BlockForest Survival Learner
mlr_learners_surv.blockforest.Rd
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:
distr
: a survival matrix in two dimensions, where observations are represented in rows and (unique event) time points in columns. Calculated using the internalblockForest:::predict.blockForest()
function.crank
: the expected mortality usingmlr3proba::.surv_return()
.
Initial parameter values
num.threads
is initialized to 1 to avoid conflicts with parallelization via future.
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
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
-> LearnerSurvBlockForest
Methods
Method importance()
The importance scores are extracted from the model slot variable.importance
.
Returns
Named numeric()
.
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