Measure for a Generic Torch Task

Wraps a plain R function into a Measure that scores the predictions of a TaskTorch. Use msr_torch() to construct one. See the Custom Learning Problems article for how to create and use such measures.

See also

Other Measure: mlr_measures_torch.default

Super class

mlr3::Measure -> MeasureTorch

Active bindings

hash

(character(1))
The hash of the measure.

Methods

Public methods

• MeasureTorch$new()

• MeasureTorch$clone()

Inherited methods

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

---

MeasureTorch$new()

Creates a new instance of this R6 class.

Usage

MeasureTorch$new(
  id,
  fun,
  minimize = NA,
  range = c(-Inf, Inf),
  predict_type = "response",
  properties = character(),
  label = NA_character_,
  obs_loss = NULL
)

Arguments

id

(character(1))
The id of the measure.

fun

(function())
The scoring function. It receives whichever of the arguments truth, response, prob, se, lazy_tensor, prediction, task, learner, train_set and weights it declares, and must return a single number. Asking for anything else is an error.

minimize

(logical(1))
Whether a smaller score is better. NA (default) means the direction is unknown.

range

(numeric(2))
The range of possible scores.

predict_type

(character(1))
The predict type the measure requires: "response" (default), "prob", "se" or "lazy_tensor". A measure asking for one it did not declare here still receives it, if the prediction has it.

properties

(character())
Properties of the measure, see Measure. The "requires_task", "requires_learner", "requires_train_set" and "weights" properties are added automatically when fun declares the corresponding argument.

label

(character(1))
The label of the measure.

obs_loss

(function() or NULL)
The per-observation loss. Declared like fun, except that train_set is not available here, and if specified adds the "obs_loss" property. It must return one number per observation.

---

MeasureTorch$clone()

The objects of this class are cloneable with this method.

Usage

MeasureTorch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

d = data.frame(x = rnorm(10), y = rnorm(10))
task = as_task_torch(d, target = "y")
measure = msr_torch("mse", function(truth, response) mean((truth - response)^2))
measure
#> 
#> ── <MeasureTorch> (mse) ────────────────────────────────────────────────────────
#> • Packages: mlr3
#> • Range: [-Inf, Inf]
#> • Minimize: NA
#> • Average: macro
#> • Parameters: list()
#> • Properties: -
#> • Predict type: response
#> • Predict sets: test
#> • Aggregator: mean()