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This vignette summarizes the package interface for quick look-up while working in R.
refine_spatial_labels(xy, labels, samples = NULL, workers = NULL)
Refines noisy labels on finite 2D/3D coordinates. samples
defines fully independent coordinate systems, and workers
is one deterministic total CPU budget.
clean_categorical_mask(mask, samples = NULL, workers = NULL)
Cleans a categorical matrix (2D) or array (3D).
simulate_spatial_domains(...)simulate_complex_spatial_domains(...)simulate_spatial_clusters(...)simulate_gradient_regions(...)simulate_volumetric_domains(...)All simulators return a spatial_refinement_benchmark
object with xy, labels, truth,
samples, and optional
boundary/sparse fields.
refine_spatial_labels() contractxy: a finite numeric matrix with exactly two or three
columns.labels: one non-missing categorical assignment per
row.samples: optional integer, character, or factor
identifiers. Coordinates and labels never cross sample boundaries, even
when coordinate values overlap.workers: NULL or one positive integer
specifying the total CPU budget.Constant axes are removed separately in each sample. A constant
z therefore uses the 2D operator, while a variable
z uses the genuine 3D operator. Every sample must retain at
least two varying axes.
The return value is an ordinary factor with input levels and
identifiers. Its pointwise attributes are candidate,
margin_score, required,
repair_margin, atlas_dispersion,
isolation, and changed. Summary attributes are
workers, dimensions_used,
labels_changed, changed_fraction,
classes_before, classes_after,
removed_classes, and sample_sizes. Class
summaries are per-sample named lists. Class preservation is not imposed;
removed_classes reports observed classes absent after
repair.
set.seed(8)
xy <- matrix(runif(1200), ncol = 2)
labels <- factor(ifelse(xy[, 1] < 0.5, "left", "right"))
refined_2d <- refine_spatial_labels(xy, labels, workers = 1L)
refined_flat_3d <- refine_spatial_labels(
cbind(xy, z = 0), labels, workers = 1L
)
stopifnot(identical(refined_2d, refined_flat_3d))
volume <- simulate_volumetric_domains(
n = 1200L, shape = "folded_layers", samples = 2L, seed = 9L
)
refined_3d <- refine_spatial_labels(
volume$xy, volume$labels, volume$samples, workers = 2L
)
attr(refined_3d, "dimensions_used")## 1 2
## 3 3overlap_xy <- rbind(xy, xy)
overlap_labels <- factor(rep(as.character(labels), 2L))
specimen <- rep(c("first", "second"), each = nrow(xy))
refined_joint <- refine_spatial_labels(
overlap_xy, overlap_labels, specimen, workers = 2L
)
attr(refined_joint, "sample_sizes")## first second
## 600 600evaluate_spatial_refinement(truth, initial, refined, ...)
Computes recovery, consistency, boundary, sparse-region, and
damage/repair diagnostics.
evaluate_mask_cleaning(reference, initial, cleaned, ...)
Computes mean IoU, boundary IoU, and damage/repair decomposition for
masks.
benchmark_spatial_refiners(data, methods, include_initial = TRUE, seed = 1L, ...)
Runs multiple methods on one or more identical benchmark
inputs.
spatial_benchmark(xy, labels, truth, samples, ...)
Validates and constructs a benchmark object.
available_spatial_benchmarks()
Lists bundled datasets and licensing information.
load_spatial_benchmark(name, scenario, seed = NULL)
Loads a bundled real scenario. For CRC, an optional user-supplied seed
makes the corruption generated from the stored recipe
reproducible.
accuracy: overall corrected agreement.accuracy_gain: improvement over input labeling.correction_recall: fraction of wrong labels that become
correct.damage_rate: fraction of correct labels that become
wrong.worst_recall: minimum class recall.boundary_accuracy: conditional accuracy on
boundary-labeled spots.sparse_region_accuracy: conditional accuracy on a
sparse reference region.ari: adjusted Rand index.n: number of observations.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.