Classification Priority Lasso Learner

Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso.

Dictionary

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

mlr_learners$get("classif.priority_lasso")
lrn("classif.priority_lasso")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, prioritylasso

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
type.measure	character	-	class, auc	-
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	-
handle.missingtestdata	character	-	none, omit.prediction, set.zero, impute.block	-
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		\((-\infty, \infty)\)
predict.gamma	numeric	gamma.1se		\((-\infty, \infty)\)
relax	logical	FALSE	TRUE, FALSE	-
s	numeric	lambda.1se		\([0, 1]\)
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		-

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 -> mlr3::LearnerClassif -> LearnerClassifPriorityLasso

Methods

Public methods

• LearnerClassifPriorityLasso$new()

• LearnerClassifPriorityLasso$selected_features()

• LearnerClassifPriorityLasso$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

LearnerClassifPriorityLasso$new()

---

Method selected_features()

Selected features, i.e. those where the coefficient is positive.

Usage

LearnerClassifPriorityLasso$selected_features()

Returns

character().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifPriorityLasso$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.priority_lasso")
print(learner)
#> <LearnerClassifPriorityLasso:classif.priority_lasso>: Priority Lasso
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, prioritylasso
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, twoclass, weights

# available parameters:
learner$param_set$ids()
#>  [1] "blocks"                 "type.measure"           "max.coef"              
#>  [4] "block1.penalization"    "lambda.type"            "standardize"           
#>  [7] "nfolds"                 "foldid"                 "cvoffset"              
#> [10] "cvoffsetnfolds"         "return.x"               "handle.missingtestdata"
#> [13] "include.allintercepts"  "use.blocks"             "alignment"             
#> [16] "alpha"                  "big"                    "devmax"                
#> [19] "dfmax"                  "eps"                    "epsnr"                 
#> [22] "exclude"                "exmx"                   "fdev"                  
#> [25] "gamma"                  "grouped"                "intercept"             
#> [28] "keep"                   "lambda"                 "lambda.min.ratio"      
#> [31] "lower.limits"           "maxit"                  "mnlam"                 
#> [34] "mxit"                   "mxitnr"                 "nlambda"               
#> [37] "offset"                 "parallel"               "penalty.factor"        
#> [40] "pmax"                   "pmin"                   "prec"                  
#> [43] "predict.gamma"          "relax"                  "s"                     
#> [46] "standardize.response"   "thresh"                 "trace.it"              
#> [49] "type.gaussian"          "type.logistic"          "type.multinomial"      
#> [52] "upper.limits"