mlr_measures_elapsed_time: Elapsed Time Measure

In mlr3: Machine Learning in R - Next Generation

Elapsed Time Measure

Description

Measures the elapsed time during train ("time_train"), predict ("time_predict"), or both ("time_both"). Aggregation of elapsed time defaults to mean but can be configured via the field aggregator of the Measure.

When predictions for multiple predict sets were made during resample() or benchmark(), the predict time shows the cumulative duration of all predictions. If learner$predict() is called manually, the last predict time gets overwritten.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("time_train")
msr("time_train")

Meta Information

• Task type: “NA”

• Range: [0, \infty)

• Minimize: TRUE

• Average: macro

• Required Prediction: “NA”

• Required Packages: mlr3

Parameters

Empty ParamSet

Super class

mlr3::Measure -> MeasureElapsedTime

Public fields

stages

(character())
Which stages of the learner to measure? Usually set during construction.

Methods

Public methods

• MeasureElapsedTime$new()

• MeasureElapsedTime$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureElapsedTime$new(id = "elapsed_time", stages)

Arguments

id

(character(1))
Identifier for the new instance.

stages

(character())
Subset of ⁠("train", "predict")⁠. The runtime of provided stages will be summed.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureElapsedTime$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval

• Package mlr3measures for the scoring functions. Dictionary of Measures: mlr_measures as.data.table(mlr_measures) for a table of available Measures in the running session (depending on the loaded packages).

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

Other Measure: Measure, MeasureClassif, MeasureRegr, MeasureSimilarity, mlr_measures, mlr_measures_aic, mlr_measures_bic, mlr_measures_classif.costs, mlr_measures_debug_classif, mlr_measures_internal_valid_score, mlr_measures_oob_error, mlr_measures_regr.rsq, mlr_measures_selected_features

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.