rpf_knn_query {rnndescent}

Query a random projection forest index for nearest neighbors

Description

Run queries against a "forest" of Random Projection Trees (Dasgupta and Freund, 2008), to return nearest neighbors taken from the reference data used to build the forest.

Usage

rpf_knn_query(
  query,
  reference,
  forest,
  k,
  cache = TRUE,
  n_threads = 0,
  verbose = FALSE,
  obs = "R"
)

Arguments

query

Matrix of n query items, with observations in the rows and features in the columns. Optionally, the data may be passed with the observations in the columns, by setting obs = "C", which should be more efficient. The reference data must be passed in the same orientation as query. Possible formats are base::data.frame(), base::matrix() or Matrix::sparseMatrix(). Sparse matrices should be in dgCMatrix format. Dataframes will be converted to numerical matrix format internally, so if your data columns are logical and intended to be used with the specialized binary metrics, you should convert it to a logical matrix first (otherwise you will get the slower dense numerical version).

reference

Matrix of m reference items, with observations in the rows and features in the columns. The nearest neighbors to the queries are calculated from this data and should be the same data used to build the forest. Optionally, the data may be passed with the observations in the columns, by setting obs = "C", which should be more efficient. The query data must be passed in the same format and orientation as reference. Possible formats are base::data.frame(), base::matrix() or Matrix::sparseMatrix(). Sparse matrices should be in dgCMatrix format.

forest

A random partition forest, created by rpf_build(), representing partitions of the data in reference.

k

Number of nearest neighbors to return. You are unlikely to get good results if you choose a value substantially larger than the value of leaf_size used to build the forest.

cache

if TRUE (the default) then candidate indices found in the leaves of the forest are cached to avoid recalculating the same distance repeatedly. This incurs an extra memory cost which scales with n_threads. Set this to FALSE to disable distance caching.

n_threads

Number of threads to use. Note that the parallelism in the search is done over the observations in query not the trees in the forest. Thus a single observation will not see any speed-up from increasing n_threads.

verbose

If TRUE, log information to the console.

obs

set to "C" to indicate that the input data orientation stores each observation as a column. The default "R" means that observations are stored in each row. Storing the data by row is usually more convenient, but internally your data will be converted to column storage. Passing it already column-oriented will save some memory and (a small amount of) CPU usage.

Value

the approximate nearest neighbor graph as a list containing:

• idx an n by k matrix containing the nearest neighbor indices.

• dist an n by k matrix containing the nearest neighbor distances.

k neighbors per observation are not guaranteed to be found. Missing data is represented with an index of 0 and a distance of NA.

References

Dasgupta, S., & Freund, Y. (2008, May). Random projection trees and low dimensional manifolds. In Proceedings of the fortieth annual ACM symposium on Theory of computing (pp. 537-546). doi:10.1145/1374376.1374452.

See Also

rpf_build()

Examples

# Build a forest of 10 trees from the odd rows
iris_odd <- iris[seq_len(nrow(iris)) %% 2 == 1, ]
iris_odd_forest <- rpf_build(iris_odd, n_trees = 10)

iris_even <- iris[seq_len(nrow(iris)) %% 2 == 0, ]
iris_even_nn <- rpf_knn_query(
  query = iris_even, reference = iris_odd,
  forest = iris_odd_forest, k = 15
)