mlr_learners_surv.coxtime | R Documentation |
Cox-Time survival model.
Calls survivalmodels::coxtime()
from package 'survivalmodels'.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.coxtime") lrn("surv.coxtime")
Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate
Id | Type | Default | Levels | Range |
frac | numeric | 0 | [0, 1] |
|
standardize_time | logical | FALSE | TRUE, FALSE | - |
log_duration | logical | FALSE | TRUE, FALSE | - |
with_mean | logical | TRUE | TRUE, FALSE | - |
with_std | logical | TRUE | TRUE, FALSE | - |
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 | - | - | |
shrink | numeric | 0 | [0, \infty) |
|
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) |
|
Package 'survivalmodels' is not on CRAN and has to be install from GitHub via
remotes::install_github("RaphaelS1/survivalmodels")
.
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCoxtime
new()
Creates a new instance of this R6 class.
LearnerSurvCoxtime$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvCoxtime$clone(deep = FALSE)
deep
Whether to make a deep clone.
RaphaelS1
Kvamme, Håvard, Borgan Ø, Scheel I (2019). “Time-to-event prediction with neural networks and Cox regression.” arXiv preprint arXiv:1907.00825.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("surv.coxtime")
print(learner)
# available parameters:
learner$param_set$ids()
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