LearnerSurvXgboostAft {mlsurvlrnrs}

R6 Class to construct a Xgboost survival learner for accelerated failure time models

Description

The 'LearnerSurvXgboostAft' class is the interface to accelerated failure time models with the 'xgboost' R package for use with the 'mlexperiments' package.

Details

Optimization metric: needs to be specified with the learner parameter 'eval_metric'. Can be used with * [mlexperiments::MLTuneParameters] * [mlexperiments::MLCrossValidation] * [mlexperiments::MLNestedCVs] Also see the official xgboost documentation on [aft models](https://xgboost.readthedocs.io/en/stable/tutorials/aft_ survival_analysis.html)

Super classes

mlexperiments::MLLearnerBase -> mllrnrs::LearnerXgboost -> LearnerSurvXgboostAft

Methods

Public methods

• LearnerSurvXgboostAft$new()

• LearnerSurvXgboostAft$clone()

Inherited methods

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

Method new()

Create a new 'LearnerSurvXgboostAft' object.

Usage

LearnerSurvXgboostAft$new(metric_optimization_higher_better)

Arguments

metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

Returns

A new 'LearnerSurvXgboostAft' R6 object.

Examples

LearnerSurvXgboostAft$new(metric_optimization_higher_better = FALSE)

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvXgboostAft$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

[xgboost::xgb.train()], [xgboost::xgb.cv()]

Examples

# execution time >2.5 sec
# 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)]

param_list_xgboost <- expand.grid(
  objective = "survival:aft",
  eval_metric = "aft-nloglik",
  subsample = seq(0.6, 1, .2),
  colsample_bytree = seq(0.6, 1, .2),
  min_child_weight = seq(1, 5, 4),
  learning_rate = c(0.1, 0.2),
  max_depth = seq(1, 5, 4)
)
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_xgboost_aft_optimizer <- mlexperiments::MLCrossValidation$new(
  learner = LearnerSurvXgboostAft$new(
    metric_optimization_higher_better = FALSE
  ),
  fold_list = fold_list,
  ncores = ncores,
  seed = seed
)
surv_xgboost_aft_optimizer$learner_args <- c(as.list(
  data.table::data.table(param_list_xgboost[1, ], stringsAsFactors = FALSE)
),
nrounds = 45L
)
surv_xgboost_aft_optimizer$performance_metric <- c_index

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

surv_xgboost_aft_optimizer$execute()



## ------------------------------------------------
## Method `LearnerSurvXgboostAft$new`
## ------------------------------------------------

LearnerSurvXgboostAft$new(metric_optimization_higher_better = FALSE)