Survival DeepHit Learner

Calls survivalmodels::deephit from package survivalmodels.

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

Dictionary of Learners: mlr3::mlr_learners

Author

RaphaelS1

Super classes

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

Methods

Public methods

• LearnerSurvDeephit$new()

• LearnerSurvDeephit$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

LearnerSurvDeephit$new()

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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"