LearnerSurvRpartCox {mlsurvlrnrs}

LearnerSurvRpartCox R6 class

Description

This learner is a wrapper around rpart::rpart() in order to fit recursive partitioning and regression trees with survival data.

Details

Optimization metric: C-index * Can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Implemented methods:

• ⁠$fit⁠ To fit the model.

• ⁠$predict⁠ To predict new data with the model.

• ⁠$cross_validation⁠ To perform a grid search (hyperparameter optimization).

• ⁠$bayesian_scoring_function⁠ To perform a Bayesian hyperparameter optimization.

Parameters that are specified with parameter_grid and / or learner_args are forwarded to rpart's argument control (see rpart::rpart.control() for further details).

Super class

mlexperiments::MLLearnerBase -> LearnerSurvRpartCox

Methods

Public methods

• LearnerSurvRpartCox$new()

• LearnerSurvRpartCox$clone()

Inherited methods

• mlexperiments::MLLearnerBase$bayesian_scoring_function()
• mlexperiments::MLLearnerBase$cross_validation()
• mlexperiments::MLLearnerBase$fit()
• mlexperiments::MLLearnerBase$predict()

Method new()

Create a new LearnerSurvRpartCox object.

Usage

LearnerSurvRpartCox$new()

Details

This learner is a wrapper around rpart::rpart() in order to fit recursive partitioning and regression trees with survival data.

Examples

LearnerSurvRpartCox$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRpartCox$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

rpart::rpart(), c_index(), rpart::rpart.control()

rpart::rpart(), c_index(),

Examples

# survival analysis

dataset <- survival::colon |>
  data.table::as.data.table() |>
  na.omit()
dataset <- dataset[get("etype") == 2, ]

seed <- 123
surv_cols <- c("status", "time", "rx")

feature_cols <- colnames(dataset)[3:(ncol(dataset) - 1)]

ncores <- 2L

split_vector <- splitTools::multi_strata(
  df = dataset[, .SD, .SDcols = surv_cols],
  strategy = "kmeans",
  k = 4
)

train_x <- model.matrix(
  ~ -1 + .,
  dataset[, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])]
)
train_y <- survival::Surv(
  event = (dataset[, get("status")] |>
             as.character() |>
             as.integer()),
  time = dataset[, get("time")],
  type = "right"
)

fold_list <- splitTools::create_folds(
  y = split_vector,
  k = 3,
  type = "stratified",
  seed = seed
)

surv_rpart_optimizer <- mlexperiments::MLCrossValidation$new(
  learner = LearnerSurvRpartCox$new(),
  fold_list = fold_list,
  ncores = ncores,
  seed = seed
)
surv_rpart_optimizer$learner_args <- list(
  minsplit = 10L,
  maxdepth = 20L,
  cp = 0.03,
  method = "exp"
)
surv_rpart_optimizer$performance_metric <- c_index

# set data
surv_rpart_optimizer$set_data(
  x = train_x,
  y = train_y
)

surv_rpart_optimizer$execute()


## ------------------------------------------------
## Method `LearnerSurvRpartCox$new`
## ------------------------------------------------

LearnerSurvRpartCox$new()