This function is a very parallel version of benchmark using batchtools. Experiments are created in the provided registry for each combination of learners, tasks and resamplings. The experiments are then stored in a registry and the runs can be started via batchtools::submitJobs. A job is one train/test split of the outer resampling. In case of nested resampling (e.g. with makeTuneWrapper), each job is a full run of inner resampling, which can be parallelized in a second step with ParallelMap.
For details on the usage and support backends have a look at the batchtools tutorial page: https://github.com/mllg/batchtools.
The general workflow with batchmark
looks like this:
Create an ExperimentRegistry using batchtools::makeExperimentRegistry.
Call
batchmark(...)
which defines jobs for all learners and tasks in an base::expand.grid fashion.Submit jobs using batchtools::submitJobs.
Babysit the computation, wait for all jobs to finish using batchtools::waitForJobs.
Call
reduceBatchmarkResult()
to reduce results into a BenchmarkResult.
If you want to use this with OpenML datasets you can generate tasks
from a vector of dataset IDs easily with tasks = lapply(data.ids, function(x) convertOMLDataSetToMlr(getOMLDataSet(x)))
.
Usage
batchmark(
learners,
tasks,
resamplings,
measures,
keep.pred = TRUE,
keep.extract = FALSE,
models = FALSE,
reg = batchtools::getDefaultRegistry()
)
Arguments
- learners
(list of Learner | character)
Learning algorithms which should be compared, can also be a single learner. If you pass strings the learners will be created via makeLearner.- tasks
list of Task
Tasks that learners should be run on.- resamplings
[(list of) ResampleDesc)
Resampling strategy for each tasks. If only one is provided, it will be replicated to match the number of tasks. If missing, a 10-fold cross validation is used.- measures
(list of Measure)
Performance measures for all tasks. If missing, the default measure of the first task is used.- keep.pred
(
logical(1)
)
Keep the prediction data in thepred
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 toTRUE
.- keep.extract
(
logical(1)
)
Keep theextract
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 theextract
slot are removed by default (keep.extract = FALSE
). Note that whenkeep.extract = FALSE
you will not be able to conduct analysis in the tuning results.- models
(
logical(1)
)
Should all fitted models be stored in the ResampleResult? Default isFALSE
.- reg
(batchtools::Registry)
Registry, created by batchtools::makeExperimentRegistry. If not explicitly passed, uses the last created registry.
See also
Other benchmark:
BenchmarkResult
,
benchmark()
,
convertBMRToRankMatrix()
,
friedmanPostHocTestBMR()
,
friedmanTestBMR()
,
generateCritDifferencesData()
,
getBMRAggrPerformances()
,
getBMRFeatSelResults()
,
getBMRFilteredFeatures()
,
getBMRLearnerIds()
,
getBMRLearnerShortNames()
,
getBMRLearners()
,
getBMRMeasureIds()
,
getBMRMeasures()
,
getBMRModels()
,
getBMRPerformances()
,
getBMRPredictions()
,
getBMRTaskDescs()
,
getBMRTaskIds()
,
getBMRTuneResults()
,
plotBMRBoxplots()
,
plotBMRRanksAsBarChart()
,
plotBMRSummary()
,
plotCritDifferences()
,
reduceBatchmarkResults()