queryKNN methods {BiocNeighbors} | R Documentation

## Query nearest neighbors

### Description

Query a dataset for nearest neighbors of points in another dataset, using a variety of algorithms.

### Usage

    queryKmknn(X, query, k, get.index=TRUE, get.distance=TRUE, last=k,
        BPPARAM=SerialParam(), precomputed=NULL, transposed=FALSE, subset=NULL,
        raw.index=FALSE, warn.ties=TRUE, ...)

    queryVptree(X, query, k, get.index=TRUE, get.distance=TRUE, last=k,
        BPPARAM=SerialParam(), precomputed=NULL, transposed=FALSE, subset=NULL,
        raw.index=FALSE, warn.ties=TRUE, ...)

    queryAnnoy(X, query, k, get.index=TRUE, get.distance=TRUE, last=k,
        BPPARAM=SerialParam(), precomputed=NULL, transposed=FALSE, subset=NULL,
        raw.index=NA, warn.ties=NA, ...)

    queryHnsw(X, query, k, get.index=TRUE, get.distance=TRUE, last=k,
        BPPARAM=SerialParam(), precomputed=NULL, transposed=FALSE, subset=NULL,
        raw.index=NA, warn.ties=NA, ...)

### Arguments

`X` | A numeric matrix where rows correspond to data points and columns correspond to variables (i.e., dimensions).
`query` | A numeric matrix of query points, containing different data points in the rows but the same number and ordering of dimensions in the columns.
`k` | A positive integer scalar specifying the number of nearest neighbors to retrieve.
`get.index` | A logical scalar indicating whether the indices of the nearest neighbors should be recorded.
`get.distance` | A logical scalar indicating whether distances to the nearest neighbors should be recorded.
`last` | An integer scalar specifying the number of furthest neighbors for which statistics should be returned.
`BPPARAM` | A BiocParallelParam object indicating how the search should be parallelized.
`precomputed` | A BiocNeighborIndex object of the appropriate class, generated from `X`. For `queryKmknn`, this should be a KmknnIndex from `buildKmknn`; for `queryVptree`, this should be a VptreeIndex from `buildVptree`; for `queryAnnoy`, this should be a AnnoyIndex from `buildAnnoy`; and for `queryHnsw`, this should be a HnswIndex from `buildHnsw`.
`transposed` | A logical scalar indicating whether the `query` is transposed, in which case `query` is assumed to contain dimensions in the rows and data points in the columns.
`subset` | A vector indicating the rows of `query` (or columns, if `transposed=TRUE`) for which the nearest neighbors should be identified.
`raw.index` | A logial scalar indicating whether raw column indices should be returned, see `?"BiocNeighbors-raw-index"`. This argument is ignored for `queryAnnoy` and `queryHnsw`.
`warn.ties` | Logical scalar indicating whether a warning should be raised if any of the `k+1` neighbors have tied distances. This argument is ignored for `queryAnnoy` and `queryHnsw`.
`...` | Further arguments to pass to the respective `build*` function for each algorithm. This includes `distance`, a string specifying whether `"Euclidean"` or `"Manhattan"` distances are to be used.

### Details

All of these functions identify points in `X` that are the `k` nearest neighbors of each point in `query`. `queryAnnoy` performs an approximate search, while `queryKmknn` and `queryVptree` are exact. This requires both `X` and `query` to have the same number of dimensions. Moreover, the upper bound for `k` is set at the number of points in `X`.

By default, nearest neighbors are identified for all data points within `query`. If `subset` is specified, nearest neighbors are only detected for the query points in the subset. This yields the same result as (but is more efficient than) subsetting the output matrices after running `queryKmknn` on the full `query`.

If `transposed=TRUE`, this function assumes that `query` is already transposed, which saves a bit of time by avoiding an unnecessary transposition. Turning off `get.index` or `get.distance` may also provide a slight speed boost when these returned values are not of interest. Using `BPPARAM` will also split the search by query points across multiple processes.

Setting `last` will return indices and/or distances for the `k - last + 1`-th closest neighbor to the `k`-th neighbor. This can be used to improve memory efficiency, e.g., by only returning statistics for the `k`-th nearest neighbor by setting `last=1`. Note that this is entirely orthogonal to `subset`.

If multiple queries are to be performed to the same `X`, it may be beneficial to build the index from `X` (e.g., with `buildKmknn`). The resulting BiocNeighborIndex object can be supplied as `precomputed` to multiple function calls, avoiding the need to repeat index construction in each call. Note that when `precomputed` is supplied, the value of `X` is ignored.

For exact methods, see comments in `?"BiocNeighbors-ties"` regarding the warnings when tied distances are observed. For approximate methods, see comments in `buildAnnoy` and `buildHnsw` about the (lack of) randomness in the search results.

### Value

A list is returned containing:

- `index`, if `get.index=TRUE`. This is an integer matrix where each row corresponds to a point (denoted here as i) in `query`. The row for i contains the row indices of `X` that are the nearest neighbors to point i, sorted by increasing distance from i.

- `distance`, if `get.distance=TRUE`. This is a numeric matrix where each row corresponds to a point (as above) and contains the sorted distances of the neighbors from i.

Each matrix contains `last` columns. If `subset` is not `NULL`, each row of the above matrices refers to a point in the subset, in the same order as supplied in `subset`.

See `?"BiocNeighbors-raw-index"` for an explanation of the output when `raw.index=TRUE` for the functions that support it.

### Author(s)

Aaron Lun

### See Also

`buildKmknn`, `buildVptree`, or `buildAnnoy` to build an index ahead of time.

See `?"BiocNeighbors-algorithms"` for an overview of the available algorithms.

### Examples

    Y <- matrix(rnorm(100000), ncol=20)
    Z <- matrix(rnorm(20000), ncol=20)
    out <- queryKmknn(Y, query=Z, k=5)
    head(out$index)
    head(out$distance)

    out2 <- queryVptree(Y, query=Z, k=5)
    head(out2$index)
    head(out2$distance)

    out3 <- queryAnnoy(Y, query=Z, k=5)
    head(out3$index)
    head(out3$distance)

    out4 <- queryHnsw(Y, query=Z, k=5)
    head(out4$index)
    head(out4$distance)

[Package BiocNeighbors version 1.4.2 Index]