BiocNeighbors 1.18.0
The BiocNeighbors package implements a few algorithms for exact nearest neighbor searching:
Both KMKNN and VP-trees involve a component of randomness during index construction, though the k-nearest neighbors result is fully deterministic1 Except in the presence of ties, see ?"BiocNeighbors-ties"
for details..
The most obvious application is to perform a k-nearest neighbors search. We’ll mock up an example here with a hypercube of points, for which we want to identify the 10 nearest neighbors for each point.
nobs <- 10000
ndim <- 20
data <- matrix(runif(nobs*ndim), ncol=ndim)
The findKNN()
method expects a numeric matrix as input with data points as the rows and variables/dimensions as the columns.
We indicate that we want to use the KMKNN algorithm by setting BNPARAM=KmknnParam()
(which is also the default, so this is not strictly necessary here).
We could use a VP tree instead by setting BNPARAM=VptreeParam()
.
fout <- findKNN(data, k=10, BNPARAM=KmknnParam())
head(fout$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 9409 4947 6178 9537 5260 4894 3072 306 228 6636
## [2,] 1547 5962 9214 242 9999 2935 2289 1071 8246 3475
## [3,] 4380 5286 9594 2424 4232 7580 5375 5945 4079 3906
## [4,] 2025 3102 5535 1981 5268 5150 6909 2393 9473 2498
## [5,] 6507 7425 2858 1753 5849 7601 2396 8150 1416 7257
## [6,] 2899 1654 6726 5665 717 7604 3320 9263 4368 8343
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9785586 0.9811400 0.9913524 0.9955739 1.0255069 1.0308471 1.0383945
## [2,] 0.9602596 0.9691522 0.9751631 0.9805606 1.0096187 1.0118636 1.0228048
## [3,] 0.9089951 0.9643081 1.0012023 1.0079162 1.0319489 1.0568212 1.0589021
## [4,] 0.7974994 1.0328205 1.0414875 1.0479995 1.0546405 1.0586517 1.0591534
## [5,] 0.8548139 0.9159989 0.9444234 0.9676975 0.9683909 0.9748149 0.9831826
## [6,] 0.9433159 1.0117848 1.0126210 1.0370186 1.0444959 1.0452362 1.0508700
## [,8] [,9] [,10]
## [1,] 1.038541 1.067886 1.074520
## [2,] 1.041626 1.041785 1.046640
## [3,] 1.059560 1.065766 1.069269
## [4,] 1.072983 1.073397 1.077641
## [5,] 1.012390 1.013242 1.032472
## [6,] 1.053048 1.073025 1.086548
Each row of the index
matrix corresponds to a point in data
and contains the row indices in data
that are its nearest neighbors.
For example, the 3rd point in data
has the following nearest neighbors:
fout$index[3,]
## [1] 4380 5286 9594 2424 4232 7580 5375 5945 4079 3906
… with the following distances to those neighbors:
fout$distance[3,]
## [1] 0.9089951 0.9643081 1.0012023 1.0079162 1.0319489 1.0568212 1.0589021
## [8] 1.0595602 1.0657655 1.0692688
Note that the reported neighbors are sorted by distance.
Another application is to identify the k-nearest neighbors in one dataset based on query points in another dataset. Again, we mock up a small data set:
nquery <- 1000
ndim <- 20
query <- matrix(runif(nquery*ndim), ncol=ndim)
We then use the queryKNN()
function to identify the 5 nearest neighbors in data
for each point in query
.
qout <- queryKNN(data, query, k=5, BNPARAM=KmknnParam())
head(qout$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 3138 7318 7776 9124 7533
## [2,] 6252 1530 2753 4190 9861
## [3,] 2254 3043 8079 6589 2833
## [4,] 1178 8334 5769 6381 3610
## [5,] 9574 2021 344 5418 4231
## [6,] 1598 8368 6649 7836 5376
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9110958 0.9896955 1.0039828 1.0040069 1.0055336
## [2,] 0.9441806 0.9474333 1.0602833 1.0626497 1.0770631
## [3,] 0.9538700 0.9605901 0.9634138 0.9688830 0.9759902
## [4,] 1.0595337 1.0763289 1.1018678 1.1339158 1.1457390
## [5,] 0.8947893 0.8952181 0.9134670 0.9660932 0.9958256
## [6,] 0.8996672 0.9262411 0.9570033 0.9598391 0.9925181
Each row of the index
matrix contains the row indices in data
that are the nearest neighbors of a point in query
.
For example, the 3rd point in query
has the following nearest neighbors in data
:
qout$index[3,]
## [1] 2254 3043 8079 6589 2833
… with the following distances to those neighbors:
qout$distance[3,]
## [1] 0.9538700 0.9605901 0.9634138 0.9688830 0.9759902
Again, the reported neighbors are sorted by distance.
Users can perform the search for a subset of query points using the subset=
argument.
This yields the same result as but is more efficient than performing the search for all points and subsetting the output.
findKNN(data, k=5, subset=3:5)
## $index
## [,1] [,2] [,3] [,4] [,5]
## [1,] 4380 5286 9594 2424 4232
## [2,] 2025 3102 5535 1981 5268
## [3,] 6507 7425 2858 1753 5849
##
## $distance
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9089951 0.9643081 1.0012023 1.0079162 1.0319489
## [2,] 0.7974994 1.0328205 1.0414875 1.0479995 1.0546405
## [3,] 0.8548139 0.9159989 0.9444234 0.9676975 0.9683909
If only the indices are of interest, users can set get.distance=FALSE
to avoid returning the matrix of distances.
This will save some time and memory.
names(findKNN(data, k=2, get.distance=FALSE))
## [1] "index"
It is also simple to speed up functions by parallelizing the calculations with the BiocParallel framework.
library(BiocParallel)
out <- findKNN(data, k=10, BPPARAM=MulticoreParam(3))
For multiple queries to a constant data
, the pre-clustering can be performed in a separate step with buildIndex()
.
The result can then be passed to multiple calls, avoiding the overhead of repeated clustering2 The algorithm type is automatically determined when BNINDEX
is specified, so there is no need to also specify BNPARAM
in the later functions..
pre <- buildIndex(data, BNPARAM=KmknnParam())
out1 <- findKNN(BNINDEX=pre, k=5)
out2 <- queryKNN(BNINDEX=pre, query=query, k=2)
The default setting is to search on the Euclidean distance.
Alternatively, we can use the Manhattan distance by setting distance="Manhattan"
in the BiocNeighborParam
object.
out.m <- findKNN(data, k=5, BNPARAM=KmknnParam(distance="Manhattan"))
Advanced users may also be interested in the raw.index=
argument, which returns indices directly to the precomputed object rather than to data
.
This may be useful inside package functions where it may be more convenient to work on a common precomputed object.
sessionInfo()
## R version 4.3.0 RC (2023-04-13 r84266)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocParallel_1.34.1 BiocNeighbors_1.18.0 knitr_1.42
## [4] BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] cli_3.6.1 rlang_1.1.0 xfun_0.38
## [4] jsonlite_1.8.4 S4Vectors_0.38.1 htmltools_0.5.5
## [7] stats4_4.3.0 sass_0.4.5 rmarkdown_2.21
## [10] grid_4.3.0 evaluate_0.20 jquerylib_0.1.4
## [13] fastmap_1.1.1 yaml_2.3.7 bookdown_0.33
## [16] BiocManager_1.30.20 compiler_4.3.0 codetools_0.2-19
## [19] Rcpp_1.0.10 lattice_0.21-8 digest_0.6.31
## [22] R6_2.5.1 parallel_4.3.0 bslib_0.4.2
## [25] Matrix_1.5-4 tools_4.3.0 BiocGenerics_0.46.0
## [28] cachem_1.0.7