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Create Evaluation Callback

Source: R/CallbackResample.R
callback_resample.Rd

Function to create a CallbackResample. Predefined callbacks are stored in the dictionary mlr_callbacks and can be retrieved with clbk().

Evaluation callbacks are called at different stages of the resampling process. Each stage is called once per resampling iteration. The stages are prefixed with on_resample_*. The text in brackets indicates what happens between the stages in the internal workhorse() function and which accesses to the ContextResample (ctx) are typical for the stage.

Start Resampling Iteration on Worker
 - on_resample_begin
   (Split `ctx$task` into training and test set with `ctx$resampling` and `ctx$iteration`)
 - on_resample_before_train
   (Train the learner `ctx$learner` on training data)
 - on_resample_before_predict
   (Predict on predict sets and store prediction data `ctx$pdatas`)
 - on_resample_end
   (Erase model `ctx$learner$model` if requested and return results)
End Resampling Iteration on Worker

The callback can store data in ctx$learner$state or ctx$data_extra. The data in ctx$data_extra is stored in the ResampleResult or BenchmarkResult. See also the section on parameters for more information on the stages.

Usage

callback_resample(
  id,
  label = NA_character_,
  man = NA_character_,
  on_resample_begin = NULL,
  on_resample_before_train = NULL,
  on_resample_before_predict = NULL,
  on_resample_end = NULL
)

Arguments

id

(character(1))
Identifier for the new instance.

label

(character(1))
Label for the new instance.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().

on_resample_begin

(function())
Stage called at the beginning of an evaluation. Called in workhorse() (internal).

on_resample_before_train

(function())
Stage called before training the learner. Called in workhorse() (internal).

on_resample_before_predict

(function())
Stage called before predicting. Called in workhorse() (internal).

on_resample_end

(function())
Stage called at the end of an evaluation. Called in workhorse() (internal).

Details

When implementing a callback, each function must have two arguments named callback and context. A callback can write data to the state ($state), e.g. settings that affect the callback itself. We highly discourage changing the task, learner and resampling objects via the callback.

Examples

learner = lrn("classif.rpart")
task = tsk("pima")
resampling = rsmp("cv", folds = 3)

# save selected features callback
callback = callback_resample("selected_features",
 on_resample_end = function(callback, context) {
    context$learner$state$selected_features = context$learner$selected_features()
  }
)

rr = resample(task, learner, resampling, callbacks = callback)
rr$learners[[1]]$state$selected_features
#> [1] "glucose"  "mass"     "age"      "pressure" "insulin"  "pregnant"

# holdout task callback
callback = callback_resample("holdout_task",
  on_resample_before_predict = function(callback, context) {
    pred = context$learner$predict(callback$state$task)
    context$data_extra = list(prediction_holdout = pred)
  }
)

task_holdout = tsk("pima")
splits = partition(task, 0.7)
task$filter(splits$train)
task_holdout$filter(splits$test)

callback$state$task = task_holdout

rr = resample(task, learner, resampling, callbacks = callback)
rr$data_extra
#> Key: <uhash, iteration>
#>                                   uhash iteration data_extra
#>                                  <char>     <int>     <list>
#> 1: bd192672-4672-4f14-ab21-ac8f719833c6         1  <list[1]>
#> 2: bd192672-4672-4f14-ab21-ac8f719833c6         2  <list[1]>
#> 3: bd192672-4672-4f14-ab21-ac8f719833c6         3  <list[1]>