Nearest neighbours of a streamed layer in predictor space

Streams a query side x through the engine and, for each row, returns the mean distance to its nearest neighbours in a resident reference cloud y – where distance is measured in predictor (attribute) space, not on coordinates. This is the continuous half of the MOP transferability metric (Owens et al. 2013): the mean environmental distance from a projection cell to the nearest part of a calibration cloud. Where spatial_knn() measures geographic proximity with GEOS, feature_knn() measures environmental novelty with an L2 or Mahalanobis distance in n-variable space.

Usage

feature_knn(
  x,
  y,
  vars = NULL,
  k = NULL,
  percentage = NULL,
  metric = c("euclidean", "mahalanobis"),
  dist_col = "knn_distance",
  nthreads = NULL,
  flush_rows = NULL
)

Arguments

x

A vectra_node (from tbl(), tbl_csv(), ...): the streamed query side. Its predictor columns are read one batch at a time.

y

A data.frame or numeric matrix: the resident reference cloud, one column per predictor. Rows with any NA are dropped.

vars

Character vector of predictor column names present in both x and y. Defaults to the column names of y.

k

Number of nearest neighbours to average over. Supply exactly one of k or percentage.

percentage

Percent of the reference cloud to average over (the nearest ceil(percentage/100 * nrow(y)) neighbours). Supply exactly one of k or percentage.

metric

"euclidean" (default) for straight L2 distance in predictor units, or "mahalanobis" for distance whitened by the reference cloud's covariance.

dist_col

Name of the appended distance column. Default "knn_distance".

nthreads

Threads for the per-batch distance scan. NULL (default) uses all available (capped to two under R CMD check).

flush_rows

Rows to buffer before spilling a run file. NULL (default) spills by the streaming memory budget instead.

Value

A vectra_node of x's columns plus dist_col, backed by temporary .vtr spills removed when the node is garbage-collected.

Details

The reference cloud is materialized once, whitened for the chosen metric, and held resident; the query side streams one batch at a time, so the projection side can exceed memory while peak memory stays at one batch plus the resident cloud. The neighbour count is either an absolute k or the nearest percentage% of the cloud (ceil(percentage/100 * nrow(y))), matching MOP's proportion-of-reference formulation.

References

Owens, H.L. et al. (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263:10-18.

See also

rast_feature_distance() for the same distance computed out-of-core over a projection raster, spatial_knn() for geographic nearest neighbours.

Examples

set.seed(1)
ref <- data.frame(bio1 = rnorm(500, 10, 2), bio12 = rnorm(500, 800, 50))
qy  <- data.frame(bio1 = c(10, 20), bio12 = c(800, 400))
f <- tempfile(fileext = ".vtr")
write_vtr(qy, f)

# Row 1 sits in the cloud (small distance); row 2 is far outside it.
tbl(f) |>
  feature_knn(ref, percentage = 5) |>
  collect()
unlink(f)