rnndescent is an R package for finding approximate
nearest neighbors, based heavily on the Python package PyNNDescent by Leland McInnes, but is a fully
independent reimplementation written in C++. It uses the following
techniques:
- Initialization by creating a forest of random project trees (Dasgupta and Freund 2008).
- Optimization by using nearest neighbor descent (Dong, Moses, and Li 2011).
- For building a search graph, graph diversification techniques from FANNG (Harwood and Drummond 2016).
- For querying new data, the back-tracking search from NGT (Iwasaki and Miyazaki 2018) (without dynamic degree-adjustment).
The easiest way to find k-nearest neighbors and query new data is to
use the rnnd_knn function, which combine several of the
available techniques into sensible defaults use the
rnnd_build and rnnd_query functions. For
greater flexibility, the underlying functions used by
rnnd_build and rnnd_query can be used
directly. The other vignettes in this package describe their use and go
into more detail about the how the methods work.
Find the k-nearest neighbors
If you just want the k-nearest neighbors of some data, use
rnnd_knn:
iris_knn <- rnnd_knn(data = iris, k = 5)The Neighbor Graph Format
The nearest neighbor graph format returned by all functions in this package is a list of two matrices:
-
idx– a matrix of indices of the nearest neighbors. As usual in R, these are 1-indexed. -
dist– the equivalent distances.
lapply(iris_knn, function(x) {
head(x, 3)
})
#> $idx
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 18 29 5 28
#> [2,] 2 13 46 35 10
#> [3,] 3 48 4 7 13
#>
#> $dist
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 0.1000000 0.1414212 0.1414212 0.1414213
#> [2,] 0 0.1414213 0.1414213 0.1414213 0.1732050
#> [3,] 0 0.1414213 0.2449490 0.2645751 0.2645753Build an Index
rnnd_knn returns the k-nearest neighbors, but does not
return any “index” that you can use to query new data. To do that, use
rnnd_build. Normally you would query the index with
different from that which you used to build the index, so let’s split
iris up:
iris_even <- iris[seq_len(nrow(iris)) %% 2 == 0, ]
iris_odd <- iris[seq_len(nrow(iris)) %% 2 == 1, ]
iris_index <- rnnd_build(iris_even, k = 5)The index is also a list but with a lot more components (none of
which are intended for manual examination), apart from the the neighbor
graph which can be found under the graph component in the
same format as the return value of rnnd_knn:
lapply(iris_index$graph, function(x) {
head(x, 3)
})
#> $idx
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 23 5 13 18
#> [2,] 2 24 15 23 5
#> [3,] 3 10 11 17 14
#>
#> $dist
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 0.1414213 0.1732050 0.2236068 0.3000000
#> [2,] 0 0.1414215 0.1732051 0.2645753 0.3162279
#> [3,] 0 0.3872986 0.4123107 0.4795830 0.5291505Be aware that for large and high-dimensional data, the returned index
can get very large, especially if you set
n_search_trees to a large value.
Querying Data
To query new data, use rnnd_query:
iris_odd_nn <- rnnd_query(
index = iris_index,
query = iris_odd,
k = 5
)
lapply(iris_odd_nn, function(x) {
head(x, 3)
})
#> $idx
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 9 14 20 4 25
#> [2,] 24 2 23 15 1
#> [3,] 19 9 4 20 14
#>
#> $dist
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.1000000 0.1414213 0.1414213 0.1732050 0.2236068
#> [2,] 0.1414213 0.2449490 0.2645753 0.3000001 0.3000002
#> [3,] 0.1414213 0.1732050 0.2236066 0.2449488 0.2449488You don’t need to keep the data that was used to build the index around, because internally, the index stores that (that’s one of the reasons the index can get large).
Another use for rnnd_query is to improve the quality of
a k-nearest neighbor graph. We are using for a query the
same data we used to build iris_index and specifying via
the init parameter the knn graph we already generated:
iris_knn_improved <- rnnd_query(
index = iris_index,
query = iris_even,
init = iris_index$graph,
k = 5
)If the k-nearest neighbor graph in index$graph isn’t
sufficiently high quality, then result of running
rnnd_query on the same data should be an improvement.
Exactly how much better is hard to say, but you can always compare the
sum of the distances:
In this case, the initial knn has not been improved, which is hardly
surprising due to the size of the dataset. Another function that might
be of use is the neighbor_overlap function to see how many
neighbors are shared between the two graphs:
neighbor_overlap(iris_index$graph, iris_knn_improved)
#> [1] 1As there was no change to the graph, the overlap is 100%. More details on this can be found in the hubness vignette and a more ambitious dataset is covered in the FMNIST article.
Parallelism
rnndescent is multi-threaded, but by default is
single-threaded. Set n_threads to set the number of threads
you want to use:
iris_index <- rnnd_build(data = iris_even, k = 5, n_threads = 2)Available Metrics
Several different distances are available in rnndescent
beyond the typically-supported Euclidean and Cosine-based distances in
other nearest neighbor packages. See the metrics vignette for more details.
Supported Data Types
- Dense matrices and data frames.
- Sparse matrices, in the
dgCMatrix. All the same distances are supported as for dense matrices. - Additionally, for dense binary data, if you supply it as a
logicalmatrix, then for certain distances intended for binary data, specialized functions will be used to speed up the computation.
Parameters
There are several options that rnnd_build and
rnnd_query expose that can be modified to change the
behavior of the different stages of the algorithm. See the documentation
for those functions (e.g. ?rnnd_build) or the Random Partition Forests, Nearest Neighbor Descent and Querying Data vignettes for more
details.