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Class for Logging Evaluated Hyperparameter Configurations

Source: R/ArchiveBatchTuning.R
ArchiveBatchTuning.Rd

The ArchiveBatchTuning stores all evaluated hyperparameter configurations and performance scores in a data.table::data.table().

Details

The ArchiveBatchTuning is a container around a data.table::data.table(). Each row corresponds to a single evaluation of a hyperparameter configuration. See the section on Data Structure for more information. The archive stores additionally a mlr3::BenchmarkResult ($benchmark_result) that records the resampling experiments. Each experiment corresponds to to a single evaluation of a hyperparameter configuration. The table ($data) and the benchmark result ($benchmark_result) are linked by the uhash column. If the archive is passed to as.data.table(), both are joined automatically.

Data Structure

The table ($data) has the following columns:

  • One column for each hyperparameter of the search space ($search_space).

  • One (list-)column for the internal_tuned_values

  • One column for each performance measure ($codomain).

  • x_domain (list())
    Lists of (transformed) hyperparameter values that are passed to the learner.

  • runtime_learners (numeric(1))
    Sum of training and predict times logged in learners per mlr3::ResampleResult / evaluation. This does not include potential overhead time.

  • timestamp (POSIXct)
    Time stamp when the evaluation was logged into the archive.

  • batch_nr (integer(1))
    Hyperparameters are evaluated in batches. Each batch has a unique batch number.

  • uhash (character(1))
    Connects each hyperparameter configuration to the resampling experiment stored in the mlr3::BenchmarkResult.

Analysis

For analyzing the tuning results, it is recommended to pass the ArchiveBatchTuning to as.data.table(). The returned data table is joined with the benchmark result which adds the mlr3::ResampleResult for each hyperparameter evaluation.

The archive provides various getters (e.g. $learners()) to ease the access. All getters extract by position (i) or unique hash (uhash). For a complete list of all getters see the methods section.

The benchmark result ($benchmark_result) allows to score the hyperparameter configurations again on a different measure. Alternatively, measures can be supplied to as.data.table().

The mlr3viz package provides visualizations for tuning results.

S3 Methods

  • as.data.table.ArchiveTuning(x, unnest = "x_domain", exclude_columns = "uhash", measures = NULL)
    Returns a tabular view of all evaluated hyperparameter configurations.
    ArchiveBatchTuning -> data.table::data.table()

    • x (ArchiveBatchTuning)

    • unnest (character())
      Transforms list columns to separate columns. Set to NULL if no column should be unnested.

    • exclude_columns (character())
      Exclude columns from table. Set to NULL if no column should be excluded.

    • measures (List of mlr3::Measure)
      Score hyperparameter configurations on additional measures.

Super classes

bbotk::Archive -> bbotk::ArchiveBatch -> ArchiveBatchTuning

Public fields

benchmark_result

(mlr3::BenchmarkResult)
Benchmark result.

Active bindings

internal_search_space

(paradox::ParamSet)
The search space containing those parameters that are internally optimized by the mlr3::Learner.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

ArchiveBatchTuning$new(
  search_space,
  codomain,
  check_values = FALSE,
  internal_search_space = NULL
)

Arguments

search_space

(paradox::ParamSet)
Hyperparameter search space. If NULL (default), the search space is constructed from the paradox::TuneToken of the learner's parameter set (learner$param_set).

codomain

(bbotk::Codomain)
Specifies codomain of objective function i.e. a set of performance measures. Internally created from provided mlr3::Measures.

check_values

(logical(1))
If TRUE (default), hyperparameter configurations are check for validity.

internal_search_space

(paradox::ParamSet or NULL)
The internal search space.

Method learner()

Retrieve mlr3::Learner of the i-th evaluation, by position or by unique hash uhash. i and uhash are mutually exclusive. Learner does not contain a model. Use $learners() to get learners with models.

Usage

ArchiveBatchTuning$learner(i = NULL, uhash = NULL)

Arguments

i

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

uhash

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

Method learners()

Retrieve list of trained mlr3::Learner objects of the i-th evaluation, by position or by unique hash uhash. i and uhash are mutually exclusive.

Usage

ArchiveBatchTuning$learners(i = NULL, uhash = NULL)

Arguments

i

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

uhash

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

Method learner_param_vals()

Retrieve param values of the i-th evaluation, by position or by unique hash uhash. i and uhash are mutually exclusive.

Usage

ArchiveBatchTuning$learner_param_vals(i = NULL, uhash = NULL)

Arguments

i

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

uhash

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

Method predictions()

Retrieve list of mlr3::Prediction objects of the i-th evaluation, by position or by unique hash uhash. i and uhash are mutually exclusive.

Usage

ArchiveBatchTuning$predictions(i = NULL, uhash = NULL)

Arguments

i

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

uhash

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

Method resample_result()

Retrieve mlr3::ResampleResult of the i-th evaluation, by position or by unique hash uhash. i and uhash are mutually exclusive.

Usage

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

Arguments

i

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

uhash

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

Method print()

Printer.

Usage

ArchiveBatchTuning$print()

Arguments

...

(ignored).

Method clone()

The objects of this class are cloneable with this method.

Usage

ArchiveBatchTuning$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.