mlr_learners_surv.aorsf: Accelerated Oblique Random Survival Forest Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Accelerated Oblique Random Survival Forest Learner

Description

Accelerated oblique random survival forest. Calls aorsf::orsf() from aorsf. Note that although the learner has the property "missing" and it can in principle deal with missing values, the behaviour has to be configured using the parameter na_action.

Initial parameter values

• n_thread: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

• mtry:

  • This hyperparameter can alternatively be set via the added hyperparameter mtry_ratio as mtry = max(ceiling(mtry_ratio * n_features), 1). Note that mtry and mtry_ratio are mutually exclusive.

Prediction types

This learner returns three 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 predict.ObliqueForest() function.

2. response: the restricted mean survival time of each test observation, derived from the survival matrix prediction (distr).

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

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.aorsf")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”, “response”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, aorsf, pracma

Parameters

Id	Type	Default	Levels	Range
n_tree	integer	500		[1, \infty)
n_split	integer	5		[1, \infty)
n_retry	integer	3		[0, \infty)
n_thread	integer	0		[0, \infty)
pred_aggregate	logical	TRUE	TRUE, FALSE	-
pred_simplify	logical	FALSE	TRUE, FALSE	-
oobag	logical	FALSE	TRUE, FALSE	-
mtry	integer	NULL		[1, \infty)
mtry_ratio	numeric	-		[0, 1]
sample_with_replacement	logical	TRUE	TRUE, FALSE	-
sample_fraction	numeric	0.632		[0, 1]
control_type	character	fast	fast, cph, net	-
split_rule	character	logrank	logrank, cstat	-
control_fast_do_scale	logical	FALSE	TRUE, FALSE	-
control_fast_ties	character	efron	efron, breslow	-
control_cph_ties	character	efron	efron, breslow	-
control_cph_eps	numeric	1e-09		[0, \infty)
control_cph_iter_max	integer	20		[1, \infty)
control_net_alpha	numeric	0.5		(-\infty, \infty)
control_net_df_target	integer	NULL		[1, \infty)
leaf_min_events	integer	1		[1, \infty)
leaf_min_obs	integer	5		[1, \infty)
split_min_events	integer	5		[1, \infty)
split_min_obs	integer	10		[1, \infty)
split_min_stat	numeric	NULL		[0, \infty)
oobag_pred_type	character	risk	none, surv, risk, chf, mort	-
importance	character	anova	none, anova, negate, permute	-
importance_max_pvalue	numeric	0.01		[1e-04, 0.9999]
tree_seeds	integer	NULL		[1, \infty)
oobag_pred_horizon	numeric	NULL		[0, \infty)
oobag_eval_every	integer	NULL		[1, \infty)
oobag_fun	untyped	NULL		-
attach_data	logical	TRUE	TRUE, FALSE	-
verbose_progress	logical	FALSE	TRUE, FALSE	-
na_action	character	fail	fail, omit, impute_meanmode	-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvAorsf

Methods

Public methods

• LearnerSurvAorsf$new()

• LearnerSurvAorsf$oob_error()

• LearnerSurvAorsf$importance()

• LearnerSurvAorsf$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvAorsf$new()

---

Method oob_error()

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerSurvAorsf$oob_error()

Returns

numeric().

---

Method importance()

The importance scores are extracted from the model.

Usage

LearnerSurvAorsf$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvAorsf$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

bcjaeger

References

Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min Y, Mcclure LA, Howard G, Simon N (2019). “Oblique random survival forests.” The Annals of Applied Statistics, 13(3). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/19-aoas1261")}.

Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM (2023). “Accelerated and interpretable oblique random survival forests.” Journal of Computational and Graphical Statistics, 1–16. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2023.2231048")}.

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.

Examples

# Define the Learner
learner = mlr3::lrn("surv.aorsf")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.