1 Introduction

The BiocNeighbors package provides several algorithms for approximate neighbor searches:

  • The Annoy (Approximate Nearest Neighbors Oh Yeah) method uses C++ code from the RcppAnnoy package. It works by building a tree where a random hyperplane partitions a group of points into two child groups at each internal node. This is repeated to construct a forest of trees where the number of trees determines the accuracy of the search. Given a query data point, we identify all points in the same leaf node for each tree. We then take the union of leaf node sets across trees and search them exactly for the nearest neighbors.
  • The HNSW (Hierarchical Navigable Small Worlds) method uses C++ code from the RcppHNSW package. It works by building a series of nagivable small world graphs containing links between points across the entire data set. The algorithm walks through the graphs where each step is chosen to move closer to a given query point. Different graphs contain links of different lengths, yielding a hierarchy where earlier steps are large and later steps are small. The accuracy of the search is determined by the connectivity of the graphs and the size of the intermediate list of potential neighbors.

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.

2 Identifying nearest neighbors

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$index)
##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 3015 4384 4696 3450 1505 3955 1962 3551 5277  7093
## [2,]  397 5322 2177 3703 9722 6857 3372  828 5178  2914
## [3,] 5397 5818 6738 9131 5548 8078 8678 5437 6376  4808
## [4,] 3463 8749 6409 7166 1714 4153 3234 7073 5505   837
## [5,] 9744 6985 8646 2594 5398 4185 7497 5317 1666  9247
## [6,]   59  696 3283 1202 5466 9526 1198 3694 7064  3665
head(fout$distance)
##           [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]
## [1,] 0.9341923 0.9527923 0.9647438 0.9769915 0.9836699 0.9871061 1.0030521
## [2,] 0.8119189 0.8634843 0.8650023 0.9214678 0.9227217 0.9279625 0.9305518
## [3,] 0.7730708 0.8406392 0.9079637 0.9366356 0.9759469 1.0036047 1.0226767
## [4,] 1.0311872 1.0526839 1.0882136 1.1277175 1.1401669 1.1420376 1.1517972
## [5,] 0.9501674 0.9572392 0.9841415 1.0178292 1.0300089 1.0811498 1.0919371
## [6,] 0.9044073 0.9194801 0.9752643 0.9846420 0.9871848 1.0118369 1.0177381
##           [,8]     [,9]     [,10]
## [1,] 1.0335310 1.033556 1.0336419
## [2,] 0.9337065 0.947928 0.9490606
## [3,] 1.0251433 1.034085 1.0367147
## [4,] 1.1562366 1.169030 1.1857411
## [5,] 1.1265150 1.140092 1.1520551
## [6,] 1.0479219 1.067483 1.0843154

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$index)
##      [,1] [,2] [,3] [,4] [,5]
## [1,] 5418 3697 1072 3493  219
## [2,] 7131 9200 3955 8575  372
## [3,] 4945 5286 5409 7741  382
## [4,] 9611 9366 9124 5016 3401
## [5,] 7495 3555 8239 5163 4434
## [6,]  896 1822 6077 7666  891
head(qout$distance)
##           [,1]      [,2]      [,3]      [,4]      [,5]
## [1,] 0.9707904 1.0662892 1.0664977 1.0710768 1.0729846
## [2,] 0.9964840 0.9966896 1.0007871 1.0154825 1.0182202
## [3,] 0.9536263 0.9846544 0.9889048 1.0100797 1.0276216
## [4,] 0.8351753 0.8431612 0.8885603 0.9292682 1.0337896
## [5,] 0.9119824 0.9192331 0.9487250 0.9638794 0.9803079
## [6,] 0.8751767 0.9048310 0.9093602 0.9205429 0.9367550

It is similarly easy to use the HNSW algorithm by setting BNPARAM=HnswParam().

3 Further options

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.

4 Saving the index files

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/RtmpkbI6iO/file13e4c51b84413.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.

5 Session information

sessionInfo()
## R version 4.4.0 Patched (2024-04-24 r86482)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.6.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] BiocNeighbors_1.21.2 knitr_1.46           BiocStyle_2.31.0    
## 
## loaded via a namespace (and not attached):
##  [1] cli_3.6.2           rlang_1.1.3         xfun_0.43          
##  [4] jsonlite_1.8.8      S4Vectors_0.41.7    htmltools_0.5.8.1  
##  [7] stats4_4.4.0        sass_0.4.9          rmarkdown_2.26     
## [10] grid_4.4.0          evaluate_0.23       jquerylib_0.1.4    
## [13] fastmap_1.1.1       yaml_2.3.8          lifecycle_1.0.4    
## [16] bookdown_0.39       BiocManager_1.30.22 compiler_4.4.0     
## [19] codetools_0.2-20    Rcpp_1.0.12         BiocParallel_1.37.1
## [22] lattice_0.22-6      digest_0.6.35       R6_2.5.1           
## [25] parallel_4.4.0      bslib_0.7.0         Matrix_1.7-0       
## [28] tools_4.4.0         BiocGenerics_0.49.1 cachem_1.0.8