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
$combine()
method of BenchmarkResult.
Usage
as_result_data(
task,
learners,
resampling,
iterations,
predictions,
learner_states = NULL,
store_backends = TRUE
)
Arguments
- task
(Task).
- learners
(list of trained Learners).
- resampling
(Resampling).
- iterations
(
integer()
).- predictions
(list of list of Predictions).
- learner_states
(
list()
)
Learner states. If not provided, the states oflearners
are automatically extracted.- store_backends
(
logical(1)
)
If set toFALSE
, the backends of the Tasks provided indata
are removed.
Value
ResultData
object which can be passed to the constructor of ResampleResult.
Examples
task = tsk("penguins")
learner = lrn("classif.rpart")
resampling = rsmp("cv", folds = 2)$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 prediction_test warnings
#> penguins classif.rpart cv 1 <PredictionClassif> 0
#> penguins classif.rpart cv 2 <PredictionClassif> 0
#> errors
#> 0
#> 0