skhubness.neighbors.HNSW

class skhubness.neighbors.HNSW(n_candidates: int = 5, metric: str = 'euclidean', method: str = 'hnsw', post_processing: int = 2, n_jobs: int = 1, verbose: int = 0)

Wrapper for using nmslib

Hierarchical navigable small-world graphs are data structures, that allow for approximate nearest neighbor search. Here, an implementation from nmslib is used.

Parameters

n_candidates: int, default = 5

Number of neighbors to retrieve

metric: str, default = ‘euclidean’

Distance metric, allowed are “angular”, “euclidean”, “manhattan”, “hamming”, “dot”

method: str, default = ‘hnsw’,

ANN method to use. Currently, only ‘hnsw’ is supported.

post_processing: int, default = 2

More post processing means longer index creation, and higher retrieval accuracy.

n_jobs: int, default = 1

Number of parallel jobs

verbose: int, default = 0

Verbosity level. If verbose >= 2, show progress bar on indexing.

Attributes

valid_metrics:

List of valid distance metrics/measures

__init__(n_candidates: int = 5, metric: str = 'euclidean', method: str = 'hnsw', post_processing: int = 2, n_jobs: int = 1, verbose: int = 0)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([n_candidates, metric, method, …])	Initialize self.
fit(X[, y])	Setup the HNSW index from training data.
kneighbors([X, n_candidates, return_distance])	Retrieve k nearest neighbors.

Attributes

valid_metrics

fit(X, y=None) → skhubness.neighbors.hnsw.HNSW

Setup the HNSW index from training data.

Parameters

X: np.array

Data to be indexed

y: any

Ignored

Returns

self: HNSW

An instance of HNSW with a built graph

kneighbors(X: Optional[numpy.ndarray] = None, n_candidates: Optional[int] = None, return_distance: bool = True) → Union[Tuple[numpy.array, numpy.array], numpy.array]

Retrieve k nearest neighbors.

Parameters

X: np.array or None, optional, default = None

Query objects. If None, search among the indexed objects.

n_candidates: int or None, optional, default = None

Number of neighbors to retrieve. If None, use the value passed during construction.

return_distance: bool, default = True

If return_distance, will return distances and indices to neighbors. Else, only return the indices.