c("mlr3pipelines"), quietly = TRUE)
 #' \dontrun{
-#' if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
 #'   library(mlr3)
 #'   library(mlr3pipelines)
 #'   task = tsk("rats")
@@ -57,7 +56,6 @@
 #'   b$train(list(train_task))
 #'   p = b$predict(list(test_task))[[1L]]
 #' }
-#' }
 PipeOpBreslow = R6Class("PipeOpBreslow",
   inherit = mlr3pipelines::PipeOp,
   public = list(

R/PipeOpCrankCompositor.R

@@ -35,10 +35,11 @@
 #'    If `TRUE`, then the `crank` will be overwritten.
 #'
 #' @seealso [pipeline_crankcompositor]
+#' @references
+#' `r format_bib("sonabend_2022", "ishwaran_2008")`
 #' @family survival compositors
-#' @examples
+#' @examplesIf mlr3misc::require_namespaces(c("mlr3pipelines"), quietly = TRUE)
 #' \dontrun{
-#' if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
 #'   library(mlr3pipelines)
 #'   task = tsk("rats")
 #'
@@ -47,7 +48,6 @@
 #'   poc = po("crankcompose", param_vals = list(overwrite = TRUE))
 #'   poc$predict(list(pred))[[1L]]
 #' }
-#' }
 #' @export
 PipeOpCrankCompositor = R6Class("PipeOpCrankCompositor",
   inherit = mlr3pipelines::PipeOp,

R/PipeOpDistrCompositor.R

@@ -3,6 +3,8 @@
 #' @template param_pipelines
 #'
 #' @description
+#' `r lifecycle::badge("experimental")`
+#'
 #' Estimates (or 'composes') a survival distribution from a predicted baseline
 #' survival distribution (`distr`) and a linear predictor (`lp`) from two [PredictionSurv]s.
 #'
@@ -46,21 +48,37 @@
 #'    nothing and returns the given [PredictionSurv]. If `TRUE`, then the `distr` is overwritten
 #'    with the `distr` composed from `lp` - this is useful for changing the prediction
 #'    `distr` from one model form to another.
+#' * `scale_lp` :: `logical(1)` \cr
+#'    This option is only applicable to `form` equal to `"aft"`. If `TRUE`, it
+#'    min-max scales the linear prediction scores to be in the interval \eqn{[0,1]},
+#'    avoiding extrapolation of the baseline \eqn{S_0(t)} on the transformed time
+#'    points \eqn{\frac{t}{\exp(lp)}}, as these will be \eqn{\in [\frac{t}{e}, t]},
+#'    and so always smaller than the maximum time point for which we have estimated
+#'    \eqn{S_0(t)}.
+#'    Note that this is just a **heuristic** to get reasonable results in the
+#'    case you observe survival predictions to be e.g. constant after the AFT
+#'    composition and it definitely provides no guarantee for creating calibrated
+#'    distribution predictions (as none of these methods do). Therefore, it is
+#'    set to `FALSE` by default.
 #'
 #' @section Internals:
 #' The respective `form`s above have respective survival distributions:
-#'    \deqn{aft: S(t) = S_0(\frac{t}{exp(lp)})}{aft: S(t) = S0(t/exp(lp))}
-#'    \deqn{ph: S(t) = S_0(t)^{exp(lp)}}{ph: S(t) = S0(t)^exp(lp)}
-#'    \deqn{po: S(t) = \frac{S_0(t)}{exp(-lp) + (1-exp(-lp)) S_0(t)}}{po: S(t) = S0(t) / [exp(-lp) + S0(t) (1-exp(-lp))]} # nolint
-#' where \eqn{S_0}{S0} is the estimated baseline survival distribution, and \eqn{lp} is the
+#'    \deqn{aft: S(t) = S_0(\frac{t}{\exp(lp)})}
+#'    \deqn{ph: S(t) = S_0(t)^{\exp(lp)}}
+#'    \deqn{po: S(t) = \frac{S_0(t)}{\exp(-lp) + (1-\exp(-lp)) S_0(t)}}
+#' where \eqn{S_0} is the estimated baseline survival distribution, and \eqn{lp} is the
 #' predicted linear predictor.
 #'
+#' For an example use of the `"aft"` composition using Kaplan-Meier as a baseline
+#' distribution, see Norman et al. (2024).
+#'
 #' @seealso [pipeline_distrcompositor]
+#' @references
+#' `r format_bib("norman_2024")`
 #' @export
 #' @family survival compositors
-#' @examples
+#' @examplesIf mlr3misc::require_namespaces(c("mlr3pipelines"), quietly = TRUE)
 #' \dontrun{
-#' if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
 #'   library(mlr3)
 #'   library(mlr3pipelines)
 #'   task = tsk("rats")
@@ -71,7 +89,6 @@
 #'   pod = po("distrcompose", param_vals = list(form = "aft", overwrite = TRUE))
 #'   pod$predict(list(base = base, pred = pred))[[1]]
 #' }
-#' }
 PipeOpDistrCompositor = R6Class("PipeOpDistrCompositor",
   inherit = mlr3pipelines::PipeOp,
   public = list(
@@ -80,9 +97,10 @@ PipeOpDistrCompositor = R6Class("PipeOpDistrCompositor",
     initialize = function(id = "distrcompose", param_vals = list()) {
       param_set = ps(
         form = p_fct(default = "aft", levels = c("aft", "ph", "po"), tags = "predict"),
-        overwrite = p_lgl(default = FALSE, tags = "predict")
+        overwrite = p_lgl(default = FALSE, tags = "predict"),
+        scale_lp = p_lgl(default = FALSE, tags = "predict")
       )
-      param_set$set_values(form = "aft", overwrite = FALSE)
+      param_set$set_values(form = "aft", overwrite = FALSE, scale_lp = FALSE)
 
       super$initialize(
         id = id,
@@ -145,6 +163,12 @@ PipeOpDistrCompositor = R6Class("PipeOpDistrCompositor",
         if (form == "ph") {
           cdf = 1 - (survmat ^ exp(lpmat))
         } else if (form == "aft") {
+          # add heuristic to keep the transformed t/exp(lp) time points within
+          # the domain of S_0(t)
+          if (self$param_set$values$scale_lp) {
+            lpmat = (lpmat - min(lpmat)) / (max(lpmat) - min(lpmat))
+          }
+          # calculate cdf = 1 - S_0(t) on the time points t/exp(lp)
           mtc = findInterval(timesmat / exp(lpmat), times)
           mtc[mtc == 0] = NA
           cdf = 1 - matrix(survmat[1L, mtc], nr, nc, FALSE)

R/PipeOpPredClassifSurvDiscTime.R

@@ -37,8 +37,7 @@
 #' @family PipeOps
 #' @family Transformation PipeOps
 #' @export
-PipeOpPredClassifSurvDiscTime = R6Class(
-  "PipeOpPredClassifSurvDiscTime",
+PipeOpPredClassifSurvDiscTime = R6Class("PipeOpPredClassifSurvDiscTime",
   inherit = mlr3pipelines::PipeOp,
 
   public = list(

R/PipeOpPredClassifSurvIPCW.R

@@ -39,8 +39,7 @@
 #' @family PipeOps
 #' @family Transformation PipeOps
 #' @export
-PipeOpPredClassifSurvIPCW = R6Class(
-  "PipeOpPredClassifSurvIPCW",
+PipeOpPredClassifSurvIPCW = R6Class("PipeOpPredClassifSurvIPCW",
   inherit = mlr3pipelines::PipeOp,
 
   public = list(

R/PipeOpPredRegrSurv.R

@@ -21,9 +21,8 @@
 #' If `NULL` then assumed no censoring in the dataset. Otherwise should be a vector of `0/1`s
 #' of same length as the prediction object, where `1` is dead and `0` censored.
 #'
-#' @examples
+#' @examplesIf mlr3misc::require_namespaces(c("mlr3pipelines"), quietly = TRUE)
 #' \dontrun{
-#' if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
 #'   library(mlr3)
 #'   library(mlr3pipelines)
 #'
@@ -45,7 +44,6 @@
 #'   new_pred = po$predict(list(pred = pred, task = task_surv))[[1]]
 #'   all.equal(new_pred$truth, task_surv$truth())
 #' }
-#' }
 #' @family PipeOps
 #' @family Transformation PipeOps
 #' @include PipeOpPredTransformer.R

R/PipeOpPredSurvRegr.R

@@ -14,10 +14,8 @@
 #' @section State:
 #' The `$state` is a named `list` with the `$state` elements inherited from [PipeOpPredTransformer].
 #'
-#'
-#' @examples
+#' @examplesIf mlr3misc::require_namespaces(c("mlr3pipelines"), quietly = TRUE)
 #' \dontrun{
-#' if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
 #'   library(mlr3)
 #'   library(mlr3pipelines)
 #'   library(survival)
@@ -29,7 +27,6 @@
 #'   new_pred = po$predict(list(pred = pred))[[1]]
 #'   print(new_pred)
 #' }
-#' }
 #' @family PipeOps
 #' @family Transformation PipeOps
 #' @include PipeOpPredTransformer.R

R/PipeOpProbregrCompositor.R

@@ -3,7 +3,9 @@
 #' @template param_pipelines
 #'
 #' @description
-#' Combines a predicted `reponse` and `se` from [PredictionRegr] with a specified probability
+#' `r lifecycle::badge("experimental")`
+#'
+#' Combines a predicted `response` and `se` from [PredictionRegr] with a specified probability
 #' distribution to estimate (or 'compose') a `distr` prediction.
 #'
 #' @section Dictionary:
@@ -38,10 +40,8 @@
 #' distribution location and scale parameters respectively.
 #'
 #' @export
-#' @examples
+#' @examplesIf mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)
 #' \dontrun{
-#' if (requireNamespace("mlr3pipelines", quietly = TRUE) &&
-#'   requireNamespace("rpart", quietly = TRUE)) {
 #'   library(mlr3)
 #'   library(mlr3pipelines)
 #'   set.seed(1)
@@ -61,7 +61,6 @@
 #'   poc = po("compose_probregr")
 #'   poc$predict(list(pred_response, pred_se))[[1]]
 #' }
-#' }
 PipeOpProbregr = R6Class("PipeOpProbregr",
   inherit = mlr3pipelines::PipeOp,
   public = list(