BiocNeighbors 1.6.0
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$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 2472 8785 4480 2287 7154 5087 306 9725 355 3394
## [2,] 5679 9999 7159 1622 8671 1447 3588 9629 1197 297
## [3,] 9862 8733 1684 7877 4205 1388 3570 1146 3504 10
## [4,] 8520 1375 7533 5937 9217 1923 6576 3922 6534 3078
## [5,] 7475 7014 2667 1272 2384 2656 6914 7399 5816 3233
## [6,] 1076 1939 6881 6542 7726 8623 8700 120 6332 2766
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1.0160896 1.0305198 1.0539002 1.0731078 1.0773553 1.079474 1.0834223
## [2,] 0.9352278 0.9359603 0.9668155 0.9747400 0.9870442 1.019455 1.0362035
## [3,] 0.8528845 0.9201739 0.9223238 0.9334182 0.9353036 0.959167 0.9967918
## [4,] 0.8827361 0.9544235 0.9728699 1.0183616 1.0454656 1.067823 1.0709544
## [5,] 0.8144282 0.8833866 0.9234329 0.9283065 0.9300396 1.002293 1.0083969
## [6,] 0.9313099 0.9518151 0.9607627 0.9678324 1.0065429 1.022031 1.0428864
## [,8] [,9] [,10]
## [1,] 1.084278 1.114447 1.116229
## [2,] 1.055618 1.071051 1.079966
## [3,] 1.001344 1.013241 1.029634
## [4,] 1.079332 1.102296 1.107838
## [5,] 1.029544 1.035694 1.036367
## [6,] 1.058502 1.072493 1.084400
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$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 9284 9570 1140 6935 7038
## [2,] 9412 1858 5012 9139 2021
## [3,] 4960 657 9308 8547 798
## [4,] 9404 4924 1916 7117 5605
## [5,] 163 807 2996 2809 9403
## [6,] 5270 2339 7438 4378 9005
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9304292 0.9459063 1.0207646 1.0232471 1.025561
## [2,] 0.8957567 0.9605144 1.0573766 1.0687405 1.092705
## [3,] 0.9340398 0.9353478 0.9844260 1.0137274 1.057676
## [4,] 0.9156544 0.9845881 1.0024112 1.0515140 1.055231
## [5,] 0.9132901 0.9353450 0.9453691 1.0402807 1.040792
## [6,] 0.8896392 0.8957989 0.9428604 0.9937068 1.045079
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/RtmpmRlYuW/file5485745454c1.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 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-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_1.6.0 knitr_1.28 BiocStyle_2.16.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 bookdown_0.18 lattice_0.20-41
## [4] digest_0.6.25 grid_4.0.0 stats4_4.0.0
## [7] magrittr_1.5 evaluate_0.14 rlang_0.4.5
## [10] stringi_1.4.6 S4Vectors_0.26.0 Matrix_1.2-18
## [13] rmarkdown_2.1 BiocParallel_1.22.0 tools_4.0.0
## [16] stringr_1.4.0 parallel_4.0.0 xfun_0.13
## [19] yaml_2.2.1 compiler_4.0.0 BiocGenerics_0.34.0
## [22] BiocManager_1.30.10 htmltools_0.4.0