mlr: reduceBatchmarkResults – R documentation

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mlr

reduceBatchmarkResults

Reduce results of a batch-distributed benchmark.

Description

This creates a BenchmarkResult from a batchtools::ExperimentRegistry. To setup the benchmark have a look at batchmark.

Usage

reduceBatchmarkResults(
  ids = NULL,
  keep.pred = TRUE,
  keep.extract = FALSE,
  show.info = getMlrOption("show.info"),
  reg = batchtools::getDefaultRegistry()
)

Arguments

`ids`	(data.frame or integer) A base::data.frame (or data.table::data.table) with a column named “job.id”. Alternatively, you may also pass a vector of integerish job ids. If not set, defaults to all successfully terminated jobs (return value of batchtools::findDone.
`keep.pred`	(`logical(1)`) Keep the prediction data in the `pred` slot of the result object. If you do many experiments (on larger data sets) these objects might unnecessarily increase object size / mem usage, if you do not really need them. The default is set to `TRUE`.
`keep.extract`	(`logical(1)`) Keep the `extract` slot of the result object. When creating a lot of benchmark results with extensive tuning, the resulting R objects can become very large in size. That is why the tuning results stored in the `extract` slot are removed by default (`keep.extract = FALSE`). Note that when `keep.extract = FALSE` you will not be able to conduct analysis in the tuning results.
`show.info`	(`logical(1)`) Print verbose output on console? Default is set via configureMlr.
`reg`	(batchtools::ExperimentRegistry) Registry, created by batchtools::makeExperimentRegistry. If not explicitly passed, uses the last created registry.

Value

(BenchmarkResult).

Other benchmark: BenchmarkResult, batchmark(), benchmark(), convertBMRToRankMatrix(), friedmanPostHocTestBMR(), friedmanTestBMR(), generateCritDifferencesData(), getBMRAggrPerformances(), getBMRFeatSelResults(), getBMRFilteredFeatures(), getBMRLearnerIds(), getBMRLearnerShortNames(), getBMRLearners(), getBMRMeasureIds(), getBMRMeasures(), getBMRModels(), getBMRPerformances(), getBMRPredictions(), getBMRTaskDescs(), getBMRTaskIds(), getBMRTuneResults(), plotBMRBoxplots(), plotBMRRanksAsBarChart(), plotBMRSummary(), plotCritDifferences()

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release

reduceBatchmarkResults

Description

Usage

Arguments

Value

See Also

mlr

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