Extreme Gradient Boosting AFT Survival Learner
mlr_learners_surv.xgboost.aft.RdeXtreme Gradient Boosting regression using an Accelerated Failure Time
objective.
Calls xgboost::xgb.train() from package xgboost with objective
set to survival:aft and eval_metric to aft-nloglik by default.
Details
This learner returns three prediction types:
response: the estimated survival time \(T\) for each test observation.lp: a vector of linear predictors (relative risk scores), one per observation, estimated as \(-log(T)\). Higher survival time denotes lower risk.crank: same aslp.
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
nroundsis initialized to 1.nthreadis initialized to 1 to avoid conflicts with parallelization via future.verboseis initialized to 0.
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.
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.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():
Meta Information
Task type: “surv”
Predict Types: “crank”, “lp”, “response”
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 | \([1, \infty)\) | |
| 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 | \([1, \infty)\) | |
| ntreelimit | integer | - | \([1, \infty)\) | |
| 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 | - | - |
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 .
Avinash B, Hyunsu C, Toby H (2022). “Survival Regression with Accelerated Failure Time Model in XGBoost.” Journal of Computational and Graphical Statistics. ISSN 15372715, doi:10.1080/10618600.2022.2067548 .
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 -> LearnerSurvXgboostAFT
Methods
Method importance()
The importance scores are calculated with xgboost::xgb.importance().
Returns
Named numeric().
Examples
learner = mlr3::lrn("surv.xgboost.aft")
print(learner)
#> <LearnerSurvXgboostAFT:surv.xgboost.aft>: Extreme Gradient Boosting AFT
#> * Model: -
#> * Parameters: nrounds=1, nthread=1, verbose=0, early_stopping_set=none
#> * Packages: mlr3, mlr3proba, mlr3extralearners, xgboost
#> * Predict Types: [crank], lp, response
#> * 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" "early_stopping_set"
#> [13] "eta" "feature_selector"
#> [15] "feval" "gamma"
#> [17] "grow_policy" "interaction_constraints"
#> [19] "iterationrange" "lambda"
#> [21] "lambda_bias" "max_bin"
#> [23] "max_delta_step" "max_depth"
#> [25] "max_leaves" "maximize"
#> [27] "min_child_weight" "missing"
#> [29] "monotone_constraints" "normalize_type"
#> [31] "nrounds" "nthread"
#> [33] "ntreelimit" "num_parallel_tree"
#> [35] "one_drop" "print_every_n"
#> [37] "process_type" "rate_drop"
#> [39] "refresh_leaf" "sampling_method"
#> [41] "sample_type" "save_name"
#> [43] "save_period" "scale_pos_weight"
#> [45] "seed_per_iteration" "skip_drop"
#> [47] "strict_shape" "subsample"
#> [49] "top_k" "tree_method"
#> [51] "tweedie_variance_power" "updater"
#> [53] "verbose" "watchlist"
#> [55] "xgb_model" "device"