Extreme Gradient Boosting Cox Survival Learner

eXtreme Gradient Boosting regression using a Cox Proportional Hazards objective. Calls xgboost::xgb.train() from package xgboost with objective set to survival:cox and eval_metric to cox-nloglik by default.

Details

Three types of prediction are returned for this learner:

1. lp: a vector of linear predictors (relative risk scores), one per observation.

2. crank: same as lp.

3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. By default, the Breslow estimator is used via mlr3proba::breslow().

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.

Initial parameter values

• nrounds is initialized to 1.

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

• verbose is initialized to 0.

Dictionary

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

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

Meta Information

• Task type: “surv”

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

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, xgboost

Parameters

Id	Type	Default	Levels	Range
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		\([1, \infty)\)
early_stopping_set	character	none	none, train, test	-
eta	numeric	0.3		\([0, 1]\)
feature_selector	character	cyclic	cyclic, shuffle, random, greedy, thrifty	-
feval	untyped	NULL		-
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		\([1, \infty)\)
num_parallel_tree	integer	1		\([1, \infty)\)
one_drop	logical	FALSE	TRUE, FALSE	-
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	NULL		-
xgb_model	untyped	-		-
device	untyped	-		-

Early stopping

Early stopping can be used to find the optimal number of boosting rounds. The early_stopping_set parameter controls which set is used to monitor the performance. By default, early_stopping_set = "none" which disables early stopping. Set early_stopping_set = "test" to monitor the performance of the model on the test set while training. The test set for early stopping can be set with the "test" row role in the mlr3::Task. Additionally, the range must be set in which the performance must increase with early_stopping_rounds and the maximum number of boosting rounds with nrounds. While resampling, the test set is automatically applied from the mlr3::Resampling. Not that using the test set for early stopping can potentially bias the performance scores.

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

bblodfon

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvXgboostCox

Methods

Public methods

• LearnerSurvXgboostCox$new()

• LearnerSurvXgboostCox$importance()

• LearnerSurvXgboostCox$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

LearnerSurvXgboostCox$new()

---

Method importance()

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

Usage

LearnerSurvXgboostCox$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvXgboostCox$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.xgboost.cox")
print(learner)
#> <LearnerSurvXgboostCox:surv.xgboost.cox>: Extreme Gradient Boosting Cox
#> * Model: -
#> * Parameters: nrounds=1, nthread=1, verbose=0, early_stopping_set=none
#> * Packages: mlr3, mlr3proba, mlr3extralearners, xgboost
#> * Predict Types:  [crank], distr, lp
#> * Feature Types: integer, numeric
#> * Properties: importance, missings, weights

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