Neighbors NN Descent

Python module: cuvs.neighbors.nn_descent

Index

1 cdef class Index

NN-Descent index object. This object stores the trained NN-Descent index, which can be used to get the NN-Descent graph and distances after building

Members

Name	Kind
`trained`	property
`graph`	property
`distances`	property

trained

1 def trained(self)

graph

1 def graph(self)

distances

1 def distances(self)

IndexParams

1 cdef class IndexParams

Parameters to build NN-Descent Index

Parameters

Name	Type	Description
`metric`	`str, default = "sqeuclidean"`	String denoting the metric type. Supported metrics are `l2`, `euclidean`, `sqeuclidean`, `inner_product`, `cosine`, and `bitwise_hamming` (`bitwise_hamming` is for int8 and uint8 data types only)
`graph_degree`	`int`	For an input dataset of dimensions (N, D), determines the final dimensions of the all-neighbors knn graph which turns out to be of dimensions (N, graph_degree)
`intermediate_graph_degree`	`int`	Internally, nn-descent builds an all-neighbors knn graph of dimensions (N, intermediate_graph_degree) before selecting the final `graph_degree` neighbors. It’s recommended that `intermediate_graph_degree` >= 1.5 * graph_degree
`max_iterations`	`int`	The number of iterations that nn-descent will refine the graph for. More iterations produce a better quality graph at cost of performance
`termination_threshold`	`float`	The delta at which nn-descent will terminate its iterations
`return_distances`	`bool`	Whether to return distances array
`dist_comp_dtype`	`str, default = "auto"`	Dtype to use for distance computation. Supported dtypes are `auto`, `fp32`, and `fp16` `auto` automatically determines the best dtype for distance computation based on the dataset dimensions. `fp32` uses fp32 distance computation for better precision at the cost of performance and memory usage. This option is only valid when data type is fp32. `fp16` uses fp16 distance computation for better performance and memory usage at the cost of precision.

Constructor

1 def __init__(self, *, metric=None, metric_arg=None, graph_degree=None, intermediate_graph_degree=None, max_iterations=None, termination_threshold=None, return_distances=None, dist_comp_dtype="auto" )

Members

Name	Kind
`metric`	property
`metric_arg`	property
`graph_degree`	property
`intermediate_graph_degree`	property
`max_iterations`	property
`termination_threshold`	property
`get_handle`	method

metric

1 def metric(self)

metric_arg

1 def metric_arg(self)

graph_degree

1 def graph_degree(self)

intermediate_graph_degree

1 def intermediate_graph_degree(self)

max_iterations

1 def max_iterations(self)

termination_threshold

1 def termination_threshold(self)

get_handle

1 def get_handle(self)

Get a pointer to the underlying C object.

build

@auto_sync_resources

1 def build(IndexParams index_params, dataset, graph=None, resources=None)

Build KNN graph from the dataset

Parameters

Name	Type	Description
`index_params`	`cuvs.neighbors.nn_descent.IndexParams`
`dataset`	`Array interface compliant matrix, on either host or device memory`	Supported dtype [float, int8, uint8]
`graph`	`Optional host matrix for storing output graph`
`resources`	`cuvs.common.Resources, optional`

Returns

Name	Type	Description
`index`	`cuvs.neighbors.nn_descent.Index`

Examples

1 >>> import cupy as cp
2 >>> from cuvs.neighbors import nn_descent
3 >>> n_samples = 50000
4 >>> n_features = 50
5 >>> n_queries = 1000
6 >>> k = 10
7 >>> dataset = cp.random.random_sample((n_samples, n_features),
8 ...                                   dtype=cp.float32)
9 >>> build_params = nn_descent.IndexParams(metric="sqeuclidean")
10 >>> index = nn_descent.build(build_params, dataset)
11 >>> graph = index.graph