This is the result container object returned by benchmark(). A BenchmarkResult consists of the data row-binded data of multiple ResampleResults, which can easily be re-constructed.

Note that all stored objects are accessed by reference. Do not modify any object without cloning it first.

S3 Methods

Public fields

data

(ResultData)
Internal data storage object of type ResultData. We discourage users to directly work with this field. Use as.table.table(BenchmarkResult) instead.

Active bindings

task_type

(character(1))
Task type of objects in the BenchmarkResult. All stored objects (Task, Learner, Prediction) in a single BenchmarkResult are required to have the same task type, e.g., "classif" or "regr". This is NA for empty BenchmarkResults.

tasks

(data.table::data.table())
Table of included Tasks with three columns:

learners

(data.table::data.table())
Table of included Learners with three columns:

Note that it is not feasible to access learned models via this field, as the training task would be ambiguous. For this reason the returned learner are reseted before they are returned. Instead, select a row from the table returned by $score().

resamplings

(data.table::data.table())
Table of included Resamplings with three columns:

resample_results

(data.table::data.table())
Returns a table with three columns:

n_resample_results

(integer(1))
Returns the total number of stored ResampleResults.

uhashes

(character())
Set of (unique) hashes of all included ResampleResults.

Methods

Public methods


Method new()

Creates a new instance of this R6 class.

Usage

BenchmarkResult$new(data = NULL)

Arguments

data

(ResultData)
An object of type ResultData, either extracted from another ResampleResult, another BenchmarkResult, or manually constructed with as_result_data().


Method help()

Opens the help page for this object.

Usage

BenchmarkResult$help()


Method format()

Helper for print outputs.

Usage

BenchmarkResult$format()


Method print()

Printer.

Usage

BenchmarkResult$print()


Method combine()

Fuses a second BenchmarkResult into itself, mutating the BenchmarkResult in-place. If the second BenchmarkResult bmr is NULL, simply returns self. Note that you can alternatively use the combine function c() which calls this method internally.

Usage

BenchmarkResult$combine(bmr)

Arguments

bmr

(BenchmarkResult)
A second BenchmarkResult object.

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keep the object in its previous state.


Method score()

Returns a table with one row for each resampling iteration, including all involved objects: Task, Learner, Resampling, iteration number (integer(1)), and Prediction. If ids is set to TRUE, character column of extracted ids are added to the table for convenient filtering: "task_id", "learner_id", and "resampling_id".

Additionally calculates the provided performance measures and binds the performance scores as extra columns. These columns are named using the id of the respective Measure.

Usage

BenchmarkResult$score(measures = NULL, ids = TRUE, predict_sets = "test")

Arguments

measures

(Measure | list of Measure)
Measure(s) to calculate.

ids

(logical(1))
Adds object ids ("task_id", "learner_id", "resampling_id") as extra character columns for convenient subsetting.

predict_sets

(character())
Vector of predict sets ({"train", "test"}) to construct the Prediction objects from. Default is "test".

Returns

data.table::data.table().


Method aggregate()

Returns a result table where resampling iterations are combined into ResampleResults. A column with the aggregated performance score is added for each Measure, named with the id of the respective measure.

For convenience, different flags can be set to extract more information from the returned ResampleResult:

Usage

BenchmarkResult$aggregate(
  measures = NULL,
  ids = TRUE,
  uhashes = FALSE,
  params = FALSE,
  conditions = FALSE
)

Arguments

measures

(Measure | list of Measure)
Measure(s) to calculate.

ids

(logical(1))
Adds object ids ("task_id", "learner_id", "resampling_id") as extra character columns for convenient subsetting.

uhashes

(logical(1))
Adds the uhash values of the ResampleResult as extra character column "uhash".

params

(logical(1))
Adds the hyperparameter values as extra list column "params". You can unnest them with mlr3misc::unnest().

conditions

(logical(1))
Adds the number of resampling iterations with at least one warning as extra integer column "warnings", and the number of resampling iterations with errors as extra integer column "errors".

Returns

data.table::data.table().


Method filter()

Subsets the benchmark result. If task_ids is not NULL, keeps all tasks with provided task ids and discards all others tasks. Same procedure for learner_ids and resampling_ids.

Usage

BenchmarkResult$filter(
  task_ids = NULL,
  task_hashes = NULL,
  learner_ids = NULL,
  learner_hashes = NULL,
  resampling_ids = NULL,
  resampling_hashes = NULL
)

Arguments

task_ids

(character())
Ids of Tasks to keep.

task_hashes

(character())
Hashes of Tasks to keep.

learner_ids

(character())
Ids of Learners to keep.

learner_hashes

(character())
Hashes of Learners to keep.

resampling_ids

(character())
Ids of Resamplings to keep.

resampling_hashes

(character())
Hashes of Resamplings to keep.

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.


Method resample_result()

Retrieve the i-th ResampleResult, by position or by unique hash uhash. i and uhash are mutually exclusive.

Usage

BenchmarkResult$resample_result(i = NULL, uhash = NULL)

Arguments

i

(integer(1))
The iteration value to filter for.

uhash

(logical(1))
The ushash value to filter for.

Returns

ResampleResult.


Method clone()

The objects of this class are cloneable with this method.

Usage

BenchmarkResult$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

set.seed(123) learners = list( lrn("classif.featureless", predict_type = "prob"), lrn("classif.rpart", predict_type = "prob") ) design = benchmark_grid( tasks = list(tsk("sonar"), tsk("spam")), learners = learners, resamplings = rsmp("cv", folds = 3) ) print(design)
#> task learner resampling #> 1: <TaskClassif[45]> <LearnerClassifFeatureless[33]> <ResamplingCV[19]> #> 2: <TaskClassif[45]> <LearnerClassifRpart[33]> <ResamplingCV[19]> #> 3: <TaskClassif[45]> <LearnerClassifFeatureless[33]> <ResamplingCV[19]> #> 4: <TaskClassif[45]> <LearnerClassifRpart[33]> <ResamplingCV[19]>
bmr = benchmark(design) print(bmr)
#> <BenchmarkResult> of 12 rows with 4 resampling runs #> nr task_id learner_id resampling_id iters warnings errors #> 1 sonar classif.featureless cv 3 0 0 #> 2 sonar classif.rpart cv 3 0 0 #> 3 spam classif.featureless cv 3 0 0 #> 4 spam classif.rpart cv 3 0 0
bmr$tasks
#> task_hash task_id task #> 1: 5ad02f3fd7003382 sonar <TaskClassif[45]> #> 2: b1ff4af1334c1f9b spam <TaskClassif[45]>
bmr$learners
#> learner_hash learner_id learner #> 1: 3bbabd1058707305 classif.featureless <LearnerClassifFeatureless[33]> #> 2: fc402e71eadd46bb classif.rpart <LearnerClassifRpart[33]>
# first 5 resampling iterations head(as.data.table(bmr, measures = c("classif.acc", "classif.auc")), 5)
#> uhash task #> 1: dda21070-039c-4fe7-acbd-8ad8b243b488 <TaskClassif[45]> #> 2: dda21070-039c-4fe7-acbd-8ad8b243b488 <TaskClassif[45]> #> 3: dda21070-039c-4fe7-acbd-8ad8b243b488 <TaskClassif[45]> #> 4: 525b8784-6490-4ab9-980b-70ea2c1a0dcc <TaskClassif[45]> #> 5: 525b8784-6490-4ab9-980b-70ea2c1a0dcc <TaskClassif[45]> #> learner resampling iteration #> 1: <LearnerClassifFeatureless[33]> <ResamplingCV[19]> 1 #> 2: <LearnerClassifFeatureless[33]> <ResamplingCV[19]> 2 #> 3: <LearnerClassifFeatureless[33]> <ResamplingCV[19]> 3 #> 4: <LearnerClassifRpart[33]> <ResamplingCV[19]> 1 #> 5: <LearnerClassifRpart[33]> <ResamplingCV[19]> 2 #> prediction #> 1: <PredictionClassif[19]> #> 2: <PredictionClassif[19]> #> 3: <PredictionClassif[19]> #> 4: <PredictionClassif[19]> #> 5: <PredictionClassif[19]>
# aggregate results bmr$aggregate()
#> nr resample_result task_id learner_id resampling_id iters #> 1: 1 <ResampleResult[21]> sonar classif.featureless cv 3 #> 2: 2 <ResampleResult[21]> sonar classif.rpart cv 3 #> 3: 3 <ResampleResult[21]> spam classif.featureless cv 3 #> 4: 4 <ResampleResult[21]> spam classif.rpart cv 3 #> classif.ce #> 1: 0.4660455 #> 2: 0.2739130 #> 3: 0.3940399 #> 4: 0.1086721
# aggregate results with hyperparameters as separate columns mlr3misc::unnest(bmr$aggregate(params = TRUE), "params")
#> nr resample_result task_id learner_id resampling_id iters #> 1: 1 <ResampleResult[21]> sonar classif.featureless cv 3 #> 2: 2 <ResampleResult[21]> sonar classif.rpart cv 3 #> 3: 3 <ResampleResult[21]> spam classif.featureless cv 3 #> 4: 4 <ResampleResult[21]> spam classif.rpart cv 3 #> classif.ce method xval #> 1: 0.4660455 mode NA #> 2: 0.2739130 <NA> 0 #> 3: 0.3940399 mode NA #> 4: 0.1086721 <NA> 0
# extract resample result for classif.rpart rr = bmr$aggregate()[learner_id == "classif.rpart", resample_result][[1]] print(rr)
#> <ResampleResult> of 3 iterations #> * Task: sonar #> * Learner: classif.rpart #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations
# access the confusion matrix of the first resampling iteration rr$predictions()[[1]]$confusion
#> truth #> response M R #> M 30 18 #> R 3 19
# reduce to subset with task id "sonar" bmr$filter(task_ids = "sonar") print(bmr)
#> <BenchmarkResult> of 6 rows with 2 resampling runs #> nr task_id learner_id resampling_id iters warnings errors #> 1 sonar classif.featureless cv 3 0 0 #> 2 sonar classif.rpart cv 3 0 0