Extreme Gradient Boosting Survival Learner

eXtreme Gradient Boosting regression. Calls xgboost::xgb.train() from package xgboost.

Note

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

Custom mlr3 defaults

• nrounds:

  • Actual default: no default.

  • Adjusted default: 1.

  • Reason for change: Without a default construction of the learner would error. Just setting a nonsense default to workaround this. nrounds needs to be tuned by the user.

• nthread:

  • Actual value: Undefined, triggering auto-detection of the number of CPUs.

  • Adjusted value: 1.

  • Reason for change: Conflicting with parallelization via future.

• verbose:

  • Actual default: 1.

  • Adjusted default: 0.

  • Reason for change: Reduce verbosity.

• objective:

  • Actual default: reg:squarederror.

  • Adjusted default: survival:cox.

  • Reason for change: Changed to a survival objective.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.xgboost")
lrn("surv.xgboost")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “lp”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, xgboost

Parameters

Id	Type	Default	Levels	Range
aft_loss_distribution	character	normal	normal, logistic, extreme	-
aft_loss_distribution_scale	numeric	-		\((-\infty, \infty)\)
alpha	numeric	0		\([0, \infty)\)
base_score	numeric	0.5		\((-\infty, \infty)\)
booster	character	gbtree	gbtree, gblinear, dart	-
callbacks	untyped	list		-
colsample_bylevel	numeric	1		\([0, 1]\)
colsample_bynode	numeric	1		\([0, 1]\)
colsample_bytree	numeric	1		\([0, 1]\)
disable_default_eval_metric	logical	FALSE	TRUE, FALSE	-
early_stopping_rounds	integer	NULL		\([1, \infty)\)
eta	numeric	0.3		\([0, 1]\)
feature_selector	character	cyclic	cyclic, shuffle, random, greedy, thrifty	-
feval	untyped			-
gamma	numeric	0		\([0, \infty)\)
grow_policy	character	depthwise	depthwise, lossguide	-
interaction_constraints	untyped	-		-
iterationrange	untyped	-		-
lambda	numeric	1		\([0, \infty)\)
lambda_bias	numeric	0		\([0, \infty)\)
max_bin	integer	256		\([2, \infty)\)
max_delta_step	numeric	0		\([0, \infty)\)
max_depth	integer	6		\([0, \infty)\)
max_leaves	integer	0		\([0, \infty)\)
maximize	logical	NULL	TRUE, FALSE	-
min_child_weight	numeric	1		\([0, \infty)\)
missing	numeric	NA		\((-\infty, \infty)\)
monotone_constraints	integer	0		\([-1, 1]\)
normalize_type	character	tree	tree, forest	-
nrounds	integer	-		\([1, \infty)\)
nthread	integer	1		\([1, \infty)\)
ntreelimit	integer	-		\([1, \infty)\)
num_parallel_tree	integer	1		\([1, \infty)\)
objective	character	survival:cox	survival:cox, survival:aft	-
one_drop	logical	FALSE	TRUE, FALSE	-
predictor	character	cpu_predictor	cpu_predictor, gpu_predictor	-
print_every_n	integer	1		\([1, \infty)\)
process_type	character	default	default, update	-
rate_drop	numeric	0		\([0, 1]\)
refresh_leaf	logical	TRUE	TRUE, FALSE	-
sampling_method	character	uniform	uniform, gradient_based	-
sample_type	character	uniform	uniform, weighted	-
save_name	untyped	-		-
save_period	integer	-		\([0, \infty)\)
scale_pos_weight	numeric	1		\((-\infty, \infty)\)
seed_per_iteration	logical	FALSE	TRUE, FALSE	-
skip_drop	numeric	0		\([0, 1]\)
strict_shape	logical	FALSE	TRUE, FALSE	-
subsample	numeric	1		\([0, 1]\)
top_k	integer	0		\([0, \infty)\)
tree_method	character	auto	auto, exact, approx, hist, gpu_hist	-
tweedie_variance_power	numeric	1.5		\([1, 2]\)
updater	untyped	-		-
verbose	integer	1		\([0, 2]\)
watchlist	untyped			-
xgb_model	untyped	-		-

References

Chen, Tianqi, Guestrin, Carlos (2016). “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 785--794. ACM. doi:10.1145/2939672.2939785 .

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

be-marc

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvXgboost

Methods

Public methods

• LearnerSurvXgboost$new()

• LearnerSurvXgboost$importance()

• LearnerSurvXgboost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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

LearnerSurvXgboost$new()

---

Method importance()

The importance scores are calculated with xgboost::xgb.importance().

Usage

LearnerSurvXgboost$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvXgboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.xgboost")
print(learner)
#> <LearnerSurvXgboost:surv.xgboost>: Gradient Boosting
#> * Model: -
#> * Parameters: nrounds=1, nthread=1, verbose=0
#> * Packages: mlr3, mlr3proba, mlr3extralearners, xgboost
#> * Predict Types:  [crank], lp
#> * Feature Types: integer, numeric
#> * Properties: importance, missings, weights

# available parameters:
learner$param_set$ids()
#>  [1] "aft_loss_distribution"       "aft_loss_distribution_scale"
#>  [3] "alpha"                       "base_score"                 
#>  [5] "booster"                     "callbacks"                  
#>  [7] "colsample_bylevel"           "colsample_bynode"           
#>  [9] "colsample_bytree"            "disable_default_eval_metric"
#> [11] "early_stopping_rounds"       "eta"                        
#> [13] "feature_selector"            "feval"                      
#> [15] "gamma"                       "grow_policy"                
#> [17] "interaction_constraints"     "iterationrange"             
#> [19] "lambda"                      "lambda_bias"                
#> [21] "max_bin"                     "max_delta_step"             
#> [23] "max_depth"                   "max_leaves"                 
#> [25] "maximize"                    "min_child_weight"           
#> [27] "missing"                     "monotone_constraints"       
#> [29] "normalize_type"              "nrounds"                    
#> [31] "nthread"                     "ntreelimit"                 
#> [33] "num_parallel_tree"           "objective"                  
#> [35] "one_drop"                    "predictor"                  
#> [37] "print_every_n"               "process_type"               
#> [39] "rate_drop"                   "refresh_leaf"               
#> [41] "sampling_method"             "sample_type"                
#> [43] "save_name"                   "save_period"                
#> [45] "scale_pos_weight"            "seed_per_iteration"         
#> [47] "skip_drop"                   "strict_shape"               
#> [49] "subsample"                   "top_k"                      
#> [51] "tree_method"                 "tweedie_variance_power"     
#> [53] "updater"                     "verbose"                    
#> [55] "watchlist"                   "xgb_model"