BiocNeighbors 1.8.0
The BiocNeighbors package provides several algorithms for approximate neighbor searches:
These methods complement the exact algorithms described previously.
Again, it is straightforward to switch from one algorithm to another by simply changing the BNPARAM argument in findKNN and queryKNN.
We perform the k-nearest neighbors search with the Annoy algorithm by specifying BNPARAM=AnnoyParam().
nobs <- 10000
ndim <- 20
data <- matrix(runif(nobs*ndim), ncol=ndim)
fout <- findKNN(data, k=10, BNPARAM=AnnoyParam())
head(fout$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 8584 3942 1711 2359 4200 6331 561 4329 5708 3803
## [2,] 7924 5792 8419 3740 920 5103 5852 7777 7011 5669
## [3,] 9756 2517 4308 6564 822 8126 4162 497 8632 2656
## [4,] 2063 1679 5260 9437 3510 3585 7202 9278 716 5452
## [5,] 6947 5654 9142 4882 8038 2318 7935 8963 396 8464
## [6,] 3856 1723 2288 6001 7079 9658 8187 1679 4700 9871
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9785542 0.9937828 0.9974682 1.0311086 1.0537837 1.0600865 1.0702299
## [2,] 0.9445717 0.9981147 1.0318105 1.0446899 1.0486889 1.0545481 1.0545697
## [3,] 0.8897178 0.9314029 0.9426289 1.0321267 1.0487431 1.0595485 1.0612212
## [4,] 0.8848239 0.9776480 0.9812904 0.9933047 1.0061167 1.0202012 1.0442240
## [5,] 0.8590254 1.0091568 1.0164609 1.0269836 1.0304595 1.0316750 1.0341054
## [6,] 0.8624543 0.8903279 0.9003656 0.9090142 0.9092635 0.9480212 0.9584342
## [,8] [,9] [,10]
## [1,] 1.0708619 1.0742493 1.0764540
## [2,] 1.0652611 1.0881760 1.0938987
## [3,] 1.0615385 1.0625939 1.0665069
## [4,] 1.0445368 1.0677571 1.0711876
## [5,] 1.0761930 1.0767903 1.0863526
## [6,] 0.9694126 0.9704188 0.9780809
We can also identify the k-nearest neighbors in one dataset based on query points in another dataset.
nquery <- 1000
ndim <- 20
query <- matrix(runif(nquery*ndim), ncol=ndim)
qout <- queryKNN(data, query, k=5, BNPARAM=AnnoyParam())
head(qout$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 9618 115 9893 3345 540
## [2,] 6628 9246 8889 9068 9166
## [3,] 4498 791 2105 113 2994
## [4,] 5805 5270 7929 179 9037
## [5,] 4913 4837 5209 4451 4154
## [6,] 3726 6298 8561 5285 1615
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9300495 0.9845788 1.0282300 1.0416820 1.0553656
## [2,] 0.9036013 0.9143441 0.9365810 0.9747946 0.9785217
## [3,] 0.8599398 0.9458985 0.9963076 1.0066876 1.0082071
## [4,] 0.9643821 0.9886798 1.0625366 1.0658094 1.0951591
## [5,] 0.8982796 0.9959829 1.0505439 1.0590628 1.0673671
## [6,] 0.9704359 0.9718943 1.0704929 1.0744665 1.1003007
It is similarly easy to use the HNSW algorithm by setting BNPARAM=HnswParam().
Most of the options described for the exact methods are also applicable here. For example:
subset to identify neighbors for a subset of points.get.distance to avoid retrieving distances when unnecessary.BPPARAM to parallelize the calculations across multiple workers.BNINDEX to build the forest once for a given data set and re-use it across calls.The use of a pre-built BNINDEX is illustrated below:
pre <- buildIndex(data, BNPARAM=AnnoyParam())
out1 <- findKNN(BNINDEX=pre, k=5)
out2 <- queryKNN(BNINDEX=pre, query=query, k=2)
Both Annoy and HNSW perform searches based on the Euclidean distance by default.
Searching by Manhattan distance is done by simply setting distance="Manhattan" in AnnoyParam() or HnswParam().
Users are referred to the documentation of each function for specific details on the available arguments.
Both Annoy and HNSW generate indexing structures - a forest of trees and series of graphs, respectively -
that are saved to file when calling buildIndex().
By default, this file is located in tempdir()1 On HPC file systems, you can change TEMPDIR to a location that is more amenable to concurrent access. and will be removed when the session finishes.
AnnoyIndex_path(pre)
## [1] "/tmp/RtmpbwZrPR/file5410fe6e783.idx"
If the index is to persist across sessions, the path of the index file can be directly specified in buildIndex.
This can be used to construct an index object directly using the relevant constructors, e.g., AnnoyIndex(), HnswIndex().
However, it becomes the responsibility of the user to clean up any temporary indexing files after calculations are complete.
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocNeighbors_1.8.0 knitr_1.30 BiocStyle_2.18.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 bookdown_0.21 lattice_0.20-41
## [4] digest_0.6.27 grid_4.0.3 stats4_4.0.3
## [7] magrittr_1.5 evaluate_0.14 rlang_0.4.8
## [10] stringi_1.5.3 S4Vectors_0.28.0 Matrix_1.2-18
## [13] rmarkdown_2.5 BiocParallel_1.24.0 tools_4.0.3
## [16] stringr_1.4.0 parallel_4.0.3 xfun_0.18
## [19] yaml_2.2.1 compiler_4.0.3 BiocGenerics_0.36.0
## [22] BiocManager_1.30.10 htmltools_0.5.0