Details

Custom nets can be used in this learner either using the survivalmodels::build_keras_net utility function or using keras. The number of output channels should be of length 1 and number of input channels is the number of features plus number of cuts.

Dictionary

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

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

Traits

  • Packages: survivalmodels, keras, pseudo, tensorflow, distr6

  • Predict Types: crank, distr

  • Feature Types: integer, numeric

  • Properties:

Custom mlr3 defaults

  • verbose:

    • Actual default: 1L

    • Adjusted default: 0L

    • Reason for change: Prevents plotting.

References

Zhao, L., & Feng, D. (2020). DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values. https://arxiv.org/abs/1908.02337

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDNNSurv

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvDNNSurv$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvDNNSurv$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.dnnsurv")) print(learner)
#> <LearnerSurvDNNSurv:surv.dnnsurv> #> * Model: - #> * Parameters: verbose=0 #> * Packages: survivalmodels, keras, pseudo, tensorflow, distr6 #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "cuts" "cutpoints" "custom_model" #> [4] "optimizer" "lr" "beta_1" #> [7] "beta_2" "epsilon" "decay" #> [10] "clipnorm" "clipvalue" "schedule_decay" #> [13] "momentum" "nesterov" "loss_weights" #> [16] "weighted_metrics" "early_stopping" "min_delta" #> [19] "patience" "verbose" "baseline" #> [22] "restore_best_weights" "batch_size" "epochs" #> [25] "validation_split" "shuffle" "sample_weight" #> [28] "initial_epoch" "steps_per_epoch" "validation_steps" #> [31] "steps" "callbacks"