BiocNeighbors 1.2.0
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$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 53 5353 2023 9931 2760 994 2604 7819 193 5814
## [2,] 8586 8981 2517 30 5372 9505 2323 2509 4531 2725
## [3,] 8854 9155 1583 3675 1517 8073 2266 6583 9844 8169
## [4,] 1471 6059 5283 5085 5558 5117 2252 8044 1832 7740
## [5,] 4140 8087 5650 8297 1478 2883 7849 1196 1603 7392
## [6,] 5630 8636 1413 1497 8811 4017 4451 5918 308 7238
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.8884642 0.9403174 0.9653104 1.0295044 1.0579624 1.0800753 1.0907043
## [2,] 0.8462005 0.8791326 0.9770808 1.0681456 1.0965602 1.1017785 1.1087655
## [3,] 0.9260832 0.9414257 0.9436049 0.9547907 0.9627618 0.9983353 1.0060830
## [4,] 0.7942638 0.9342518 0.9665577 0.9737488 0.9789218 0.9791681 0.9900901
## [5,] 0.8463366 0.8486429 0.8935444 0.9395248 0.9626724 0.9695854 0.9758337
## [6,] 0.9866148 1.0038661 1.0106814 1.0253493 1.0294634 1.0418093 1.0592244
## [,8] [,9] [,10]
## [1,] 1.1039586 1.1043444 1.1120301
## [2,] 1.1115894 1.1366431 1.1477174
## [3,] 1.0140244 1.0210154 1.0217787
## [4,] 0.9931630 0.9944342 1.0029531
## [5,] 0.9780093 0.9782090 0.9799659
## [6,] 1.0645270 1.0812619 1.0826969
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$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 3716 4025 1881 7406 1396
## [2,] 8143 5213 5877 3956 2456
## [3,] 1309 3117 6457 4684 2083
## [4,] 1267 6694 621 2222 8358
## [5,] 9348 8161 448 3750 76
## [6,] 2257 2699 5238 769 4032
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.8983552 0.9042156 0.9361026 0.9491780 0.9586194
## [2,] 0.9948943 1.0481845 1.0545416 1.0589106 1.0658462
## [3,] 0.9102096 0.9705096 0.9977661 1.0842166 1.1232716
## [4,] 0.8271732 1.0789361 1.1740106 1.2131128 1.2179416
## [5,] 0.8764364 0.9215204 0.9771883 1.0012633 1.0081911
## [6,] 0.7942141 0.8562478 0.8593587 0.9096819 0.9113994
It is similarly easy to use the HNSW algorithm by setting BNPARAM=HnswParam()
.
Most of the options described for the KMKNN algorithm 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)
Users are referred to the documentation of each function for specific details on the available arguments.
The forest of trees form an indexing structure that is saved to file.
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 parallelized access. and will be removed when the session finishes.
AnnoyIndex_path(pre)
## [1] "/tmp/RtmpP3KLXO/file12efe4c5809cb.idx"
If the index is to persist across sessions, the path of the index file can be directly specified in buildIndex
.
However, this means that it becomes the responsibility of the user to clean up any temporary indexing files after calculations are complete.
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocNeighbors_1.2.0 knitr_1.22 BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 bookdown_0.9 digest_0.6.18
## [4] stats4_3.6.0 magrittr_1.5 evaluate_0.13
## [7] stringi_1.4.3 S4Vectors_0.22.0 rmarkdown_1.12
## [10] BiocParallel_1.18.0 tools_3.6.0 stringr_1.4.0
## [13] parallel_3.6.0 xfun_0.6 yaml_2.2.0
## [16] compiler_3.6.0 BiocGenerics_0.30.0 BiocManager_1.30.4
## [19] htmltools_0.3.6