Neighbors All Neighbors

Python module: cuvs.neighbors.all_neighbors

AllNeighborsParams

1 cdef class AllNeighborsParams

Parameters for all-neighbors k-NN graph building.

Parameters

Name	Type	Description
`algo`	`str or cuvsAllNeighborsAlgo`	Algorithm to use for local k-NN graph building. Options: “brute_force”, “ivf_pq”, “nn_descent”
`overlap_factor`	`int, default=2`	Number of clusters each point is assigned to (must be < n_clusters)
`n_clusters`	`int, default=1`	Number of clusters/batches to partition the dataset into (> overlap_factor). Use n_clusters>1 to distribute the work across GPUs.
`metric`	`str or cuvsDistanceType, default="sqeuclidean"`	Distance metric to use for graph construction
`ivf_pq_params`	`cuvs.neighbors.ivf_pq.IndexParams, optional`	IVF-PQ specific parameters (used when algo=“ivf_pq”)
`nn_descent_params`	`cuvs.neighbors.nn_descent.IndexParams, optional`	NN-Descent specific parameters (used when algo=“nn_descent”)

Constructor

1 def __init__(self, *, algo="nn_descent", overlap_factor=2, n_clusters=1, metric="sqeuclidean", ivf_pq_params=None, nn_descent_params=None)

Members

Name	Kind
`get_handle`	method
`algo`	property
`overlap_factor`	property
`n_clusters`	property
`metric`	property

get_handle

1 def get_handle(self)

Get a pointer to the underlying C object.

algo

1 def algo(self)

Algorithm used for local k-NN graph building.

overlap_factor

1 def overlap_factor(self)

Number of clusters each point is assigned to.

n_clusters

1 def n_clusters(self)

Number of clusters/batches to partition the dataset into.

metric

1 def metric(self)

Distance metric used for graph construction.

build

@auto_convert_output

1 def 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

Name	Type	Description
`dataset`	`array_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
`k`	`int`	Number of nearest neighbors to find for each point
`params`	`AllNeighborsParams`	Parameters object containing all build settings including algorithm choice and algorithm-specific parameters.
`indices`	`array_like, optional`	Optional output buffer for indices [num_rows x k] on device (int64). If not provided, will be allocated automatically.
`distances`	`array_like, optional`	Optional output buffer for distances [num_rows x k] on device (float32)
`core_distances`	`array_like, optional`	Optional output buffer for core distances [num_rows] on device (float32). Requires distances parameter to be provided.
`alpha`	`float, default=1.0`	Mutual-reachability scaling; used only when core_distances is provided
`resources`	`Resources 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

Name	Type	Description
`indices`	`array_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.
`distances`	`array_like or None`	k-NN distances if distances buffer was provided, None otherwise
`core_distances`	`array_like or None`	Core distances if core_distances buffer was provided, None otherwise