reduceResultsBatchmark: Collect Results from batchmark
In mlr3batchmark: Batch Experiments for 'mlr3'

View source: R/reduceResultsBatchmark.R

reduceResultsBatchmark

R Documentation

Collect Results from batchmark

Description

Collect the results from jobs defined via batchmark() and combine them into a mlr3::BenchmarkResult.

Note that ids defaults to finished jobs (as reported by batchtools::findDone()). If a job threw an error, is expired or is still running, it will be ignored with this default. Just leaving these jobs out in an analysis is not statistically sound. Instead, try to robustify your jobs by using a fallback learner (c.f. mlr3::Learner).

Usage

reduceResultsBatchmark(
  ids = NULL,
  store_backends = TRUE,
  reg = batchtools::getDefaultRegistry(),
  fun = NULL,
  unmarshal = TRUE
)

Arguments

`ids`	[`data.frame` or `integer`] A `data.frame` (or `data.table`) with a column named “job.id”. Alternatively, you may also pass a vector of integerish job ids. If not set, defaults to the return value of `findDone`. Invalid ids are ignored.
`store_backends`	(`logical(1)`) Keep the DataBackend of the Task in the ResampleResult? Set to `TRUE` if your performance measures require a Task, or to analyse results more conveniently. Set to `FALSE` to reduce the file size and memory footprint after serialization. The current default is `TRUE`, but this eventually will be changed in a future release.
`reg`	[`Registry`] Registry. If not explicitly passed, uses the default registry (see `setDefaultRegistry`).
`fun`	[`function`] Function to apply to each result. The result is passed unnamed as first argument. If `NULL`, the identity is used. If the function has the formal argument “job”, the `Job`/`Experiment` is also passed to the function.
`unmarshal`	`Learner` Whether to unmarshal learners that were marshaled during the execution. If `TRUE` all models are stored in unmarshaled form. If `FALSE`, all learners (that need marshaling) are stored in marshaled form.