Run machine learning benchmarks as distributed experiments.

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:

1. Create an ExperimentRegistry using batchtools::makeExperimentRegistry.

2. Call batchmark(...) which defines jobs for all learners and tasks in an base::expand.grid fashion.

3. Submit jobs using batchtools::submitJobs.

4. Babysit the computation, wait for all jobs to finish using batchtools::waitForJobs.

5. 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))).

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 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.
models	(logical(1)) Should all fitted models be stored in the ResampleResult? Default is FALSE.
reg	(batchtools::Registry) Registry, created by batchtools::makeExperimentRegistry. If not explicitly passed, uses the last created registry.

Value

(data.table). Generated job ids are stored in the column “job.id”.

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()