mlr_learners_surv.pchazard: Survival PC-Hazard Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival PC-Hazard Learner

Description

Logistic-Hazard fits a discrete neural network based on a cross-entropy loss and predictions of a discrete hazard function, also known as Nnet-Survival. 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.

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.pycox() function.

2. crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.pchazard")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		[0, 1]
cuts	integer	10		[1, \infty)
cutpoints	untyped	-		-
scheme	character	equidistant	equidistant, quantiles	-
cut_min	numeric	0		[0, \infty)
num_nodes	untyped	c(32L, 32L)		-
batch_norm	logical	TRUE	TRUE, FALSE	-
reduction	character	mean	mean, none, sum	-
dropout	numeric	-		[0, 1]
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	-
custom_net	untyped	-		-
device	untyped	-		-
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	-
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	untyped	c(0.9, 0.999)		-
amsgrad	logical	FALSE	TRUE, FALSE	-
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	-
etas	untyped	c(0.5, 1.2)		-
step_sizes	untyped	c(1e-06, 50)		-
dampening	numeric	0		(-\infty, \infty)
nesterov	logical	FALSE	TRUE, FALSE	-
batch_size	integer	256		(-\infty, \infty)
epochs	integer	1		[1, \infty)
verbose	logical	TRUE	TRUE, FALSE	-
num_workers	integer	0		(-\infty, \infty)
shuffle	logical	TRUE	TRUE, FALSE	-
best_weights	logical	FALSE	TRUE, FALSE	-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		(-\infty, \infty)
patience	integer	10		(-\infty, \infty)
interpolate	logical	FALSE	TRUE, FALSE	-
sub	integer	10		[1, \infty)

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

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

Methods

Public methods

• LearnerSurvPCHazard$new()

• LearnerSurvPCHazard$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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.

Author(s)

RaphaelS1

References

Kvamme, Håvard, 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.

Examples

lrn("surv.pchazard")

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.