Details

Custom nets can be used in this learner either using the survivalmodels::build_pytorch_net utility function or using torch via reticulate. 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.deephit")
lrn("surv.deephit")

Traits

  • Packages: survivalmodels, distr6, reticulate

  • Predict Types: crank, distr

  • Feature Types: integer, numeric

  • Properties:

References

Changhee Lee, William R Zame, Jinsung Yoon, and Mihaela van der Schaar. Deephit: A deep learning approach to survival analysis with competing risks. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDeephit

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvDeephit$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvDeephit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.deephit")) print(learner)
#> <LearnerSurvDeephit:surv.deephit> #> * Model: - #> * Parameters: list() #> * Packages: survivalmodels, distr6, reticulate #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "frac" "cuts" "cutpoints" "scheme" #> [5] "cut_min" "num_nodes" "batch_norm" "dropout" #> [9] "activation" "custom_net" "device" "mod_alpha" #> [13] "sigma" "shrink" "optimizer" "rho" #> [17] "eps" "lr" "weight_decay" "learning_rate" #> [21] "lr_decay" "betas" "amsgrad" "lambd" #> [25] "alpha" "t0" "momentum" "centered" #> [29] "etas" "step_sizes" "dampening" "nesterov" #> [33] "batch_size" "epochs" "verbose" "num_workers" #> [37] "shuffle" "best_weights" "early_stopping" "min_delta" #> [41] "patience" "interpolate" "inter_scheme" "sub"