Survival DeepSurv Learner

Calls survivalmodels::deepsurv from package survivalmodels.

Dictionary

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

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

Traits

• Packages: survivalmodels, distr6, reticulate

• Predict Types: crank, distr

• Feature Types: integer, numeric

• Properties:

References

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. https://doi.org/10.1186/s12874-018-0482-1

See also

Dictionary of Learners: mlr3::mlr_learners

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDeepsurv

Methods

Public methods

• LearnerSurvDeepsurv$new()

• LearnerSurvDeepsurv$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

LearnerSurvDeepsurv$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvDeepsurv$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.deepsurv"))
print(learner)
#> <LearnerSurvDeepsurv:surv.deepsurv>
#> * Model: -
#> * Parameters: list()
#> * Packages: survivalmodels, distr6, reticulate
#> * Predict Type: crank
#> * Feature types: integer, numeric
#> * Properties: -

# available parameters:
learner$param_set$ids()
#>  [1] "frac"           "num_nodes"      "batch_norm"     "dropout"       
#>  [5] "activation"     "device"         "shrink"         "optimizer"     
#>  [9] "rho"            "eps"            "lr"             "weight_decay"  
#> [13] "learning_rate"  "lr_decay"       "betas"          "amsgrad"       
#> [17] "lambd"          "alpha"          "t0"             "momentum"      
#> [21] "centered"       "etas"           "step_sizes"     "dampening"     
#> [25] "nesterov"       "batch_size"     "epochs"         "verbose"       
#> [29] "num_workers"    "shuffle"        "best_weights"   "early_stopping"
#> [33] "min_delta"      "patience"