Survival PC-Hazard Learner

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

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		\([0, 1]\)
cuts	integer	10		\([1, \infty)\)
cutpoints	list	-		\((-\infty, \infty)\)
scheme	character	equidistant	equidistant, quantiles	\((-\infty, \infty)\)
cut_min	numeric	0		\([0, \infty)\)
num_nodes	list	32, 32		\((-\infty, \infty)\)
batch_norm	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
reduction	character	mean	mean, none, sum	\((-\infty, \infty)\)
dropout	numeric	-		\([0, 1]\)
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	\((-\infty, \infty)\)
custom_net	list	-		\((-\infty, \infty)\)
device	list	-		\((-\infty, \infty)\)
shrink	numeric	0		\([0, \infty)\)
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	\((-\infty, \infty)\)
rho	numeric	0.9		\((-\infty, \infty)\)
eps	numeric	1e-08		\((-\infty, \infty)\)
lr	numeric	1		\((-\infty, \infty)\)
weight_decay	numeric	0		\((-\infty, \infty)\)
learning_rate	numeric	0.01		\((-\infty, \infty)\)
lr_decay	numeric	0		\((-\infty, \infty)\)
betas	list	0.900, 0.999		\((-\infty, \infty)\)
amsgrad	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
lambd	numeric	1e-04		\([0, \infty)\)
alpha	numeric	0.75		\([0, \infty)\)
t0	numeric	1e+06		\((-\infty, \infty)\)
momentum	numeric	0		\((-\infty, \infty)\)
centered	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
etas	list	0.5, 1.2		\((-\infty, \infty)\)
step_sizes	list	1e-06, 5e+01		\((-\infty, \infty)\)
dampening	numeric	0		\((-\infty, \infty)\)
nesterov	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
batch_size	integer	256		\((-\infty, \infty)\)
epochs	integer	1		\([1, \infty)\)
verbose	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
num_workers	integer	0		\((-\infty, \infty)\)
shuffle	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
best_weights	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
early_stopping	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
min_delta	numeric	0		\((-\infty, \infty)\)
patience	integer	10		\((-\infty, \infty)\)
interpolate	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
sub	integer	10		\([1, \infty)\)

References

Kvamme, H., & Borgan, Ø. (2019). Continuous and discrete-time survival prediction with neural networks. ArXiv Preprint ArXiv:1910.06724.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPCHazard

Methods

Public methods

• LearnerSurvPCHazard$new()

• LearnerSurvPCHazard$clone()

Inherited methods

• mlr3::Learner$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvPCHazard$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvPCHazard$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("survivalmodels", quietly = TRUE) && requireNamespace("distr6", quietly = TRUE) && requireNamespace("reticulate", quietly = TRUE)) {
  learner = mlr3::lrn("surv.pchazard")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerSurvPCHazard:surv.pchazard>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, survivalmodels, distr6, reticulate
#> * Predict Type: crank
#> * Feature types: integer, numeric
#> * Properties: -
#>  [1] "frac"           "cuts"           "cutpoints"      "scheme"        
#>  [5] "cut_min"        "num_nodes"      "batch_norm"     "reduction"     
#>  [9] "dropout"        "activation"     "custom_net"     "device"        
#> [13] "shrink"         "optimizer"      "rho"            "eps"           
#> [17] "lr"             "weight_decay"   "learning_rate"  "lr_decay"      
#> [21] "betas"          "amsgrad"        "lambd"          "alpha"         
#> [25] "t0"             "momentum"       "centered"       "etas"          
#> [29] "step_sizes"     "dampening"      "nesterov"       "batch_size"    
#> [33] "epochs"         "verbose"        "num_workers"    "shuffle"       
#> [37] "best_weights"   "early_stopping" "min_delta"      "patience"      
#> [41] "interpolate"    "sub"