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Create a lazy tensor

Source: R/lazy_tensor.R
lazy_tensor.Rd

Creates a lazy_tensor vector. Because it is a vector, it can be stored in a data.table, which gives mlr3torch the ability to use arbitrary tensors in its task. It is 'lazy', because the tensors are not stored in-memory, but only loaded when calling materialize(). The vector itself only describes how to load the data. It is also possible to preprocess lazy_tensors, e.g. via po("augment_<key>"), and po("trafo_<key>").

Usage

lazy_tensor(data_descriptor = NULL, ids = NULL)

Arguments

data_descriptor

(DataDescriptor or NULL)
The data descriptor or NULL for a lazy tensor of length 0.

ids

(integer())
The elements of the data_descriptor to be included in the lazy tensor.

Examples

ds = dataset("example",
  initialize = function() self$iris = iris[, -5],
  .getitem = function(i) list(x = torch_tensor(as.numeric(self$iris[i, ]))),
  .length = function() nrow(self$iris)
)()
dd = as_data_descriptor(ds, list(x = c(NA, 4L)))
lt = as_lazy_tensor(dd)