BiocNeighbors 1.20.0
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$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 2065 3382 9533 6800 7401 6027 8114 2676 3376 2115
## [2,] 5440 2966 9413 8952 8321 8963 6158 2420 3812 134
## [3,] 8774 9799 367 2519 6234 9930 4373 6345 8692 8965
## [4,] 2148 9200 5277 7036 7967 6239 7887 1658 4406 4455
## [5,] 3999 9006 353 9666 7327 5542 5491 4161 7471 1536
## [6,] 8447 7364 1229 3462 2859 5880 8753 7978 3477 5057
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.8601041 0.9405645 0.9441125 0.9635423 0.9694718 0.9796962 0.9873729
## [2,] 0.8796636 0.8815487 1.0410194 1.0890603 1.0928391 1.1079828 1.1171982
## [3,] 0.8381739 0.9027989 1.0481477 1.0529239 1.0538338 1.0565367 1.0602912
## [4,] 0.8008537 0.9552288 0.9555032 0.9755692 0.9805155 0.9818309 1.0077884
## [5,] 1.0846477 1.0980233 1.1084281 1.1473471 1.1496366 1.1501803 1.1510255
## [6,] 0.8706205 0.9055222 0.9159952 0.9233606 0.9338148 0.9814867 0.9859933
## [,8] [,9] [,10]
## [1,] 1.0025652 1.005314 1.038677
## [2,] 1.1173337 1.132791 1.134128
## [3,] 1.0632358 1.075982 1.085719
## [4,] 1.0262845 1.051799 1.062860
## [5,] 1.1538075 1.156275 1.159527
## [6,] 0.9966103 1.004058 1.005432
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$index[3,]
## [1] 8774 9799 367 2519 6234 9930 4373 6345 8692 8965
… with the following distances to those neighbors:
fout$distance[3,]
## [1] 0.8381739 0.9027989 1.0481477 1.0529239 1.0538338 1.0565367 1.0602912
## [8] 1.0632358 1.0759820 1.0857192
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$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2386 4418 6449 5340 2518
## [2,] 6158 4949 729 7451 8321
## [3,] 2234 4236 3897 2139 1332
## [4,] 9255 6489 4716 9554 5314
## [5,] 8740 9926 2242 34 1405
## [6,] 2507 3074 6277 6389 7293
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.7603441 0.8482767 0.8776201 0.8933037 0.9188237
## [2,] 0.8588626 0.8873143 0.9607321 0.9699190 1.0059270
## [3,] 0.8563127 0.9349425 0.9768696 0.9887398 1.0160313
## [4,] 0.8235188 0.9186935 0.9253849 0.9547868 0.9610018
## [5,] 0.8146578 0.8711415 0.9147830 0.9238796 0.9280706
## [6,] 0.9415215 1.0382651 1.0693572 1.1052094 1.1185167
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$index[3,]
## [1] 2234 4236 3897 2139 1332
… with the following distances to those neighbors:
qout$distance[3,]
## [1] 0.8563127 0.9349425 0.9768696 0.9887398 1.0160313
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,] 8774 9799 367 2519 6234
## [2,] 2148 9200 5277 7036 7967
## [3,] 3999 9006 353 9666 7327
##
## $distance
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.8381739 0.9027989 1.0481477 1.0529239 1.0538338
## [2,] 0.8008537 0.9552288 0.9555032 0.9755692 0.9805155
## [3,] 1.0846477 1.0980233 1.1084281 1.1473471 1.1496366
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 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## time zone: America/New_York
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] BiocParallel_1.36.0 BiocNeighbors_1.20.0 knitr_1.44
## [4] BiocStyle_2.30.0
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## loaded via a namespace (and not attached):
## [1] cli_3.6.1 rlang_1.1.1 xfun_0.40
## [4] jsonlite_1.8.7 S4Vectors_0.40.0 htmltools_0.5.6.1
## [7] stats4_4.3.1 sass_0.4.7 rmarkdown_2.25
## [10] grid_4.3.1 evaluate_0.22 jquerylib_0.1.4
## [13] fastmap_1.1.1 yaml_2.3.7 bookdown_0.36
## [16] BiocManager_1.30.22 compiler_4.3.1 codetools_0.2-19
## [19] Rcpp_1.0.11 lattice_0.22-5 digest_0.6.33
## [22] R6_2.5.1 parallel_4.3.1 bslib_0.5.1
## [25] Matrix_1.6-1.1 tools_4.3.1 BiocGenerics_0.48.0
## [28] cachem_1.0.8
Wang, X. 2012. “A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.” Proc Int Jt Conf Neural Netw 43 (6): 2351–8.
Yianilos, P. N. 1993. “Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces.” In SODA, 93:311–21. 194.