mlr_learners_surv.deephit.Rd
Calls survivalmodels::deephit from package survivalmodels.
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
.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.deephit") lrn("surv.deephit")
Packages: survivalmodels, distr6, reticulate
Predict Types: crank, distr
Feature Types: integer, numeric
Properties:
Changhee Lee, William R Zame, Jinsung Yoon, and Mihaela van der Schaar. Deephit: A deep learning approach to survival analysis with competing risks. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvDeephit
new()
Creates a new instance of this R6 class.
LearnerSurvDeephit$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvDeephit$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.deephit")) print(learner)#> <LearnerSurvDeephit:surv.deephit> #> * Model: - #> * Parameters: list() #> * Packages: survivalmodels, distr6, reticulate #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -# available parameters: learner$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"