mlr_learners_surv.deepsurv: Survival DeepSurv Learner

mlr_learners_surv.deepsurvR Documentation

Survival DeepSurv Learner

Description

DeepSurv fits a neural network based on the partial likelihood from a Cox PH. Calls survivalmodels::deepsurv() from package 'survivalmodels'.

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.deepsurv")

Meta Information

Parameters

Id Type Default Levels Range
frac numeric 0 [0, 1]
num_nodes untyped c(32L, 32L) -
batch_norm logical TRUE TRUE, FALSE -
dropout numeric - [0, 1]
activation character relu celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ... -
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)

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 -> LearnerSurvDeepsurv

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerSurvDeepsurv$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerSurvDeepsurv$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Katzman, L J, Shaham, Uri, Cloninger, Alexander, Bates, Jonathan, Jiang, Tingting, Kluger, Yuval (2018). “DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.” BMC medical research methodology, 18(1), 1–12.

See Also

Examples

lrn("surv.deepsurv")

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