Prediction Object for a Generic Torch Task — PredictionTorch • mlr3torch

The Prediction object returned by learners that were trained on a TaskTorch.

Because a TaskTorch can represent very different learning problems, this class does not prescribe much about how truth, response, prob and se are stored. This is defined by the task's prediction encoder, where you need to ensure that the first dimension indexes the observations. Within that, an element may be an atomic vector, a matrix(), an array() of any dimensionality, a data.table or a lazy_tensor. truth is whatever task$truth() returned – a vector for one target, a data.table for several, a lazy_tensor for a lazy tensor column, and nothing at all for a task without a target.

When a prediction is converted to a data.table, which is e.g. used for the printer, the conversion depends on the type of the object. A prob matrix spreads into one column per class, the way it does for a classification prediction. Everything else that is wider than one value per observation becomes a single column whose cells hold that observation's own array, printed as its shape – <array[3]> for a response matrix with three columns, <array[3x224x224]> for a prob with a class dimension and two spatial ones.

Missing Predictions

Only a response with a single value per observation can report a missing prediction, so $missing can only be TRUE for scalars and is always FALSE for parially missing predictions.

Super class

mlr3::Prediction -> PredictionTorch

Active bindings

response: (any)
The predicted response.
prob: (any)
The predicted probabilities.
se: (any)
The standard errors of the prediction.
lazy_tensor: (lazy_tensor or data.table of them)
The output of the network, for the predict type "lazy_tensor".

Methods

Inherited methods

`PredictionTorch$new()`

Creates a new instance of this R6 class.

Usage

PredictionTorch$new(
  task = NULL,
  row_ids = task$row_ids,
  truth = if (!is.null(task)) task$truth(row_ids),
  response = NULL,
  prob = NULL,
  se = NULL,
  lazy_tensor = NULL,
  weights = NULL,
  check = TRUE
)

Arguments

task: (TaskTorch)
The task that was predicted on. Used to extract the default row_ids and the truth.
row_ids: (integer())
The row ids of the predicted observations.
truth: (any)
The ground truth, i.e. what task$truth() returned.
response: (any)
The predicted response.
prob: (any)
The predicted probabilities.
se: (any)
The standard errors of the prediction.
lazy_tensor: (lazy_tensor or data.table of them)
The output of the network, see the predict type "lazy_tensor" of LearnerTorch. A network with more than one head produces one column per head.
weights: (numeric() or NULL)
The measure weights of the predicted observations, i.e. the weights_measure column of the task. mlr3 fills this in, so it rarely has to be passed by hand.
check: (logical(1))
Whether to check the consistency of the prediction data.

`PredictionTorch$clone()`

The objects of this class are cloneable with this method.

Usage

PredictionTorch$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

d = data.frame(x = rnorm(10), y1 = rnorm(10), y2 = rnorm(10))
task = as_task_torch(d, target = c("y1", "y2"))
PredictionTorch$new(task, response = as.matrix(d[, c("y1", "y2")]))
#> 
#> ── <PredictionTorch> for 10 observations: ──────────────────────────────────────
#>  row_ids    truth.y1    truth.y2   response
#>        1 -0.55369938  0.46815442 <array[2]>
#>        2  0.62898204  0.36295126 <array[2]>
#>        3  2.06502490 -1.30454355 <array[2]>
#>      ---         ---         ---        ---
#>        8 -0.05260191 -0.01595031 <array[2]>
#>        9  0.54299634 -0.82678895 <array[2]>
#>       10 -0.91407483 -1.51239965 <array[2]>