Survival Cox-Time Learner
mlr_learners_surv.coxtime.Rd
Cox-Time survival model.
Calls survivalmodels::coxtime()
from package 'survivalmodels'.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("surv.coxtime")
mlr_learnerslrn("surv.coxtime")
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 | \([0, 1]\) | |
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 , 32, 32 | - | |
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 | \((-\infty, \infty)\) |
Installation
Package 'survivalmodels' is not on CRAN and has to be install from GitHub via
remotes::install_github("RaphaelS1/survivalmodels")
.
References
Kvamme, Håvard, Borgan Ø, Scheel I (2019). “Time-to-event prediction with neural networks and Cox regression.” arXiv preprint arXiv:1907.00825.
See also
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.
Super classes
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCoxtime
Examples
learner = mlr3::lrn("surv.coxtime")
print(learner)
#> <LearnerSurvCoxtime:surv.coxtime>: Cox-Time Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6,
#> reticulate
#> * Predict Types: [crank], distr
#> * Feature Types: integer, numeric
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "frac" "standardize_time" "log_duration" "with_mean"
#> [5] "with_std" "num_nodes" "batch_norm" "dropout"
#> [9] "activation" "device" "shrink" "optimizer"
#> [13] "rho" "eps" "lr" "weight_decay"
#> [17] "learning_rate" "lr_decay" "betas" "amsgrad"
#> [21] "lambd" "alpha" "t0" "momentum"
#> [25] "centered" "etas" "step_sizes" "dampening"
#> [29] "nesterov" "batch_size" "epochs" "verbose"
#> [33] "num_workers" "shuffle" "best_weights" "early_stopping"
#> [37] "min_delta" "patience"