RaphaelS1

set_seed(1234)

RaphaelS1

library(mlr3)
library(mlr3proba)

## get the `whas` task from mlr3proba
whas <- tsk("whas")

## create our own task from the rats dataset
rats_data <- survival::rats

## convert characters to factors

RaphaelS1

library(mlr3pipelines)

create_pipeops <- function(learner) {
 po("encode") %>>% po("scale") %>>% po("learner", learner)
}

## apply our function
learners <- lapply(learners, create_pipeops)

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library(survivalmodels)

install_pycox(pip = TRUE, install_torch = TRUE)
install_keras(pip = TRUE, install_tensorflow = TRUE)

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## load ggplot2 for autoplots
library(ggplot2)

## critical difference diagrams for IGS
autoplot(bma, meas = "graf", type = "cd", ratio = 1/3, p.value = 0.1)

RaphaelS1

library(mlr3tuning)

create_autotuner <- function(learner) {
 AutoTuner$new(
   learner = learner,
   search_space = search_space,
   resampling = rsmp("holdout"),
   measure = msr("surv.cindex"),
   terminator = trm("evals", n_evals = 2),
   tuner = tnr("random_search")

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library(paradox)

search_space <- ps(
 ## p_dbl for numeric valued parameters
 dropout = p_dbl(lower = 0, upper = 1),
 weight_decay = p_dbl(lower = 0, upper = 0.5),
 learning_rate = p_dbl(lower = 0, upper = 1),
 
 ## p_int for integer valued parameters
 nodes = p_int(lower = 1, upper = 32),

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## learners are stored in mlr3extralearners
library(mlr3extralearners)

## load learners
learners <- lrns(
 paste0("surv.", c("coxtime", "deephit", "deepsurv", "loghaz", "pchazard")),
 frac = 0.3, early_stopping = TRUE, epochs = 10, optimizer = "adam"
)
 
# apply our function

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library(mlr3benchmark)

## create mlr3benchmark object
bma <- as.BenchmarkAggr(bm)

## run global Friedman test
bma$friedman_test()

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`%=%` <- function(l, r) {
    l <- trimws(strsplit(l, ",", TRUE)[[1]])
    if (all(l == "USE.NAMES") || all(l == "*") || all(l == "?")) {
      stopifnot(length(names(r)) > 0)
      l <- names(r)
    } else {
      stopifnot(identical(length(l), length(r)))
      which <- l == "?"
      if (any(which)) {
        stopifnot(length(names(r)) > 0)