Coercion methods to BenchmarkAggr. For mlr3::BenchmarkResult this is a simple
wrapper around the BenchmarkAggr constructor called with mlr3::BenchmarkResult$aggregate().
Usage
as.BenchmarkAggr(
obj,
task_id = "task_id",
learner_id = "learner_id",
independent = TRUE,
strip_prefix = TRUE,
...
)Arguments
- obj
(mlr3::BenchmarkResult|
matrix(1))
Passed to BenchmarkAggr$new().- task_id, learner_id, independent, strip_prefix
See BenchmarkAggr
$initialize().- ...
ANY
Passed to mlr3::BenchmarkResult$aggregate().
Examples
df = data.frame(tasks = factor(rep(c("A", "B"), each = 5),
levels = c("A", "B")),
learners = factor(paste0("L", 1:5)),
RMSE = runif(10), MAE = runif(10))
as.BenchmarkAggr(df, task_id = "tasks", learner_id = "learners")
#> <BenchmarkAggr> of 10 rows with 2 tasks, 5 learners and 2 measures
#> tasks learners RMSE MAE
#> 1: A L1 0.16703681 0.3812115
#> 2: A L2 0.97895632 0.2558161
#> 3: A L3 0.08437176 0.5651859
#> 4: A L4 0.07509937 0.1297404
#> 5: A L5 0.58178853 0.2279997
#> 6: B L1 0.93441621 0.8017235
#> 7: B L2 0.25682867 0.8973348
#> 8: B L3 0.05649689 0.1636037
#> 9: B L4 0.69183235 0.8169671
#> 10: B L5 0.55987702 0.4542707
if (requireNamespaces(c("mlr3", "rpart"))) {
library(mlr3)
task = tsks(c("boston_housing", "mtcars"))
learns = lrns(c("regr.featureless", "regr.rpart"))
bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 2)))
# default measure
as.BenchmarkAggr(bm)
# change measure
as.BenchmarkAggr(bm, measures = msr("regr.rmse"))
}
#> <BenchmarkAggr> of 4 rows with 2 tasks, 2 learners and 1 measure
#> task_id learner_id rmse
#> 1: boston_housing featureless 9.339177
#> 2: boston_housing rpart 2.040312
#> 3: mtcars featureless 6.087346
#> 4: mtcars rpart 6.087346