nnd_knn {rnndescent}

Find nearest neighbors using nearest neighbor descent

Description

Uses the Nearest Neighbor Descent method due to Dong and co-workers (2011) to optimize an approximate nearest neighbor graph.

Usage

nnd_knn(
  data,
  k = NULL,
  metric = "euclidean",
  init = "rand",
  init_args = NULL,
  n_iters = NULL,
  max_candidates = NULL,
  delta = 0.001,
  low_memory = TRUE,
  weight_by_degree = FALSE,
  use_alt_metric = TRUE,
  n_threads = 0,
  verbose = FALSE,
  progress = "bar",
  obs = "R",
  ret_forest = FALSE
)

Arguments

data

Matrix of n items to generate neighbors for, with observations in the rows and features in the columns. Optionally, input can be passed with observations in the columns, by setting obs = "C", which should be more efficient. 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).

k

Number of nearest neighbors to return. Optional if init is specified.

metric

Type of distance calculation to use. One of:

• "braycurtis"

• "canberra"

• "chebyshev"

• "correlation" (1 minus the Pearson correlation)

• "cosine"

• "dice"

• "euclidean"

• "hamming"

• "hellinger"

• "jaccard"

• "jensenshannon"

• "kulsinski"

• "sqeuclidean" (squared Euclidean)

• "manhattan"

• "rogerstanimoto"

• "russellrao"

• "sokalmichener"

• "sokalsneath"

• "spearmanr" (1 minus the Spearman rank correlation)

• "symmetrickl" (symmetric Kullback-Leibler divergence)

• "tsss" (Triangle Area Similarity-Sector Area Similarity or TS-SS metric)

• "yule"

For non-sparse data, the following variants are available with preprocessing: this trades memory for a potential speed up during the distance calculation. Some minor numerical differences should be expected compared to the non-preprocessed versions:

• "cosine-preprocess": cosine with preprocessing.

• "correlation-preprocess": correlation with preprocessing.

For non-sparse binary data passed as a logical matrix, the following metrics have specialized variants which should be substantially faster than the non-binary variants (in other cases the logical data will be treated as a dense numeric vector of 0s and 1s):

• "dice"

• "hamming"

• "jaccard"

• "kulsinski"

• "matching"

• "rogerstanimoto"

• "russellrao"

• "sokalmichener"

• "sokalsneath"

• "yule"

init

Name of the initialization strategy or initial data neighbor graph to optimize. One of:

• "rand" random initialization (the default).

• "tree" use the random projection tree method of Dasgupta and Freund (2008).

• a pre-calculated neighbor graph. A list containing:

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

  • dist (optional) an n by k matrix containing the nearest neighbor distances. If the input distances are omitted, they will be calculated for you.'

If k and init are specified as arguments to this function, and the number of neighbors provided in init is not equal to k then:

• if k is smaller, only the k closest values in init are retained.

• if k is larger, then random neighbors will be chosen to fill init to the size of k. Note that there is no checking if any of the random neighbors are duplicates of what is already in init so effectively fewer than k neighbors may be chosen for some observations under these circumstances.

init_args

a list containing arguments to pass to the random partition forest initialization. See rpf_knn() for possible arguments. To avoid inconsistences with the tree calculation and subsequent nearest neighbor descent optimization, if you attempt to provide a metric or use_alt_metric option in this list it will be ignored.

n_iters

Number of iterations of nearest neighbor descent to carry out. By default, this will be chosen based on the number of observations in data.

max_candidates

Maximum number of candidate neighbors to try for each item in each iteration. Use relative to k to emulate the "rho" sampling parameter in the nearest neighbor descent paper. By default, this is set to k or 60, whichever is smaller.

delta

The minimum relative change in the neighbor graph allowed before early stopping. Should be a value between 0 and 1. The smaller the value, the smaller the amount of progress between iterations is allowed. Default value of 0.001 means that at least 0.1% of the neighbor graph must be updated at each iteration.

low_memory

If TRUE, use a lower memory, but more computationally expensive approach to index construction. If set to FALSE, you should see a noticeable speed improvement, especially when using a smaller number of threads, so this is worth trying if you have the memory to spare.

weight_by_degree

If TRUE, then candidates for the local join are weighted according to their in-degree, so that if there are more than max_candidates in a candidate list, candidates with a smaller degree are favored for retention. This prevents items with large numbers of edges crowding out other items and for high-dimensional data is likely to provide a small improvement in accuracy. Because this incurs a small extra cost of counting the degree of each node, and because it tends to delay early convergence, by default this is FALSE.

use_alt_metric

If TRUE, use faster metrics that maintain the ordering of distances internally (e.g. squared Euclidean distances if using metric = "euclidean"), then apply a correction at the end. Probably the only reason to set this to FALSE is if you suspect that some sort of numeric issue is occurring with your data in the alternative code path.

n_threads

Number of threads to use.

verbose

If TRUE, log information to the console.

progress

Determines the type of progress information logged if verbose = TRUE. Options are:

• "bar": a simple text progress bar.

• "dist": the sum of the distances in the approximate knn graph at the end of each iteration.

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.

ret_forest

If TRUE and init = "tree" then the RP forest used to initialize the nearest neighbors will be returned with the nearest neighbor data. See the Value section for details. The returned forest can be used as part of initializing the search for new data: see rpf_knn_query() and rpf_filter() for more details.

Details

If no initial graph is provided, a random graph is generated, or you may also specify the use of a graph generated from a forest of random projection trees, using the method of Dasgupta and Freund (2008).

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.

• forest (if init = "tree" and ret_forest = TRUE only): the RP forest used to initialize the neighbor data.

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.

Dong, W., Moses, C., & Li, K. (2011, March). Efficient k-nearest neighbor graph construction for generic similarity measures. In Proceedings of the 20th international conference on World Wide Web (pp. 577-586). ACM. doi:10.1145/1963405.1963487.

Examples

# Find 4 (approximate) nearest neighbors using Euclidean distance
# If you pass a data frame, non-numeric columns are removed
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean")

# Manhattan (l1) distance
iris_nn <- nnd_knn(iris, k = 4, metric = "manhattan")

# Multi-threading: you can choose the number of threads to use: in real
# usage, you will want to set n_threads to at least 2
iris_nn <- nnd_knn(iris, k = 4, metric = "manhattan", n_threads = 1)

# Use verbose flag to see information about progress
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean", verbose = TRUE)

# Nearest neighbor descent uses random initialization, but you can pass any
# approximation using the init argument (as long as the metrics used to
# calculate the initialization are compatible with the metric options used
# by nnd_knn).
iris_nn <- random_knn(iris, k = 4, metric = "euclidean")
iris_nn <- nnd_knn(iris, init = iris_nn, metric = "euclidean", verbose = TRUE)

# Number of iterations controls how much optimization is attempted. A smaller
# value will run faster but give poorer results
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean", n_iters = 2)

# You can also control the amount of work done within an iteration by
# setting max_candidates
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean", max_candidates = 50)

# Optimization may also stop early if not much progress is being made. This
# convergence criterion can be controlled via delta. A larger value will
# stop progress earlier. The verbose flag will provide some information if
# convergence is occurring before all iterations are carried out.
set.seed(1337)
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean", n_iters = 5, delta = 0.5)

# To ensure that descent only stops if no improvements are made, set delta = 0
set.seed(1337)
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean", n_iters = 5, delta = 0)

# A faster version of the algorithm is available that avoids repeated
# distance calculations at the cost of using more RAM. Set low_memory to
# FALSE to try it.
set.seed(1337)
iris_nn <- nnd_knn(iris, k = 4, metric = "euclidean", low_memory = FALSE)

# Using init = "tree" is usually more efficient than random initialization.
# arguments to the tree initialization method can be passed via the init_args
# list
set.seed(1337)
iris_nn <- nnd_knn(iris, k = 4, init = "tree", init_args = list(n_trees = 5))