rnnd_build {rnndescent}

Build approximate nearest neighbors index and neighbor graph

Description

This function builds an approximate nearest neighbors graph with convenient defaults, then prepares the index for querying new data, for later use with rnnd_query(). For more control over the process, please see the other functions in the package.

Usage

rnnd_build(
  data,
  k = 30,
  metric = "euclidean",
  use_alt_metric = TRUE,
  init = "tree",
  n_trees = NULL,
  leaf_size = NULL,
  max_tree_depth = 200,
  margin = "auto",
  n_iters = NULL,
  delta = 0.001,
  max_candidates = NULL,
  low_memory = TRUE,
  weight_by_degree = FALSE,
  n_search_trees = 1,
  pruning_degree_multiplier = 1.5,
  diversify_prob = 1,
  prune_reverse = FALSE,
  n_threads = 0,
  verbose = FALSE,
  progress = "bar",
  obs = "R"
)

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 build the index for. You can specify a different number when running rnnd_query, but the index is calibrated using this value so it's recommended to set k according to the likely number of neighbors you will want to retrieve. Optional if init is specified, in which case k can be inferred from the init data. If you do both, then the specified version of k will take precedence.

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"

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.

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.

n_trees

The number of trees to use in the RP forest. A larger number will give more accurate results at the cost of a longer computation time. The default of NULL means that the number is chosen based on the number of observations in data. Only used if init = "tree".

leaf_size

The maximum number of items that can appear in a leaf. This value should be chosen to match the expected number of neighbors you will want to retrieve when running queries (e.g. if you want find 50 nearest neighbors set leaf_size = 50) and should not be set to a value smaller than 10. Only used if init = "tree".

max_tree_depth

The maximum depth of the tree to build (default = 200). If the maximum tree depth is exceeded then the leaf size of a tree may exceed leaf_size which can result in a large number of neighbor distances being calculated. If verbose = TRUE a message will be logged to indicate that the leaf size is large. However, increasing the max_tree_depth may not help: it may be that there is something unusual about the distribution of your data set under your chose metric that makes a tree-based initialization inappropriate. Only used if init = "tree".

margin

A character string specifying the method used to assign points to one side of the hyperplane or the other. Possible values are:

• "explicit" categorizes all distance metrics as either Euclidean or Angular (Euclidean after normalization), explicitly calculates a hyperplane and offset, and then calculates the margin based on the dot product with the hyperplane.

• "implicit" calculates the distance from a point to each of the points defining the normal vector. The margin is calculated by comparing the two distances: the point is assigned to the side of the hyperplane that the normal vector point with the closest distance belongs to.

• "auto" (the default) picks the margin method depending on whether a binary-specific metric such as "bhammming" is chosen, in which case "implicit" is used, and "explicit" otherwise: binary-specific metrics involve storing the data in a way that isn't very efficient for the "explicit" method and the binary-specific metric is usually a lot faster than the generic equivalent such that the cost of two distance calculations for the margin method is still faster.

Only used if init = "tree".

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.

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.

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.

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.

n_search_trees

the number of trees to keep in the search forest as part of index preparation. The default is 1.

pruning_degree_multiplier

How strongly to truncate the final neighbor list for each item. The neighbor list of each item will be truncated to retain only the closest d neighbors, where d = k * pruning_degree_multiplier, and k is the original number of neighbors per item in graph. Roughly, values larger than 1 will keep all the nearest neighbors of an item, plus the given fraction of reverse neighbors (if they exist). For example, setting this to 1.5 will keep all the forward neighbors and then half as many of the reverse neighbors, although exactly which neighbors are retained is also dependent on any occlusion pruning that occurs. Set this to NULL to skip this step.

diversify_prob

the degree of diversification of the search graph by removing unnecessary edges through occlusion pruning. This should take a value between 0 (no diversification) and 1 (remove as many edges as possible) and is treated as the probability of a neighbor being removed if it is found to be an "occlusion". If item p and q, two members of the neighbor list of item i, are closer to each other than they are to i, then the nearer neighbor p is said to "occlude" q. It is likely that q will be in the neighbor list of p so there is no need to retain it in the neighbor list of i. You may also set this to NULL to skip any occlusion pruning. Note that occlusion pruning is carried out twice, once to the forward neighbors, and once to the reverse neighbors.

prune_reverse

If TRUE, prune the reverse neighbors of each item before the reverse graph diversification step using pruning_degree_multiplier. Because the number of reverse neighbors can be much larger than the number of forward neighbors, this can help to avoid excessive computation during the diversification step, with little overall effect on the final search graph. Default is FALSE.

n_threads

Number of threads to use.

verbose

If TRUE, log information to the console.

progress

Determines the type of progress information logged during the nearest neighbor descent stage when 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.

Details

The process of k-nearest neighbor graph construction using Random Projection Forests (Dasgupta and Freund, 2008) for initialization and Nearest Neighbor Descent (Dong and co-workers, 2011) for refinement. Index preparation, uses the graph diversification method of Harwood and Drummond (2016).

Value

the approximate nearest neighbor index, a list containing:

• graph the k-nearest neighbor graph, 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.

• Other list items are intended only for internal use by other functions such as rnnd_query().

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.

Harwood, B., & Drummond, T. (2016). Fanng: Fast approximate nearest neighbour graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5713-5722).

See Also

rnnd_query()

Examples

iris_even <- iris[seq_len(nrow(iris)) %% 2 == 0, ]
iris_odd <- iris[seq_len(nrow(iris)) %% 2 == 1, ]

# Find 4 (approximate) nearest neighbors using Euclidean distance
iris_even_index <- rnnd_build(iris_even, k = 4)
iris_odd_nbrs <- rnnd_query(index = iris_even_index, query = iris_odd, k = 4)