Benchmark Multiple Learners on Multiple Tasks

Runs a benchmark on arbitrary combinations of tasks (Task), learners (Learner), and resampling strategies (Resampling), possibly in parallel.

benchmark(design, store_models = FALSE)

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.
store_models	:: logical(1) Keep the fitted model after the test set has been predicted? Set to TRUE if you want to further analyse the models or want to extract information like variable importance.

Value

BenchmarkResult.

Note

The fitted models are discarded after the predictions have been scored in order to reduce memory consumption. If you need access to the models for later analysis, set store_models to TRUE.

Parallelization

This function can be parallelized with the future package. One job is one resampling iteration, and all jobs are send to an apply function from future.apply in a single batch. To select a parallel backend, use future::plan().

Logging

The mlr3 uses the lgr package for logging. lgr supports multiple log levels which can be queried with getOption("lgr.log_levels").

To suppress output and reduce verbosity, you can lower the log from the default level "info" to "warn":

lgr::get_logger("mlr3")$set_threshold("warn")

To get additional log output for debugging, increase the log level to "debug" or "trace":

lgr::get_logger("mlr3")$set_threshold("debug")

To log to a file or a data base, see the documentation of lgr::lgr-package.

Examples

# benchmarking with benchmark_grid()
tasks = lapply(c("iris", "sonar"), tsk)
learners = lapply(c("classif.featureless", "classif.rpart"), lrn)
resamplings = rsmp("cv", folds = 3)

design = benchmark_grid(tasks, learners, resamplings)
print(design)
#>             task                     learner     resampling
#> 1: <TaskClassif> <LearnerClassifFeatureless> <ResamplingCV>
#> 2: <TaskClassif>       <LearnerClassifRpart> <ResamplingCV>
#> 3: <TaskClassif> <LearnerClassifFeatureless> <ResamplingCV>
#> 4: <TaskClassif>       <LearnerClassifRpart> <ResamplingCV>

set.seed(123)
bmr = benchmark(design)

## data of all resamplings
head(as.data.table(bmr))
#>                                   uhash          task
#> 1: 73b46536-ab17-4451-bb0d-8563f8f24951 <TaskClassif>
#> 2: 73b46536-ab17-4451-bb0d-8563f8f24951 <TaskClassif>
#> 3: 73b46536-ab17-4451-bb0d-8563f8f24951 <TaskClassif>
#> 4: dca25f7b-d92b-4ef0-a0bd-c8acdc15f8ce <TaskClassif>
#> 5: dca25f7b-d92b-4ef0-a0bd-c8acdc15f8ce <TaskClassif>
#> 6: dca25f7b-d92b-4ef0-a0bd-c8acdc15f8ce <TaskClassif>
#>                        learner     resampling iteration prediction
#> 1: <LearnerClassifFeatureless> <ResamplingCV>         1     <list>
#> 2: <LearnerClassifFeatureless> <ResamplingCV>         2     <list>
#> 3: <LearnerClassifFeatureless> <ResamplingCV>         3     <list>
#> 4:       <LearnerClassifRpart> <ResamplingCV>         1     <list>
#> 5:       <LearnerClassifRpart> <ResamplingCV>         2     <list>
#> 6:       <LearnerClassifRpart> <ResamplingCV>         3     <list>

## aggregated performance values
aggr = bmr$aggregate()
print(aggr)
#>    nr  resample_result task_id          learner_id resampling_id iters
#> 1:  1 <ResampleResult>    iris classif.featureless            cv     3
#> 2:  2 <ResampleResult>    iris       classif.rpart            cv     3
#> 3:  3 <ResampleResult>   sonar classif.featureless            cv     3
#> 4:  4 <ResampleResult>   sonar       classif.rpart            cv     3
#>    classif.ce
#> 1: 0.69333333
#> 2: 0.06666667
#> 3: 0.46632160
#> 4: 0.30317460

## Extract predictions of first resampling result
rr = aggr$resample_result[[1]]
as.data.table(rr$prediction())
#>      row_id     truth   response
#>   1:      1    setosa  virginica
#>   2:      4    setosa  virginica
#>   3:      7    setosa  virginica
#>   4:      9    setosa  virginica
#>   5:     10    setosa  virginica
#>  ---                            
#> 146:    135 virginica versicolor
#> 147:    139 virginica versicolor
#> 148:    140 virginica versicolor
#> 149:    143 virginica versicolor
#> 150:    146 virginica versicolor

# benchmarking with a custom design:
# - fit classif.featureless on iris with a 3-fold CV
# - fit classif.rpart on sonar using a holdout
tasks = list(tsk("iris"), tsk("sonar"))
learners = list(lrn("classif.featureless"), lrn("classif.rpart"))
resamplings = list(rsmp("cv", folds = 3), rsmp("holdout"))

design = data.table::data.table(
  task = tasks,
  learner = learners,
  resampling = resamplings
)

## instantiate resamplings
design$resampling = Map(
  function(task, resampling) resampling$clone()$instantiate(task),
  task = design$task, resampling = design$resampling
)

## run benchmark
bmr = benchmark(design)
print(bmr)
#> <BenchmarkResult> of 4 rows with 2 resampling runs
#>  nr task_id          learner_id resampling_id iters warnings errors
#>   1    iris classif.featureless            cv     3        0      0
#>   2   sonar       classif.rpart       holdout     1        0      0

## get the training set of the 2nd iteration of the featureless learner on iris
rr = bmr$aggregate()[learner_id == "classif.featureless"]$resample_result[[1]]
rr$resampling$train_set(2)
#>   [1]   2   3   7  14  17  18  20  22  32  37  40  43  44  45  47  50  53  56
#>  [19]  57  63  67  69  71  72  73  76  79  81  83  84  85  88  92  95  97 113
#>  [37] 114 115 120 127 130 131 137 138 140 141 142 143 149 150   6   8  11  12
#>  [55]  15  16  19  23  24  28  29  33  34  38  39  42  49  51  58  59  61  62
#>  [73]  64  65  66  68  74  77  78  82  87  90  93  99 101 104 105 108 111 112
#>  [91] 119 121 124 128 129 133 135 144 145 146