Package 'mlr3batchmark'

mlr3batchmark: Batch Experiments for 'mlr3'

Description

Extends the 'mlr3' package with a connector to the package 'batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters.

Author(s)

Maintainer: Marc Becker [email protected] (ORCID)

Authors:

• Michel Lang [email protected] (ORCID)

See Also

Useful links:

• https:///mlr3batchmark.mlr-org.com

• https://github.com/mlr-org/mlr3batchmark

• Report bugs at https://github.com/mlr-org/mlr3batchmark/issues

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Benchmark Experiments on Batch Systems

Description

This function provides the functionality to leave the interface of mlr3 for the computation of benchmark experiments and switch over to batchtools for a more fine grained control over the execution.

batchmark() populates a batchtools::ExperimentRegistry with jobs in a mlr3::benchmark() fashion. Each combination of mlr3::Task and mlr3::Resampling defines a batchtools::Problem, each mlr3::Learner is an batchtools::Algorithm.

After the jobs have been submitted and are terminated, results can be collected with reduceResultsBatchmark() which returns a mlr3::BenchmarkResult and thus to return to the interface of mlr3.

Usage

batchmark(design, store_models = FALSE, reg = batchtools::getDefaultRegistry())

Arguments

design	(data.frame()) Data frame (or data.table::data.table()) with three columns: "task", "learner", and "resampling". Each row defines a resampling by providing a Task, Learner and an instantiated Resampling strategy. The helper function benchmark_grid() can assist in generating an exhaustive design (see examples) and instantiate the Resamplings per Task. Additionally, you can set the additional column 'param_values', see benchmark_grid().
store_models	(logical(1)) Store the fitted model in the resulting object= Set to TRUE if you want to further analyse the models or want to extract information like variable importance.
reg	batchtools::ExperimentRegistry.

Value

data.table() with ids of created jobs (invisibly).

Examples

tasks = list(mlr3::tsk("iris"), mlr3::tsk("sonar"))
learners = list(mlr3::lrn("classif.featureless"), mlr3::lrn("classif.rpart"))
resamplings = list(mlr3::rsmp("cv", folds = 3), mlr3::rsmp("holdout"))

design = mlr3::benchmark_grid(
  tasks = tasks,
  learners = learners,
  resamplings = resamplings
)

reg = batchtools::makeExperimentRegistry(NA)
batchmark(design, reg = reg)
batchtools::submitJobs(reg = reg)

reduceResultsBatchmark(reg = reg)

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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()
)

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).

Value

mlr3::BenchmarkResult.

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