set_validate.GraphLearner {mlr3pipelines}

Configure Validation for a GraphLearner

Description

Configure validation for a graph learner.

In a GraphLearner, validation can be configured on two levels:

1. On the GraphLearner level, which specifies how the validation set is constructed before entering the graph.

2. On the level of the individual PipeOps (such as PipeOpLearner), which specifies which pipeops actually make use of the validation data (set its ⁠$validate⁠ field to "predefined") or not (set it to NULL). This can be specified via the argument ids.

Usage

## S3 method for class 'GraphLearner'
set_validate(
  learner,
  validate,
  ids = NULL,
  args_all = list(),
  args = list(),
  ...
)

Arguments

learner

(GraphLearner)
The graph learner to configure.

validate

(numeric(1), "predefined", "test", or NULL)
How to set the ⁠$validate⁠ field of the learner. If set to NULL all validation is disabled, both on the graph learner level, but also for all pipeops.

ids

(NULL or character())
For which pipeops to enable validation. This parameter is ignored when validate is set to NULL. By default, validation is enabled for the final PipeOp in the Graph.

args_all

(list())
Rarely needed. A named list of parameter values that are passed to all subsequet set_validate() calls on the individual PipeOps.

args

(named list())
Rarely needed. A named list of lists, specifying additional argments to be passed to set_validate() when calling it on the individual PipeOps.

...

(any)
Currently unused.

Examples

library(mlr3)

glrn = as_learner(po("pca") %>>% lrn("classif.debug"))
set_validate(glrn, 0.3)
glrn$validate
glrn$graph$pipeops$classif.debug$learner$validate

set_validate(glrn, NULL)
glrn$validate
glrn$graph$pipeops$classif.debug$learner$validate

set_validate(glrn, 0.2, ids = "classif.debug")
glrn$validate
glrn$graph$pipeops$classif.debug$learner$validate