Create a Dataset from a Task — task

Creates a torch dataset from an mlr3 Task. The resulting dataset's $.getbatch() method returns a list with elements x, y and .index:

x is a list with tensors, whose content is defined by the parameter feature_ingress_tokens.
y is the target variable and its content is defined by the parameter target_batchgetter.
.index is the index of the batch in the task's data.

The data is returned on the device specified by the parameter device.

Usage

task_dataset(
  task,
  feature_ingress_tokens,
  target_batchgetter = NULL,
  cache_data = TRUE
)

Arguments

task: (Task)
The task for which to build the dataset.
feature_ingress_tokens: (named list() of TorchIngressToken)
Each ingress token defines one item in the $x value of a batch with corresponding names.
target_batchgetter: (function() or NULL)
Converts the target columns of a batch into the target tensor y that the loss is applied to. Takes an argument data, a data.table with only the target columns, and optionally an argument x, the named list of feature tensors of the batch, which is what a target that is a function of the input needs, see get_target_batchgetter(). If NULL (default), the batches have no y element.
cache_data: (logical(1))
Whether to retrieve the data from the task once (in $initialize()) and to take the batches from it, instead of retrieving every batch from the task, which is slow. This needs memory for a copy of the task data (lazy tensor columns only contain indices and are small).

Value

torch::dataset

Examples

task = tsk("iris")
sepal_ingress = TorchIngressToken(
  features = c("Sepal.Length", "Sepal.Width"),
  batchgetter = batchgetter_num,
  shape = c(NA, 2)
)
petal_ingress = TorchIngressToken(
  features = c("Petal.Length", "Petal.Width"),
  batchgetter = batchgetter_num,
  shape = c(NA, 2)
)
ingress_tokens = list(sepal = sepal_ingress, petal = petal_ingress)

target_batchgetter = function(data) {
  torch_tensor(data = data[[1L]], dtype = torch_float32())$unsqueeze(2)
}
dataset = task_dataset(task, ingress_tokens, target_batchgetter)
batch = dataset$.getbatch(1:10)
batch
#> $x
#> $x$sepal
#> torch_tensor
#>  5.1000  3.5000
#>  4.9000  3.0000
#>  4.7000  3.2000
#>  4.6000  3.1000
#>  5.0000  3.6000
#>  5.4000  3.9000
#>  4.6000  3.4000
#>  5.0000  3.4000
#>  4.4000  2.9000
#>  4.9000  3.1000
#> [ CPUFloatType{10,2} ]
#> 
#> $x$petal
#> torch_tensor
#>  1.4000  0.2000
#>  1.4000  0.2000
#>  1.3000  0.2000
#>  1.5000  0.2000
#>  1.4000  0.2000
#>  1.7000  0.4000
#>  1.4000  0.3000
#>  1.5000  0.2000
#>  1.4000  0.2000
#>  1.5000  0.1000
#> [ CPUFloatType{10,2} ]
#> 
#> 
#> $.index
#> torch_tensor
#>   1
#>   2
#>   3
#>   4
#>   5
#>   6
#>   7
#>   8
#>   9
#>  10
#> [ CPULongType{10} ]
#> 
#> $y
#> torch_tensor
#>  1
#>  1
#>  1
#>  1
#>  1
#>  1
#>  1
#>  1
#>  1
#>  1
#> [ CPUFloatType{10,1} ]
#>