BiocNeighbors 1.20.0
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$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 8367 8024 412 7452 4986 8059 8767 5863 5568 9170
## [2,] 7505 5084 4941 9248 8300 2767 2588 5815 2414 6779
## [3,] 1423 6033 4754 5243 1063 7402 5736 569 2958 2372
## [4,] 4099 6815 4468 4392 8794 2570 9754 4784 9030 4412
## [5,] 581 2481 8556 6514 4270 6759 2803 4132 5325 7112
## [6,] 8144 6697 3402 9300 5248 3157 7580 2971 1667 1060
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9171678 0.9425159 0.9431646 0.9572297 0.9644480 0.9805946 1.0174010
## [2,] 0.9621327 0.9670848 0.9929832 0.9993303 1.0228894 1.0396564 1.0438344
## [3,] 0.8206025 0.8798296 0.8804766 0.9020995 0.9048094 0.9160107 0.9232739
## [4,] 0.8161841 0.8854532 0.9174184 0.9401400 0.9851396 0.9930533 0.9965966
## [5,] 1.0427125 1.0645164 1.0669452 1.0683836 1.1139536 1.1449358 1.1469938
## [6,] 0.7417828 0.9002681 0.9140049 0.9155242 0.9189631 0.9382798 0.9427933
## [,8] [,9] [,10]
## [1,] 1.0249903 1.0295681 1.0335536
## [2,] 1.0509665 1.0897555 1.0998900
## [3,] 0.9412209 0.9614992 0.9652658
## [4,] 1.0189202 1.0367405 1.0378553
## [5,] 1.1491128 1.1525401 1.1539816
## [6,] 0.9929794 0.9986178 1.0044509
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$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 6822 4426 1715 7508 2464
## [2,] 957 5985 2454 2068 3042
## [3,] 9306 7186 6908 9102 7949
## [4,] 866 6060 1349 6982 8261
## [5,] 8861 443 188 3160 1484
## [6,] 9055 4826 3262 5514 6679
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9289543 1.0253005 1.0686672 1.0935981 1.1488045
## [2,] 0.7525633 0.8865743 0.8985186 0.9768201 0.9948143
## [3,] 0.9024137 0.9536997 1.0628147 1.0737333 1.0961367
## [4,] 0.8370829 0.9533898 0.9977849 1.0355099 1.0538019
## [5,] 0.7983236 0.8354070 0.8403973 0.8904105 0.9825965
## [6,] 0.9309431 1.0334160 1.0844851 1.1001310 1.1003900
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/RtmpesdGhm/fileb34145b0b1486.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 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## 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
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocNeighbors_1.20.0 knitr_1.44 BiocStyle_2.30.0
##
## 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 BiocParallel_1.36.0 lattice_0.22-5
## [22] digest_0.6.33 R6_2.5.1 parallel_4.3.1
## [25] bslib_0.5.1 Matrix_1.6-1.1 tools_4.3.1
## [28] BiocGenerics_0.48.0 cachem_1.0.8