mlr_measures_elapsed_time function

Elapsed Time Measure

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. The elapsed time accounts only for the training duration of the primary learner, excluding the time required for training the fallback learner.

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: $[0, Inf)$
• Minimize: TRUE
• Average: macro
• Required Prediction: NA
• Required Packages: list("mlr3")

Parameters

Empty ParamSet

See Also

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

• Package list("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:

  • list("mlr3proba") for probabilistic supervised regression and survival analysis.
  • list("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

Super class

mlr3::Measure -> MeasureElapsedTime

Public fields

• stages: (character())

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

Methods

Public methods

• list("MeasureElapsedTime$new()")

• list("MeasureElapsedTime$clone()")

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.