skhubness.neighbors.NearestNeighbors¶

class skhubness.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm: str = 'auto', algorithm_params: dict = None, hubness: str = None, hubness_params: dict = None, leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, **kwargs)[source]¶

Unsupervised learner for implementing neighbor searches.

Read more in the scikit-learn User Guide

Parameters

n_neighbors: int, optional (default = 5)

Number of neighbors to use by default for kneighbors() queries.

radius: float, optional (default = 1.0)

Range of parameter space to use by default for radius_neighbors() queries.

algorithm{‘auto’, ‘hnsw’, ‘lsh’, ‘falconn_lsh’, ‘nng’, ‘rptree’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional

Algorithm used to compute the nearest neighbors:

‘hnsw’ will use HNSW
‘lsh’ will use PuffinnLSH
‘falconn_lsh’ will use FalconnLSH
‘nng’ will use NNG
‘rptree’ will use RandomProjectionTree
‘ball_tree’ will use BallTree
‘kd_tree’ will use KDTree
‘brute’ will use a brute-force search.
‘auto’ will attempt to decide the most appropriate exact algorithm based on the values passed to fit() method. This will not select an approximate nearest neighbor algorithm.

Note: fitting on sparse input will override the setting of this parameter, using brute force.

algorithm_params: dict, optional

Override default parameters of the NN algorithm. For example, with algorithm=’lsh’ and algorithm_params={n_candidates: 100} one hundred approximate neighbors are retrieved with LSH. If parameter hubness is set, the candidate neighbors are further reordered with hubness reduction. Finally, n_neighbors objects are used from the (optionally reordered) candidates.

hubness: {‘mutual_proximity’, ‘local_scaling’, ‘dis_sim_local’, None}, optional

Hubness reduction algorithm

‘mutual_proximity’ or ‘mp’ will use MutualProximity
‘local_scaling’ or ‘ls’ will use LocalScaling
‘dis_sim_local’ or ‘dsl’ will use DisSimLocal

If None, no hubness reduction will be performed (=vanilla kNN).

hubness_params: dict, optional

Override default parameters of the selected hubness reduction algorithm. For example, with hubness=’mp’ and hubness_params={‘method’: ‘normal’} a mutual proximity variant is used, which models distance distributions with independent Gaussians.

leaf_size: int, optional (default = 30)

Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

metric: string or callable, default ‘minkowski’

metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used.

If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string.

Distance matrices are not supported.

Valid values for metric are:

from scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’]
from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’]

See the documentation for scipy.spatial.distance for details on these metrics.

p: integer, optional (default = 2)

Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metric_params: dict, optional (default = None)

Additional keyword arguments for the metric function.

n_jobs: int or None, optional (default=None)

The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

See also

KNeighborsClassifier
RadiusNeighborsClassifier
KNeighborsRegressor
RadiusNeighborsRegressor
BallTree

Notes

See Nearest Neighbors in the scikit-learn online documentation for a discussion of the choice of algorithm and leaf_size.

https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

Examples

>>> import numpy as np
>>> from skhubness.neighbors import NearestNeighbors
>>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]

>>> neigh = NearestNeighbors(2, 0.4)
>>> neigh.fit(samples)  
NearestNeighbors(...)

>>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
... 
array([[2, 0]]...)

>>> nbrs = neigh.radius_neighbors([[0, 0, 1.3]], 0.4, return_distance=False)
>>> np.asarray(nbrs[0][0])
array(2)

__init__(n_neighbors=5, radius=1.0, algorithm: str = 'auto', algorithm_params: dict = None, hubness: str = None, hubness_params: dict = None, leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([n_neighbors, radius, algorithm, …])	Initialize self.
`fit`(X[, y])	Fit the model using X as training data
`get_params`([deep])	Get parameters for this estimator.
`kcandidates`([X, n_neighbors, return_distance])	Finds the K-neighbors of a point.
`kneighbors`([X, n_neighbors, return_distance])	TODO
`kneighbors_graph`([X, n_neighbors, mode])	Computes the (weighted) graph of k-Neighbors for points in X
`radius_neighbors`([X, radius, return_distance])	Finds the neighbors within a given radius of a point or points.
`radius_neighbors_graph`([X, radius, mode])	Computes the (weighted) graph of Neighbors for points in X
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y=None)¶

Fit the model using X as training data

Parameters

X{array-like, sparse matrix, BallTree, KDTree}: Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric=’precomputed’.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any: Parameter names mapped to their values.

kcandidates(X=None, n_neighbors=None, return_distance=True) → numpy.ndarray[source]¶

Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point.

Parameters

Xarray-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’: The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
n_neighborsint: Number of neighbors to get (default is the value passed to the constructor).
return_distanceboolean, optional. Defaults to True.: If False, distances will not be returned

Returns

distarray: Array representing the lengths to points, only present if return_distance=True
indarray: Indices of the nearest points in the population matrix.

Examples

In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]

>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from skhubness.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples) 
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print(neigh.kneighbors([[1., 1., 1.]])) 
(array([[0.5]]), array([[2]]))

As you can see, it returns [[0.5]], and [[2]], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points:

>>> X = [[0., 1., 0.], [1., 0., 1.]]
>>> neigh.kneighbors(X, return_distance=False) 
array([[1],
       [2]]...)

kneighbors(X=None, n_neighbors=None, return_distance=True)[source]¶: TODO

kneighbors_graph(X=None, n_neighbors=None, mode='connectivity')¶

Computes the (weighted) graph of k-Neighbors for points in X

Parameters

Xarray-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’: The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
n_neighborsint: Number of neighbors for each sample. (default is value passed to the constructor).
mode{‘connectivity’, ‘distance’}, optional: Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.

Returns

Asparse graph in CSR format, shape = [n_queries, n_samples_fit]: n_samples_fit is the number of samples in the fitted data A[i, j] is assigned the weight of edge that connects i to j.

See also

NearestNeighbors.radius_neighbors_graph

Examples

>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=2)
>>> neigh.fit(X)
NearestNeighbors(n_neighbors=2)
>>> A = neigh.kneighbors_graph(X)
>>> A.toarray()
array([[1., 0., 1.],
       [0., 1., 1.],
       [1., 0., 1.]])

radius_neighbors(X=None, radius=None, return_distance=True)[source]¶

Finds the neighbors within a given radius of a point or points.

Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary are included in the results.

The result points are not necessarily sorted by distance to their query point.

Parameters

Xarray-like, (n_samples, n_features), optional: The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
radiusfloat: Limiting distance of neighbors to return. (default is the value passed to the constructor).
return_distanceboolean, optional. Defaults to True.: If False, distances will not be returned

Returns

distarray, shape (n_samples,) of arrays: Array representing the distances to each point, only present if return_distance=True. The distance values are computed according to the metric constructor parameter.
indarray, shape (n_samples,) of arrays: An array of arrays of indices of the approximate nearest points from the population matrix that lie within a ball of size radius around the query points.

Notes

Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. For efficiency, radius_neighbors returns arrays of objects, where each object is a 1D array of indices or distances.

Examples

In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1, 1, 1]:

>>> import numpy as np
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from skhubness.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.6)
>>> neigh.fit(samples) 
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> rng = neigh.radius_neighbors([[1., 1., 1.]])
>>> print(np.asarray(rng[0][0])) 
[1.5 0.5]
>>> print(np.asarray(rng[1][0])) 
[1 2]

The first array returned contains the distances to all points which are closer than 1.6, while the second array returned contains their indices. In general, multiple points can be queried at the same time.

radius_neighbors_graph(X=None, radius=None, mode='connectivity')[source]¶

Computes the (weighted) graph of Neighbors for points in X

Neighborhoods are restricted the points at a distance lower than radius.

Parameters

Xarray-like, shape = [n_samples, n_features], optional: The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
radiusfloat: Radius of neighborhoods. (default is the value passed to the constructor).
mode{‘connectivity’, ‘distance’}, optional: Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.

Returns

Asparse matrix in CSR format, shape = [n_samples, n_samples]: A[i, j] is assigned the weight of edge that connects i to j.