Container for Benchmarking Results

This is the result container object returned by benchmark(). A BenchmarkResult consists of the data of multiple ResampleResults. The contents of a BenchmarkResult and ResampleResult are almost identical and the stored ResampleResults can be extracted via the $resample_result(i) method, where i is the index of the performed resample experiment. This allows us to investigate the extracted ResampleResult and individual resampling iterations, as well as the predictions and models from each fold.

BenchmarkResults can be visualized via mlr3viz's autoplot() function.

For statistical analysis of benchmark results and more advanced plots, see mlr3benchmark.

Note

All stored objects are accessed by reference. Do not modify any extracted object without cloning it first.

S3 Methods

as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test", task_characteristics = FALSE)
BenchmarkResult -> data.table::data.table()
Returns a tabular view of the internal data.
c(...)
(BenchmarkResult, ...) -> BenchmarkResult
Combines multiple objects convertible to BenchmarkResult into a new BenchmarkResult.

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:

"task_hash" (character(1)),
"task_id" (character(1)), and
"task" (Task).

learners

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

"learner_hash" (character(1)),
"learner_id" (character(1)), and
"learner" (Learner).

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 reset 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:

"resampling_hash" (character(1)),
"resampling_id" (character(1)), and
"resampling" (Resampling).

resample_results

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

uhash (character()).
resample_result (ResampleResult).

n_resample_results

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

uhashes

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

uhash_table

(data.table::data.table)
Table with columns uhash, learner_id, task_id and resampling_id.

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(...)

Arguments

...: (ignored).

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 `marshal()`

Marshals all stored models.

Usage

BenchmarkResult$marshal(...)

Arguments

...: (any)
Additional arguments passed to marshal_model().

Method `unmarshal()`

Unmarshals all stored models.

Usage

BenchmarkResult$unmarshal(...)

Arguments

...: (any)
Additional arguments passed to unmarshal_model().

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,
  conditions = FALSE,
  predictions = TRUE
)

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 to the returned table.
conditions: (logical(1))
Adds condition messages ("warnings", "errors") as extra list columns of character vectors to the returned table
predictions: (logical(1))
Additionally return prediction objects, one column for each predict_set of all learners combined. Columns are named "prediction_train", "prediction_test" and "prediction_internal_valid", if present.

Returns

data.table::data.table().

Method `obs_loss()`

Calculates the observation-wise loss via the loss function set in the Measure's field obs_loss. Returns a data.table() with the columns row_ids, truth, response and one additional numeric column for each measure, named with the respective measure id. If there is no observation-wise loss function for the measure, the column is filled with NA values. Note that some measures such as RMSE, do have an $obs_loss, but they require an additional transformation after aggregation, in this example taking the square-root.

Usage

BenchmarkResult$obs_loss(measures = NULL, predict_sets = "test")

Arguments

measures: (Measure | list of Measure)
Measure(s) to calculate.
predict_sets: (character())
The predict sets.

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.

The method for aggregation is controlled by the Measure, e.g. micro aggregation, macro aggregation or custom aggregation. Most measures default to macro aggregation.

Note that the aggregated performances just give a quick impression which approaches work well and which approaches are probably underperforming. However, the aggregates do not account for variance and cannot replace a statistical test. See mlr3viz to get a better impression via boxplots or mlr3benchmark for critical difference plots and significance tests.

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. You can either directly provide the row IDs or the uhashes of the resample results to keep, or use the learner_ids, task_ids and resampling_ids arguments to filter for learner, task and resampling IDs. The three options are mutually exclusive.

Usage

BenchmarkResult$filter(
  i = NULL,
  uhashes = NULL,
  learner_ids = NULL,
  task_ids = NULL,
  resampling_ids = NULL
)

Arguments

i: (integer() | NULL)
The iteration values to filter for.
uhashes: (character() | NULL)
The uhashes of the resample results to filter for.
learner_ids: (character() | NULL)
The learner IDs to filter for.
task_ids: (character() | NULL)
The task IDs to filter for.
resampling_ids: (character() | NULL)
The resampling IDs to filter for.

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.

Examples

design = benchmark_grid(
  tsks(c("iris", "sonar")),
  lrns(c("classif.debug", "classif.featureless")),
  rsmp("holdout")
)
bmr = benchmark(design)
bmr
bmr2 = bmr$clone(deep = TRUE)
bmr2$filter(learner_ids = "classif.featureless")
bmr2

Method `resample_result()`

Retrieve the i-th ResampleResult, by position, by unique hash uhash or by learner, task and resampling IDs. All three options are mutually exclusive.

Usage

BenchmarkResult$resample_result(
  i = NULL,
  uhash = NULL,
  task_id = NULL,
  learner_id = NULL,
  resampling_id = NULL
)

Arguments

i: (integer(1) | NULL)
The iteration value to filter for.
uhash: (character(1) | NULL)
The unique identifier to filter for.
task_id: (character(1) | NULL)
The task ID to filter for.
learner_id: (character(1) | NULL)
The learner ID to filter for.
resampling_id: (character(1) | NULL)
The resampling ID to filter for.

Returns

ResampleResult.

Examples

design = benchmark_grid(
  tsk("iris"),
  lrns(c("classif.debug", "classif.featureless")),
  rsmp("holdout")
)
bmr = benchmark(design)
bmr$resample_result(learner_id = "classif.featureless")
bmr$resample_result(i = 1)
bmr$resample_result(uhash = uhashes(bmr, learner_id = "classif.debug"))

Method `discard()`

Shrinks the BenchmarkResult by discarding parts of the internally stored data. Note that certain operations might stop work, e.g. extracting importance values from learners or calculating measures requiring the task's data.

Usage

BenchmarkResult$discard(backends = FALSE, models = FALSE)

Arguments

backends: (logical(1))
If TRUE, the DataBackend is removed from all stored Tasks.
models: (logical(1))
If TRUE, the stored model is removed from all Learners.

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 `set_threshold()`

Sets the threshold for the response prediction of classification learners, given they have output a probability prediction for a binary classification task.

The resample results for which to change the threshold can either be specified directly via uhashes, by selecting the specific iterations (i) or by filtering according to learner, task and resampling IDs.

If none of the three options is specified, the threshold is set for all resample results.

Usage

BenchmarkResult$set_threshold(
  threshold,
  i = NULL,
  uhashes = NULL,
  learner_ids = NULL,
  task_ids = NULL,
  resampling_ids = NULL,
  ties_method = "random"
)

Arguments

threshold

(numeric(1))
Threshold value.

i

(integer() | NULL)
The iteration values to filter for.

uhashes

(character() | NULL)
The unique identifiers of the ResampleResults for which the threshold should be set.

learner_ids

(character() | NULL)
The learner IDs for which the threshold should be set.

task_ids

(character() | NULL)
The task IDs for which the threshold should be set.

resampling_ids

(character() | NULL)
The resampling IDs for which the threshold should be set.

ties_method

(character(1))
Method to handle ties in probabilities when selecting a class label. Must be one of "random", "first" or "last" (corresponding to the same options in max.col()).

"random": Randomly select one of the tied class labels (default).
"first": Select the first class label among tied values.
"last": Select the last class label among tied values.

Examples

design = benchmark_grid(
  tsk("sonar"),
  lrns(c("classif.debug", "classif.featureless"), predict_type = "prob"),
  rsmp("holdout")
)
bmr = benchmark(design)
bmr$set_threshold(0.8, learner_ids = "classif.featureless")
bmr$set_threshold(0.3, i = 2)
bmr$set_threshold(0.7, uhashes = uhashes(bmr, learner_ids = "classif.featureless"))

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("penguins")),
  learners = learners,
  resamplings = rsmp("cv", folds = 3)
)
print(design)
#>        task             learner resampling
#>      <char>              <char>     <char>
#> 1:    sonar classif.featureless         cv
#> 2:    sonar       classif.rpart         cv
#> 3: penguins classif.featureless         cv
#> 4: penguins       classif.rpart         cv

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 penguins classif.featureless            cv     3        0      0
#>   4 penguins       classif.rpart            cv     3        0      0

bmr$tasks
#> Key: <task_hash>
#>           task_hash  task_id                   task
#>              <char>   <char>                 <list>
#> 1: 389105c365bf9405    sonar    <TaskClassif:sonar>
#> 2: a91e40052f01cdb1 penguins <TaskClassif:penguins>
bmr$learners
#> Key: <learner_hash>
#>        learner_hash          learner_id
#>              <char>              <char>
#> 1: 24129222692c2943       classif.rpart
#> 2: 38adf5e650d6602c classif.featureless
#>                                            learner
#>                                             <list>
#> 1:             <LearnerClassifRpart:classif.rpart>
#> 2: <LearnerClassifFeatureless:classif.featureless>

# first 5 resampling iterations
head(as.data.table(bmr, measures = c("classif.acc", "classif.auc")), 5)
#>                                   uhash                task
#>                                  <char>              <list>
#> 1: 42323adc-d295-4c5e-a773-3301b1ada8e2 <TaskClassif:sonar>
#> 2: 42323adc-d295-4c5e-a773-3301b1ada8e2 <TaskClassif:sonar>
#> 3: 42323adc-d295-4c5e-a773-3301b1ada8e2 <TaskClassif:sonar>
#> 4: 03892d45-d2de-4534-a949-823874d570b6 <TaskClassif:sonar>
#> 5: 03892d45-d2de-4534-a949-823874d570b6 <TaskClassif:sonar>
#>                                            learner     resampling iteration
#>                                             <list>         <list>     <int>
#> 1: <LearnerClassifFeatureless:classif.featureless> <ResamplingCV>         1
#> 2: <LearnerClassifFeatureless:classif.featureless> <ResamplingCV>         2
#> 3: <LearnerClassifFeatureless:classif.featureless> <ResamplingCV>         3
#> 4:             <LearnerClassifRpart:classif.rpart> <ResamplingCV>         1
#> 5:             <LearnerClassifRpart:classif.rpart> <ResamplingCV>         2
#>             prediction task_id          learner_id resampling_id
#>                 <list>  <char>              <char>        <char>
#> 1: <PredictionClassif>   sonar classif.featureless            cv
#> 2: <PredictionClassif>   sonar classif.featureless            cv
#> 3: <PredictionClassif>   sonar classif.featureless            cv
#> 4: <PredictionClassif>   sonar       classif.rpart            cv
#> 5: <PredictionClassif>   sonar       classif.rpart            cv

# aggregate results
bmr$aggregate()
#>       nr  task_id          learner_id resampling_id iters classif.ce
#>    <int>   <char>              <char>        <char> <int>      <num>
#> 1:     1    sonar classif.featureless            cv     3 0.46604555
#> 2:     2    sonar       classif.rpart            cv     3 0.27391304
#> 3:     3 penguins classif.featureless            cv     3 0.55814900
#> 4:     4 penguins       classif.rpart            cv     3 0.05812357
#> Hidden columns: resample_result

# aggregate results with hyperparameters as separate columns
mlr3misc::unnest(bmr$aggregate(params = TRUE), "params")
#>       nr  task_id          learner_id resampling_id iters classif.ce method
#>    <int>   <char>              <char>        <char> <int>      <num> <char>
#> 1:     1    sonar classif.featureless            cv     3 0.46604555   mode
#> 2:     2    sonar       classif.rpart            cv     3 0.27391304   <NA>
#> 3:     3 penguins classif.featureless            cv     3 0.55814900   mode
#> 4:     4 penguins       classif.rpart            cv     3 0.05812357   <NA>
#>     xval
#>    <int>
#> 1:    NA
#> 2:     0
#> 3:    NA
#> 4:     0
#> Hidden columns: resample_result

# extract resample result for classif.rpart
rr = bmr$aggregate()[learner_id == "classif.rpart", resample_result][[1]]
print(rr)
#> <ResampleResult> with 3 resampling iterations
#>  task_id    learner_id resampling_id iteration     prediction_test warnings
#>    sonar classif.rpart            cv         1 <PredictionClassif>        0
#>    sonar classif.rpart            cv         2 <PredictionClassif>        0
#>    sonar classif.rpart            cv         3 <PredictionClassif>        0
#>  errors
#>       0
#>       0
#>       0

# 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

## ------------------------------------------------
## Method `BenchmarkResult$filter`
## ------------------------------------------------

design = benchmark_grid(
  tsks(c("iris", "sonar")),
  lrns(c("classif.debug", "classif.featureless")),
  rsmp("holdout")
)
bmr = benchmark(design)
bmr
#> <BenchmarkResult> of 4 rows with 4 resampling runs
#>  nr task_id          learner_id resampling_id iters warnings errors
#>   1    iris       classif.debug       holdout     1        0      0
#>   2    iris classif.featureless       holdout     1        0      0
#>   3   sonar       classif.debug       holdout     1        0      0
#>   4   sonar classif.featureless       holdout     1        0      0
bmr2 = bmr$clone(deep = TRUE)
bmr2$filter(learner_ids = "classif.featureless")
bmr2
#> <BenchmarkResult> of 2 rows with 2 resampling runs
#>  nr task_id          learner_id resampling_id iters warnings errors
#>   1    iris classif.featureless       holdout     1        0      0
#>   2   sonar classif.featureless       holdout     1        0      0

## ------------------------------------------------
## Method `BenchmarkResult$resample_result`
## ------------------------------------------------

design = benchmark_grid(
  tsk("iris"),
  lrns(c("classif.debug", "classif.featureless")),
  rsmp("holdout")
)
bmr = benchmark(design)
bmr$resample_result(learner_id = "classif.featureless")
#> <ResampleResult> with 1 resampling iterations
#>  task_id          learner_id resampling_id iteration     prediction_test
#>     iris classif.featureless       holdout         1 <PredictionClassif>
#>  warnings errors
#>         0      0
bmr$resample_result(i = 1)
#> <ResampleResult> with 1 resampling iterations
#>  task_id    learner_id resampling_id iteration     prediction_test warnings
#>     iris classif.debug       holdout         1 <PredictionClassif>        0
#>  errors
#>       0
bmr$resample_result(uhash = uhashes(bmr, learner_id = "classif.debug"))
#> <ResampleResult> with 1 resampling iterations
#>  task_id    learner_id resampling_id iteration     prediction_test warnings
#>     iris classif.debug       holdout         1 <PredictionClassif>        0
#>  errors
#>       0

## ------------------------------------------------
## Method `BenchmarkResult$set_threshold`
## ------------------------------------------------

design = benchmark_grid(
  tsk("sonar"),
  lrns(c("classif.debug", "classif.featureless"), predict_type = "prob"),
  rsmp("holdout")
)
bmr = benchmark(design)
bmr$set_threshold(0.8, learner_ids = "classif.featureless")
#> Key: <uhash, iteration>
#>                                   uhash iteration      learner_state prediction
#>                                  <char>     <int>             <list>     <list>
#> 1: 00d4fe13-c0af-47a7-8587-0daf7b0add61         1 <learner_state[9]>  <list[1]>
#> 2: 126b0fc4-dc3b-4d52-8a77-120a53c6e294         1 <learner_state[8]>  <list[1]>
#>        learner_hash        task_hash    learner_phash  resampling_hash
#>              <char>           <char>           <char>           <char>
#> 1: 729485e635936fe0 389105c365bf9405 c1ddf900c095e7ef 35db3d2bb507d357
#> 2: 38adf5e650d6602c 389105c365bf9405 3f40f5f172de95d9 35db3d2bb507d357
bmr$set_threshold(0.3, i = 2)
#> Key: <uhash, iteration>
#>                                   uhash iteration      learner_state prediction
#>                                  <char>     <int>             <list>     <list>
#> 1: 00d4fe13-c0af-47a7-8587-0daf7b0add61         1 <learner_state[9]>  <list[1]>
#> 2: 126b0fc4-dc3b-4d52-8a77-120a53c6e294         1 <learner_state[8]>  <list[1]>
#>        learner_hash        task_hash    learner_phash  resampling_hash
#>              <char>           <char>           <char>           <char>
#> 1: 729485e635936fe0 389105c365bf9405 c1ddf900c095e7ef 35db3d2bb507d357
#> 2: 38adf5e650d6602c 389105c365bf9405 3f40f5f172de95d9 35db3d2bb507d357
bmr$set_threshold(0.7, uhashes = uhashes(bmr, learner_ids = "classif.featureless"))
#> Key: <uhash, iteration>
#>                                   uhash iteration      learner_state prediction
#>                                  <char>     <int>             <list>     <list>
#> 1: 00d4fe13-c0af-47a7-8587-0daf7b0add61         1 <learner_state[9]>  <list[1]>
#> 2: 126b0fc4-dc3b-4d52-8a77-120a53c6e294         1 <learner_state[8]>  <list[1]>
#>        learner_hash        task_hash    learner_phash  resampling_hash
#>              <char>           <char>           <char>           <char>
#> 1: 729485e635936fe0 389105c365bf9405 c1ddf900c095e7ef 35db3d2bb507d357
#> 2: 38adf5e650d6602c 389105c365bf9405 3f40f5f172de95d9 35db3d2bb507d357

Note

S3 Methods

See also

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method help()

Usage

Method format()

Usage

Arguments

Method print()

Usage

Method combine()

Usage

Arguments

Returns

Method marshal()

Usage

Arguments

Method unmarshal()

Usage

Arguments

Method score()

Usage

Arguments

Returns

Method obs_loss()

Usage

Arguments

Method aggregate()

Usage

Arguments

Returns

Method filter()

Usage

Arguments

Returns

Examples

Method resample_result()

Usage

Arguments

Returns

Examples

Method discard()

Usage

Arguments

Returns

Method set_threshold()

Usage

Arguments

Examples

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `help()`

Method `format()`

Method `print()`

Method `combine()`

Method `marshal()`

Method `unmarshal()`

Method `score()`

Method `obs_loss()`

Method `aggregate()`

Method `filter()`

Method `resample_result()`

Method `discard()`

Method `set_threshold()`

Method `clone()`