Create a case-aware torch Dataset

Converts a PhysioExperiment assay to fixed channel-by-time items. Split cases before calling this function, fit normalization on training cases only, and pass normalization_stats to validation and prediction datasets. Class tensors are zero-based torch_long; input and regression tensors are torch_float32. A one-item batch always retains its batch dimension.

Usage

peDataset(
  x,
  targets = NULL,
  assay_name = NULL,
  cases = NULL,
  channels = NULL,
  window_samples = NULL,
  stride_samples = NULL,
  normalization = c("none", "zscore", "robust"),
  normalization_stats = NULL,
  task = c("classification", "regression"),
  class_levels = NULL,
  dtype = "float32"
)

Arguments

x

A PhysioExperiment; attach subject keys with withCaseData().

targets

Optional one-per-selected-case target vector.

assay_name, cases, channels

Exact assay and identity selections.

window_samples, stride_samples

Optional complete-window dimensions.

normalization

Exact normalization method.

normalization_stats

Frozen statistics from a training dataset.

task

Exact task name.

class_levels

Optional ordered classification labels.

dtype

Currently exactly "float32".

Value

A physio_torch_dataset.

Examples

# Prepare the input offline: a time x channel x case assay with channel labels.
arr <- array(stats::rnorm(50 * 2 * 6), dim = c(50, 2, 6))
pe <- PhysioExperiment::PhysioExperiment(
  assays = list(raw = arr),
  colData = S4Vectors::DataFrame(label = c("C3", "C4")),
  samplingRate = 100
)
# Building the Dataset itself needs the optional torch backend.
if (requireNamespace("torch", quietly = TRUE)) {
  ds <- peDataset(
    pe,
    targets = rep(c("rest", "task"), 3),
    task = "classification",
    class_levels = c("rest", "task")
  )
}
#> Error: the R package `torch` is installed but LibTorch is unavailable; install it explicitly with `torch::install_torch()`