cuvs.neighbors.all_neighbors

cuvs.neighbors.all_neighbors#

3 min read time

Applies to Linux

Submodules#

cuvs.neighbors.all_neighbors.all_neighbors

Classes#

AllNeighborsParams

AllNeighborsParams(algo=u'nn_descent', *, overlap_factor=2, n_clusters=1, metric=u'sqeuclidean', ivf_pq_params=None, nn_descent_params=None)

Functions#

build(dataset, k, params, *[, indices, distances, ...])

All-neighbors allows building an approximate all-neighbors knn graph.

Package Contents#

class cuvs.neighbors.all_neighbors.AllNeighborsParams#

AllNeighborsParams(algo=u’nn_descent’, *, overlap_factor=2, n_clusters=1, metric=u’sqeuclidean’, ivf_pq_params=None, nn_descent_params=None)

Parameters for all-neighbors k-NN graph building.

algostr or cuvsAllNeighborsAlgo
Algorithm to use for local k-NN graph building. Options: “brute_force”, “ivf_pq”, “nn_descent”

overlap_factorint, default=2
Number of clusters each point is assigned to (must be < n_clusters)

n_clustersint, default=1
Number of clusters/batches to partition the dataset into (> overlap_factor). Use n_clusters>1 to distribute the work across GPUs.

metricstr or cuvsDistanceType, default=”sqeuclidean”
Distance metric to use for graph construction

ivf_pq_paramscuvs.neighbors.ivf_pq.IndexParams, optional
IVF-PQ specific parameters (used when algo=”ivf_pq”)

nn_descent_paramscuvs.neighbors.nn_descent.IndexParams, optional
NN-Descent specific parameters (used when algo=”nn_descent”)

static __reduce__(*args, **kwargs)#: AllNeighborsParams.__reduce_cython__(self)

static __setstate__(*args, **kwargs)#: AllNeighborsParams.__setstate_cython__(self, __pyx_state)

get_handle()#: AllNeighborsParams.get_handle(self) Get a pointer to the underlying C object.

cuvs.neighbors.all_neighbors.build(dataset, k, params, *, indices=None, distances=None, core_distances=None, alpha=1.0, resources=None)#

All-neighbors allows building an approximate all-neighbors knn graph. Given a full dataset, it finds nearest neighbors for all the training vectors in the dataset.

Parameters#

datasetarray_like: Training dataset to build the k-NN graph for. Can be provided on host (for multi-GPU build) or device (for single-GPU build). Host vs device location is automatically detected. Supported dtype: float32
kint: Number of nearest neighbors to find for each point
paramsAllNeighborsParams: Parameters object containing all build settings including algorithm choice and algorithm-specific parameters.
indicesarray_like, optional: Optional output buffer for indices [num_rows x k] on device (int64). If not provided, will be allocated automatically.
distancesarray_like, optional: Optional output buffer for distances [num_rows x k] on device (float32)
core_distancesarray_like, optional: Optional output buffer for core distances [num_rows] on device (float32). Requires distances parameter to be provided.
alphafloat, default=1.0: Mutual-reachability scaling; used only when core_distances is provided
resourcesResources or MultiGpuResources, optional: CUDA resources to use for the operation. If not provided, a default Resources object will be created. Use MultiGpuResources to enable multi-GPU execution across multiple devices.

Returns#

indicesarray_like: k-NN indices for each point [num_rows x k], always on device. If indices buffer was provided, returns the same array filled with results.
distancesarray_like or None: k-NN distances if distances buffer was provided, None otherwise
core_distancesarray_like or None: Core distances if core_distances buffer was provided, None otherwise