2.6 Benchmarking

Comparing the performance of different learners on multiple tasks and/or different resampling schemes is a recurrent task. This operation is usually referred to as “benchmarking” in the field of machine-learning. The mlr3 package offers the benchmark() function for convenience.

2.6.1 Design Creation

In mlr3 we require you to supply a “design” of your benchmark experiment. By “design” we essentially mean the matrix of settings you want to execute. A “design” consists of Task, Learner and Resampling.

Here, we call benchmark() to perform a single holdout split on a single task and two learners. We use the benchmark_grid() function to create an exhaustive design and properly instantiate the resampling:

library(data.table)
design = benchmark_grid(
  tasks = tsk("iris"),
  learners = list(lrn("classif.rpart"), lrn("classif.featureless")),
  resamplings = rsmp("holdout")
)
print(design)
##             task                     learner          resampling
## 1: <TaskClassif>       <LearnerClassifRpart> <ResamplingHoldout>
## 2: <TaskClassif> <LearnerClassifFeatureless> <ResamplingHoldout>
bmr = benchmark(design)

Note that the holdout splits have been automatically instantiated for each row of the design. As a result, the rpart learner used a different training set than the featureless learner. However, for comparison of learners you usually want the learners to see the same splits into train and test sets. To overcome this issue, the resampling strategy needs to be manually instantiated before creating the design.

The interface of benchmark() allows for full flexibility. Irrespective, the creation of such design tables can be tedious. Therefore, mlr3 provides a convenience function to quickly generate design tables and instantiate resampling strategies in an exhaustive grid fashion: benchmark_grid().

# get some example tasks
tasks = lapply(c("german_credit", "sonar"), tsk)

# get some learners and for all learners ...
# * predict probabilities
# * predict also on the training set
library(mlr3learners)
learners = c("classif.featureless", "classif.rpart", "classif.ranger", "classif.kknn")
learners = lapply(learners, lrn,
  predict_type = "prob", predict_sets = c("train", "test"))

# compare via 3-fold cross validation
resamplings = rsmp("cv", folds = 3)

# create a BenchmarkDesign object
design = benchmark_grid(tasks, learners, resamplings)
print(design)
##             task                     learner     resampling
## 1: <TaskClassif> <LearnerClassifFeatureless> <ResamplingCV>
## 2: <TaskClassif>       <LearnerClassifRpart> <ResamplingCV>
## 3: <TaskClassif>      <LearnerClassifRanger> <ResamplingCV>
## 4: <TaskClassif>        <LearnerClassifKKNN> <ResamplingCV>
## 5: <TaskClassif> <LearnerClassifFeatureless> <ResamplingCV>
## 6: <TaskClassif>       <LearnerClassifRpart> <ResamplingCV>
## 7: <TaskClassif>      <LearnerClassifRanger> <ResamplingCV>
## 8: <TaskClassif>        <LearnerClassifKKNN> <ResamplingCV>

2.6.2 Execution and Aggregation of Results

After the benchmark design is ready, we can directly call benchmark():

# execute the benchmark
bmr = benchmark(design)

Note that we did not instantiate the resampling instance. benchmark_grid() took care of it for us: Each resampling strategy is instantiated for each task during the construction of the exhaustive grid.

After the benchmark, one can calculate and aggregate the performance with .$aggregate():

# measures:
# * area under the curve (auc) on training
# * area under the curve (auc) on test
measures = list(
  msr("classif.auc", id = "auc_train", predict_sets = "train"),
  msr("classif.auc", id = "auc_test")
)
bmr$aggregate(measures)
##    nr  resample_result       task_id          learner_id resampling_id iters
## 1:  1 <ResampleResult> german_credit classif.featureless            cv     3
## 2:  2 <ResampleResult> german_credit       classif.rpart            cv     3
## 3:  3 <ResampleResult> german_credit      classif.ranger            cv     3
## 4:  4 <ResampleResult> german_credit        classif.kknn            cv     3
## 5:  5 <ResampleResult>         sonar classif.featureless            cv     3
## 6:  6 <ResampleResult>         sonar       classif.rpart            cv     3
## 7:  7 <ResampleResult>         sonar      classif.ranger            cv     3
## 8:  8 <ResampleResult>         sonar        classif.kknn            cv     3
##    auc_train auc_test
## 1:    0.5000   0.5000
## 2:    0.7855   0.7033
## 3:    0.9984   0.7966
## 4:    0.9867   0.7158
## 5:    0.5000   0.5000
## 6:    0.9420   0.7469
## 7:    1.0000   0.9152
## 8:    0.9986   0.9200

Subsequently, we can aggregate the results further. For example, we might be interested which learner performed best over all tasks simultaneously. Simply aggregating the performances with the mean is usually not statistically sound. Instead, we calculate the rank statistic for each learner grouped by task. Then the calculated ranks grouped by learner are aggregated. Since the AUC needs to be maximized, we multiply with \(-1\) so that the best learner gets a rank of 1.

tab = bmr$aggregate(measures)
ranks = tab[, .(learner_id, rank_train = rank(-auc_train), rank_test = rank(-auc_test)), by = task_id]
print(ranks)
##          task_id          learner_id rank_train rank_test
## 1: german_credit classif.featureless          4         4
## 2: german_credit       classif.rpart          3         3
## 3: german_credit      classif.ranger          1         1
## 4: german_credit        classif.kknn          2         2
## 5:         sonar classif.featureless          4         4
## 6:         sonar       classif.rpart          3         3
## 7:         sonar      classif.ranger          1         2
## 8:         sonar        classif.kknn          2         1

ranks[, .(mrank_train = mean(rank_train), mrank_test = mean(rank_test)), by = learner_id][order(mrank_test)]
##             learner_id mrank_train mrank_test
## 1:      classif.ranger           1        1.5
## 2:        classif.kknn           2        1.5
## 3:       classif.rpart           3        3.0
## 4: classif.featureless           4        4.0

Unsurprisingly, the featureless learner is outperformed.

2.6.3 Plotting Benchmark Results

Analogously to plotting tasks, predictions or resample results, mlr3viz also provides a autoplot() method for benchmark results.

library(mlr3viz)
library(ggplot2)

autoplot(bmr) + theme(axis.text.x = element_text(angle = 45, hjust = 1))

autoplot(bmr, type = "roc", task_id = "german_credit")

All available types are listed on the manual page of autoplot.BenchmarkResult().

2.6.4 Converting specific benchmark objects to resample objects

A BenchmarkResult object is essentially a collection of multiple ResampleResult objects. As these are stored in a column of the aggregated data.table(), we can easily extract them:

tab = bmr$aggregate(measures)
rr = tab[task_id == "sonar" & learner_id == "classif.ranger"]$resample_result[[1]]
print(rr)
## <ResampleResult> of 3 iterations
## * Task: sonar
## * Learner: classif.ranger
## * Warnings: 0 in 0 iterations
## * Errors: 0 in 0 iterations

We can now investigate this resampling and even single resampling iterations using one of the approach shown in the previous section:

measure = msr("classif.auc")
rr$aggregate(measure)
## classif.auc 
##      0.9152

# get the iteration with worst AUC
perf = rr$score(measure)
i = which.min(perf$classif.auc)

# get the corresponding learner and train set
print(rr$learners[[i]])
## <LearnerClassifRanger:classif.ranger>
## * Model: -
## * Parameters: list()
## * Packages: ranger
## * Predict Type: prob
## * Feature types: logical, integer, numeric, character, factor, ordered
## * Properties: importance, multiclass, oob_error, twoclass, weights
head(rr$resampling$train_set(i))
## [1]  4  5  6 10 15 17