Survival Cox-Time Learner

Calls survivalmodels::coxtime 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.coxtime")
lrn("surv.coxtime")

Traits

• Packages: survivalmodels, distr6, reticulate

• Predict Types: crank, distr

• Feature Types: integer, numeric

• Properties:

References

Kvamme, H., Borgan, Ø., & Scheel, I. (2019). Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research, 20(129), 1–30.

See also

Dictionary of Learners: mlr3::mlr_learners

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxtime

Methods

Public methods

• LearnerSurvCoxtime$new()

• LearnerSurvCoxtime$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

LearnerSurvCoxtime$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

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

# available parameters:
learner$param_set$ids()
#>  [1] "frac"             "standardize_time" "log_duration"     "with_mean"       
#>  [5] "with_std"         "num_nodes"        "batch_norm"       "dropout"         
#>  [9] "activation"       "device"           "shrink"           "optimizer"       
#> [13] "rho"              "eps"              "lr"               "weight_decay"    
#> [17] "learning_rate"    "lr_decay"         "betas"            "amsgrad"         
#> [21] "lambd"            "alpha"            "t0"               "momentum"        
#> [25] "centered"         "etas"             "step_sizes"       "dampening"       
#> [29] "nesterov"         "batch_size"       "epochs"           "verbose"         
#> [33] "num_workers"      "shuffle"          "best_weights"     "early_stopping"  
#> [37] "min_delta"        "patience"