mlr_learners_surv.loghaz: Survival LogisticHazard Learner

In mlr3learners/mlr3learners.pycox: Adds Learners from Python Package pycox for mlr3

Description

A mlr3proba::LearnerSurv implementing LogisticHazard from Python package https://pypi.org/project/pycox/. Also known as nnet-survival.

Calls pycox.models.LogisticHazard.

Details

Custom nets can be used in this learner either using the build_pytorch_net utility function or using torch via reticulate. However note that the number of output channels depends on the number of discretised time-points, i.e. the parameters cuts or cutpoints.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.loghaz")
lrn("surv.loghaz")

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvLogisticHazard

Methods

Public methods

• LearnerSurvLogisticHazard$new()

• LearnerSurvLogisticHazard$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvLogisticHazard$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvLogisticHazard$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Gensheimer, M. F., & Narasimhan, B. (2018). A Simple Discrete-Time Survival Model for Neural Networks, 1–17. https://doi.org/arXiv:1805.00917v3

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

if (requireNamespace("mlr3learners.pycox")) {
  learner = mlr3::lrn("surv.loghaz")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr3learners/mlr3learners.pycox documentation built on Sept. 24, 2020, 10:40 a.m.