Fit or apply the MiniRocket transform — minirocket • PhysioML

Uses aeon.transformations.collection.convolution_based.MiniRocket. A fitted model records the realized feature width because aeon rounds the requested kernel count according to the MiniRocket kernel family.

Usage

minirocket(
  x,
  model = NULL,
  assay_name = NULL,
  channels = NULL,
  cases = NULL,
  n_kernels = 10000L,
  seed = 1L,
  deterministic = TRUE,
  backend = "aeon"
)

Arguments

x: A PhysioExperiment.
model: NULL or a fitted physio_rocket_model.
assay_name, channels, cases: Input selection; see catch22().
n_kernels: One finite whole number of kernels.
seed: One whole-number backend seed in [0, 2^31 - 1].
deterministic: One non-missing logical. TRUE requires the fixed-seed, single-worker aeon path and records that contract. Determinism is scoped to the recorded backend versions and platform.
backend: Exactly "aeon".

Value

A physio_rocket_transform; see rocket().

References

Dempster A, Schmidt DF, Webb GI (2021). MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification. doi:10.1145/3447548.3467231

Examples

arr <- array(stats::rnorm(60 * 2 * 4), dim = c(60, 2, 4))
pe <- PhysioExperiment::PhysioExperiment(
  assays = list(raw = arr),
  colData = S4Vectors::DataFrame(label = c("C3", "C4")),
  samplingRate = 100
)
# \donttest{
# The transform needs a caller-managed Python environment with NumPy and aeon.
fit <- minirocket(pe, n_kernels = 84)
#> Error: the active reticulate Python lacks required module(s): aeon
dim(fit$features)
#> Error: object 'fit' not found
# }