BlockForest Survival Learner

Random survival forests for blocks of clinical and omics covariate data. Calls blockForest::blockfor() from package blockForest.

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and (unique event) time points in columns. Calculated using the internal blockForest:::predict.blockForest() function.

2. crank: the expected mortality using mlr3proba::.surv_return().

Initial parameter values

• num.threads is initialized to 1 to avoid conflicts with parallelization via future.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.blockforest")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, blockForest

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
block.method	character	BlockForest	BlockForest, RandomBlock, BlockVarSel, VarProb, SplitWeights	-
num.trees	integer	2000		\([1, \infty)\)
mtry	untyped	NULL		-
nsets	integer	300		\([1, \infty)\)
num.trees.pre	integer	1500		\([1, \infty)\)
splitrule	character	extratrees	logrank, extratrees, C, maxstat	-
always.select.block	integer	0		\([0, 1]\)
importance	character	-	none, impurity, impurity_corrected, permutation	-
num.threads	integer	1		\([1, \infty)\)
seed	integer	NULL		\((-\infty, \infty)\)
verbose	logical	TRUE	TRUE, FALSE	-

References

Hornung, R., Wright, N. M (2019). “Block Forests: Random forests for blocks of clinical and omics covariate data.” BMC Bioinformatics, 20(1), 1–17. doi:10.1186/s12859-019-2942-y , https://doi.org/10.1186/s12859-019-2942-y.

See also

• 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.

Author

bblodfon

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvBlockForest

Methods

Public methods

• LearnerSurvBlockForest$new()

• LearnerSurvBlockForest$importance()

• LearnerSurvBlockForest$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvBlockForest$new()

---

Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvBlockForest$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvBlockForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define a Task
task = tsk("grace")
# Create train and test set
ids = partition(task)
# check task's features
task$feature_names
#> [1] "age"        "los"        "revasc"     "revascdays" "stchange"  
#> [6] "sysbp"     
# partition features to 2 blocks
blocks = list(bl1 = 1:3, bl2 = 4:6)
# define learner
learner = lrn("surv.blockforest", blocks = blocks,
              importance = "permutation", nsets = 10,
              num.trees = 50, num.trees.pre = 10, splitrule = "logrank")
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# feature importance
learner$importance()
#>  revascdays      revasc         age         los       sysbp    stchange 
#> 0.113379414 0.066572334 0.023393605 0.011948626 0.008995241 0.008562334 
# Make predictions for the test observations
pred = learner$predict(task, row_ids = ids$test)
pred
#> 
#> ── <PredictionSurv> for 330 observations: ──────────────────────────────────────
#>  row_ids time status    crank     distr
#>        5  180  FALSE 16.80202 <list[1]>
#>        8    2  FALSE 28.07705 <list[1]>
#>       12  180  FALSE 14.59938 <list[1]>
#>      ---  ---    ---      ---       ---
#>      993   53   TRUE 85.50580 <list[1]>
#>      998  180  FALSE 32.69874 <list[1]>
#>     1000   15  FALSE 61.66238 <list[1]>
# Score the predictions
pred$score()
#> surv.cindex 
#>   0.8338479