Survival Priority Lasso Learner

Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso. Many parameters for this survival learner are the same as mlr_learners_surv.cv_glmnet as prioritylasso() calls glmnet::cv.glmnet() during training phase. Note that prioritylasso() has ways to deal with block-wise missing data, but this feature is not supported currently.

Prediction types

This learner returns three prediction types:

1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using prioritylasso::predict.prioritylasso().

2. crank: same as lp.

3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mlr3proba::breslow() where the Breslow estimator is used for computing the baseline hazard.

Initial parameter values

• family is set to "cox" for the Cox survival objective and cannot be changed

• type.measure set to "deviance" (cross-validation measure)

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.priority_lasso")

Meta Information

• Task type: “surv”

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

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, prioritylasso

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
max.coef	untyped	NULL		-
block1.penalization	logical	TRUE	TRUE, FALSE	-
lambda.type	character	lambda.min	lambda.min, lambda.1se	-
standardize	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		\([1, \infty)\)
foldid	untyped	NULL		-
cvoffset	logical	FALSE	TRUE, FALSE	-
cvoffsetnfolds	integer	10		\([1, \infty)\)
return.x	logical	TRUE	TRUE, FALSE	-
include.allintercepts	logical	FALSE	TRUE, FALSE	-
use.blocks	untyped	"all"		-
alignment	character	lambda	lambda, fraction	-
alpha	numeric	1		\([0, 1]\)
big	numeric	9.9e+35		\((-\infty, \infty)\)
devmax	numeric	0.999		\([0, 1]\)
dfmax	integer	-		\([0, \infty)\)
eps	numeric	1e-06		\([0, 1]\)
epsnr	numeric	1e-08		\([0, 1]\)
exclude	untyped	-		-
exmx	numeric	250		\((-\infty, \infty)\)
fdev	numeric	1e-05		\([0, 1]\)
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		\([0, 1]\)
lower.limits	untyped	-Inf		-
maxit	integer	100000		\([1, \infty)\)
mnlam	integer	5		\([1, \infty)\)
mxit	integer	100		\([1, \infty)\)
mxitnr	integer	25		\([1, \infty)\)
nlambda	integer	100		\([1, \infty)\)
offset	untyped	NULL		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
pmax	integer	-		\([0, \infty)\)
pmin	numeric	1e-09		\([0, 1]\)
prec	numeric	1e-10		\((-\infty, \infty)\)
standardize.response	logical	FALSE	TRUE, FALSE	-
thresh	numeric	1e-07		\([0, \infty)\)
trace.it	integer	0		\([0, 1]\)
type.gaussian	character	-	covariance, naive	-
type.logistic	character	Newton	Newton, modified.Newton	-
type.multinomial	character	ungrouped	ungrouped, grouped	-
upper.limits	untyped	Inf		-
relax	logical	FALSE	TRUE, FALSE	-

References

Simon K, Vindi J, Roman H, Tobias H, Anne-Laure B (2018). “Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.” BMC Bioinformatics, 19. doi:10.1186/s12859-018-2344-6 .

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

HarutyunyanLiana

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPriorityLasso

Methods

Public methods

• LearnerSurvPriorityLasso$new()

• LearnerSurvPriorityLasso$selected_features()

• LearnerSurvPriorityLasso$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

LearnerSurvPriorityLasso$new()

---

Method selected_features()

Selected features, i.e. those where the coefficient is non-zero.

Usage

LearnerSurvPriorityLasso$selected_features()

Returns

character().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvPriorityLasso$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define a Task
task = tsk("grace")
# Create train and test set
ids = partition(task)
# check task's features
task$feature_names
#> [1] "age"        "los"        "revasc"     "revascdays" "stchange"  
#> [6] "sysbp"     
# partition features to 2 blocks
blocks = list(bl1 = 1:3, bl2 = 4:6)
# define learner
learner = lrn("surv.priority_lasso", blocks = blocks, block1.penalization = FALSE,
              lambda.type = "lambda.1se", standardize = TRUE, nfolds = 5)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# selected features
learner$selected_features()
#> [1] "age"        "los"        "revasc"     "revascdays"
# Make predictions for the test observations
pred = learner$predict(task, row_ids = ids$test)
pred
#> <PredictionSurv> for 330 observations:
#>  row_ids time status     crank        lp     distr
#>        1  180  FALSE 0.4155903 0.4155903 <list[1]>
#>        5  180  FALSE 0.9555264 0.9555264 <list[1]>
#>        7  180  FALSE 0.7505083 0.7505083 <list[1]>
#>      ---  ---    ---       ---       ---       ---
#>      998  180  FALSE 3.3460276 3.3460276 <list[1]>
#>      999    3   TRUE 3.7532025 3.7532025 <list[1]>
#>     1000   15  FALSE 3.7172993 3.7172993 <list[1]>
# Score the predictions
pred$score()
#> surv.cindex 
#>   0.7546127