mlr_pipeops_crankcompose: PipeOpCrankCompositor

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

PipeOpCrankCompositor

Description

Uses a predicted distr in a PredictionSurv to estimate (or 'compose') a crank prediction.

Dictionary

This PipeOp can be instantiated via the dictionary mlr3pipelines::mlr_pipeops or with the associated sugar function mlr3pipelines::po():

PipeOpCrankCompositor$new()
mlr_pipeops$get("crankcompose")
po("crankcompose")

Input and Output Channels

PipeOpCrankCompositor has one input channel named "input", which takes NULL during training and PredictionSurv during prediction.

PipeOpCrankCompositor has one output channel named "output", producing NULL during training and a PredictionSurv during prediction.

The output during prediction is the PredictionSurv from the input but with the crank predict type overwritten by the given estimation method.

State

The ⁠$state⁠ is left empty (list()).

Parameters

• method :: character(1)
  Determines what method should be used to produce a continuous ranking from the distribution. Currently only mort is supported, which is the sum of the cumulative hazard, also called expected/ensemble mortality, see Ishwaran et al. (2008). For more details, see get_mortality().

• overwrite :: logical(1)
  If FALSE (default) and the prediction already has a crank prediction, then the compositor returns the input prediction unchanged. If TRUE, then the crank will be overwritten.

Super class

mlr3pipelines::PipeOp -> PipeOpCrankCompositor

Methods

Public methods

• PipeOpCrankCompositor$new()

• PipeOpCrankCompositor$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpCrankCompositor$new(id = "crankcompose", param_vals = list())

Arguments

id

(character(1))
Identifier of the resulting object.

param_vals

(list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpCrankCompositor$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Sonabend, Raphael, Bender, Andreas, Vollmer, Sebastian (2022). “Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.” Bioinformatics. ISSN 1367-4803, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/BIOINFORMATICS/BTAC451")}, https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac451/6640155.

Ishwaran, Hemant, Kogalur, B U, Blackstone, H E, Lauer, S M, others (2008). “Random survival forests.” The Annals of applied statistics, 2(3), 841–860.

See Also

pipeline_crankcompositor

Other survival compositors: mlr_pipeops_compose_breslow_distr, mlr_pipeops_distrcompose, mlr_pipeops_responsecompose

Examples

## Not run: 
  library(mlr3pipelines)
  task = tsk("rats")

  # change the crank prediction type of a Cox's model predictions
  pred = lrn("surv.coxph")$train(task)$predict(task)
  poc = po("crankcompose", param_vals = list(overwrite = TRUE))
  poc$predict(list(pred))[[1L]]

## End(Not run)

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.