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This function allows to construct or convert to a ResultData object, the result container used by ResampleResult and BenchmarkResult. A ResampleResult or BenchmarkResult can be initialized with the returned object. Note that ResampleResults can be converted to a BenchmarkResult with as_benchmark_result() and multiple BenchmarkResults can be combined to a larger BenchmarkResult with the $combine() method of BenchmarkResult.

Usage

as_result_data(
  task,
  learners,
  resampling,
  iterations,
  predictions,
  learner_states = NULL,
  store_backends = TRUE
)

Arguments

task

(Task).

learners

(list of trained Learners).

resampling

(Resampling).

iterations

(integer()).

predictions

(list of list of Predictions).

learner_states

(list())
Learner states. If not provided, the states of learners are automatically extracted.

store_backends

(logical(1))
If set to FALSE, the backends of the Tasks provided in data are removed.

Value

ResultData object which can be passed to the constructor of ResampleResult.

Examples

task = tsk("penguins")
learner = lrn("classif.rpart")
resampling = rsmp("cv", folds = 2)$instantiate(task)
iterations = seq_len(resampling$iters)

# manually train two learners.
# store learners and predictions
learners = list()
predictions = list()
for (i in iterations) {
  l = learner$clone(deep = TRUE)
  learners[[i]] = l$train(task, row_ids = resampling$train_set(i))
  predictions[[i]] = list(test = l$predict(task, row_ids = resampling$test_set(i)))
}

rdata = as_result_data(task, learners, resampling, iterations, predictions)
ResampleResult$new(rdata)
#> <ResampleResult> with 2 resampling iterations
#>   task_id    learner_id resampling_id iteration warnings errors
#>  penguins classif.rpart            cv         1        0      0
#>  penguins classif.rpart            cv         2        0      0