# -*- coding: utf-8 -*-
# SPDX-License-Identifier: BSD-3-Clause
""" Base and mixin classes for nearest neighbors.
Adapted from scikit-learn codebase at
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neighbors/base.py.
"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck
# Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
# Hubness reduction and approximate nearest neighbor support by Roman Feldbauer <roman.feldbauer@univie.ac.at>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
from functools import partial
import warnings
import numpy as np
from scipy.sparse import issparse, csr_matrix
from sklearn.neighbors.base import NeighborsBase as SklearnNeighborsBase
from sklearn.neighbors.base import KNeighborsMixin as SklearnKNeighborsMixin
from sklearn.neighbors.base import RadiusNeighborsMixin as SklearnRadiusNeighborsMixin
from sklearn.neighbors.base import UnsupervisedMixin, SupervisedFloatMixin, SupervisedIntegerMixin
from sklearn.neighbors.base import _tree_query_radius_parallel_helper
from sklearn.neighbors.ball_tree import BallTree
from sklearn.neighbors.kd_tree import KDTree
from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS, pairwise_distances_chunked
from sklearn.utils import check_array, gen_even_slices
from sklearn.utils.validation import check_is_fitted
from joblib import Parallel, delayed, effective_n_jobs
from .approximate_neighbors import ApproximateNearestNeighbor, UnavailableANN
from .hnsw import HNSW
from .random_projection_trees import RandomProjectionTree
from ..reduction import NoHubnessReduction, LocalScaling, MutualProximity, DisSimLocal
try:
from .lsh import FalconnLSH
except ImportError:
FalconnLSH = UnavailableANN
try:
from .lsh import PuffinnLSH
except ImportError:
PuffinnLSH = UnavailableANN
try:
from .onng import NNG
except ImportError:
NNG = UnavailableANN
__all__ = ['KNeighborsMixin', 'NeighborsBase', 'RadiusNeighborsMixin',
'SupervisedFloatMixin', 'SupervisedIntegerMixin', 'UnsupervisedMixin',
'VALID_METRICS', 'VALID_METRICS_SPARSE',
]
VALID_METRICS = dict(lsh=PuffinnLSH.valid_metrics if not issubclass(PuffinnLSH, UnavailableANN) else [],
falconn_lsh=FalconnLSH.valid_metrics if not issubclass(FalconnLSH, UnavailableANN) else [],
onng=NNG.valid_metrics if not issubclass(NNG, UnavailableANN) else [],
hnsw=HNSW.valid_metrics,
rptree=RandomProjectionTree.valid_metrics,
ball_tree=BallTree.valid_metrics,
kd_tree=KDTree.valid_metrics,
# The following list comes from the
# sklearn.metrics.pairwise doc string
brute=(list(PAIRWISE_DISTANCE_FUNCTIONS.keys()) +
['braycurtis', 'canberra', 'chebyshev',
'correlation', 'cosine', 'dice', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis',
'matching', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener',
'sokalsneath', 'sqeuclidean',
'yule', 'wminkowski']))
VALID_METRICS_SPARSE = dict(lsh=[],
falconn_lsh=[],
onng=[],
hnsw=[],
rptree=[],
ball_tree=[],
kd_tree=[],
brute=(PAIRWISE_DISTANCE_FUNCTIONS.keys()
- {'haversine'}),
)
ALG_WITHOUT_RADIUS_QUERY = ['hnsw', 'lsh', 'rptree', 'onng', ]
ANN_ALG = ['hnsw', 'lsh', 'falconn_lsh', 'rptree', 'onng', ]
def _check_weights(weights):
"""Check to make sure weights are valid"""
if weights in (None, 'uniform', 'distance'):
return weights
elif callable(weights):
return weights
else:
raise ValueError("weights not recognized: should be 'uniform', "
"'distance', or a callable function")
def _get_weights(dist, weights):
"""Get the weights from an array of distances and a parameter ``weights``
Parameters
----------
dist : ndarray
The input distances
weights : {'uniform', 'distance' or a callable}
The kind of weighting used
Returns
-------
weights_arr : array of the same shape as ``dist``
if ``weights == 'uniform'``, then returns None
"""
if weights in (None, 'uniform'):
return None
elif weights == 'distance':
# if user attempts to classify a point that was zero distance from one
# or more training points, those training points are weighted as 1.0
# and the other points as 0.0
if dist.dtype is np.dtype(object):
for point_dist_i, point_dist in enumerate(dist):
# check if point_dist is iterable
# (ex: RadiusNeighborClassifier.predict may set an element of
# dist to 1e-6 to represent an 'outlier')
if hasattr(point_dist, '__contains__') and 0. in point_dist:
dist[point_dist_i] = point_dist == 0.
else:
dist[point_dist_i] = 1. / point_dist
else:
with np.errstate(divide='ignore'):
dist = 1. / dist
inf_mask = np.isinf(dist)
inf_row = np.any(inf_mask, axis=1)
dist[inf_row] = inf_mask[inf_row]
return dist
elif callable(weights):
return weights(dist)
else:
raise ValueError("weights not recognized: should be 'uniform', "
"'distance', or a callable function")
class NeighborsBase(SklearnNeighborsBase):
"""Base class for nearest neighbors estimators."""
def __init__(self, n_neighbors=None, radius=None,
algorithm='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, verbose: int = 0, **kwargs):
super().__init__(n_neighbors=n_neighbors,
radius=radius,
algorithm=algorithm,
leaf_size=leaf_size,
metric=metric, p=p, metric_params=metric_params,
n_jobs=n_jobs)
if algorithm_params is None:
n_candidates = 1 if hubness is None else 100
algorithm_params = {'n_candidates': n_candidates,
'metric': metric}
self.algorithm_params = algorithm_params
self.hubness_params = hubness_params if hubness_params is not None else {}
self.hubness = hubness
self.verbose = verbose
self.kwargs = kwargs
def _check_hubness_algorithm(self):
if self.hubness not in ['mp', 'mutual_proximity',
'ls', 'local_scaling',
'dsl', 'dis_sim_loca',
None]:
raise ValueError(f'Unrecognized hubness algorithm: {self.hubness}')
# Users are allowed to use various identifiers for the algorithms,
# but here we normalize them to the short abbreviations used downstream
if self.hubness in ['mp', 'mutual_proximity']:
self.hubness = 'mp'
elif self.hubness in ['ls', 'local_scaling']:
self.hubness = 'ls'
elif self.hubness in ['dsl', 'dis_sim_local']:
self.hubness = 'dsl'
elif self.hubness is None:
pass
else:
raise ValueError(f'Internal error: unknown hubness algorithm: {self.hubness}')
def _check_algorithm_metric(self):
if self.algorithm not in ['auto', 'brute',
'kd_tree', 'ball_tree'] + ANN_ALG:
raise ValueError("unrecognized algorithm: '%s'" % self.algorithm)
if self.algorithm == 'auto':
if self.metric == 'precomputed':
alg_check = 'brute'
elif (callable(self.metric) or
self.metric in VALID_METRICS['ball_tree']):
alg_check = 'ball_tree'
else:
alg_check = 'brute'
else:
alg_check = self.algorithm
if callable(self.metric):
if self.algorithm in ['kd_tree'] + ANN_ALG:
# callable metric is only valid for brute force and ball_tree
raise ValueError(f"{self.algorithm} algorithm does not support callable metric '{self.metric}'")
elif self.metric not in VALID_METRICS[alg_check]:
raise ValueError(f"Metric '{self.metric}' not valid. Use "
f"sorted(skhubness.neighbors.VALID_METRICS['{alg_check}']) "
f"to get valid options. "
f"Metric can also be a callable function.")
if self.metric_params is not None and 'p' in self.metric_params:
warnings.warn("Parameter p is found in metric_params. "
"The corresponding parameter from __init__ "
"is ignored.", SyntaxWarning, stacklevel=3)
effective_p = self.metric_params['p']
else:
effective_p = self.p
if self.metric in ['wminkowski', 'minkowski'] and effective_p <= 0:
raise ValueError("p must be greater than zero for minkowski metric")
def _check_algorithm_hubness_compatibility(self):
if self.hubness == 'dsl':
if self.metric in ['euclidean', 'minkowski']:
self.metric = 'euclidean' # DSL input must still be squared Euclidean
self.hubness_params['squared'] = False
if self.p != 2:
warnings.warn(f'DisSimLocal only supports squared Euclidean distances: Ignoring p={self.p}.')
elif self.metric in ['sqeuclidean']:
self.hubness_params['squared'] = True
else:
warnings.warn(f'DisSimLocal only supports squared Euclidean distances: Ignoring metric={self.metric}.')
self.metric = 'euclidean'
self.hubness_params['squared'] = True
def _fit(self, X):
self._check_algorithm_metric()
self._check_hubness_algorithm()
self._check_algorithm_hubness_compatibility()
if self.metric_params is None:
self.effective_metric_params_ = {}
else:
self.effective_metric_params_ = self.metric_params.copy()
effective_p = self.effective_metric_params_.get('p', self.p)
if self.metric in ['wminkowski', 'minkowski']:
self.effective_metric_params_['p'] = effective_p
self.effective_metric_ = self.metric
# For minkowski distance, use more efficient methods where available
if self.metric == 'minkowski':
p = self.effective_metric_params_.pop('p', 2)
if p <= 0:
raise ValueError(f"p must be greater than one for minkowski metric, "
f"or in ]0, 1[ for fractional norms.")
elif p == 1:
self.effective_metric_ = 'manhattan'
elif p == 2:
self.effective_metric_ = 'euclidean'
elif p == np.inf:
self.effective_metric_ = 'chebyshev'
else:
self.effective_metric_params_['p'] = p
if isinstance(X, NeighborsBase):
self._fit_X = X._fit_X
self._tree = X._tree
self._fit_method = X._fit_method
self._index = X._index
self._hubness_reduction = X._hubness_reduction
return self
elif isinstance(X, BallTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'ball_tree'
return self
elif isinstance(X, KDTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'kd_tree'
return self
elif isinstance(X, ApproximateNearestNeighbor):
self._tree = None
if isinstance(X, PuffinnLSH):
self._fit_X = X.X_train_
self._fit_method = 'lsh'
elif isinstance(X, FalconnLSH):
self._fit_X = X.X_train_
self._fit_method = 'falconn_lsh'
elif isinstance(X, NNG):
self._fit_method = 'onng'
elif isinstance(X, HNSW):
self._fit_method = 'hnsw'
elif isinstance(X, RandomProjectionTree):
self._fit_method = 'rptree'
self._index = X
# TODO enable hubness reduction here
...
return self
X = check_array(X, accept_sparse='csr')
n_samples = X.shape[0]
if n_samples == 0:
raise ValueError(f"n_samples must be greater than 0 (but was {n_samples}.")
if issparse(X):
if self.algorithm not in ('auto', 'brute'):
warnings.warn("cannot use tree with sparse input: "
"using brute force")
if self.effective_metric_ not in VALID_METRICS_SPARSE['brute'] \
and not callable(self.effective_metric_):
raise ValueError(f"Metric '{self.effective_metric_}' not valid for sparse input. "
f"Use sorted(sklearn.neighbors.VALID_METRICS_SPARSE['brute']) "
f"to get valid options. Metric can also be a callable function.")
self._fit_X = X.copy()
self._tree = None
self._fit_method = 'brute'
if self.hubness is not None:
warnings.warn(f'cannot use hubness reduction with tree: disabling hubness reduction.')
self.hubness = None
self._hubness_reduction_method = None
self._hubness_reduction = NoHubnessReduction()
return self
self._fit_method = self.algorithm
self._fit_X = X
self._hubness_reduction_method = self.hubness
if self._fit_method == 'auto':
# A tree approach is better for small number of neighbors,
# and KDTree is generally faster when available
if ((self.n_neighbors is None or
self.n_neighbors < self._fit_X.shape[0] // 2) and
self.metric != 'precomputed'):
if self.effective_metric_ in VALID_METRICS['kd_tree']:
self._fit_method = 'kd_tree'
elif (callable(self.effective_metric_) or
self.effective_metric_ in VALID_METRICS['ball_tree']):
self._fit_method = 'ball_tree'
else:
self._fit_method = 'brute'
else:
self._fit_method = 'brute'
self._index = None
if self._fit_method == 'ball_tree':
self._tree = BallTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
self._index = None
elif self._fit_method == 'kd_tree':
self._tree = KDTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
self._index = None
elif self._fit_method == 'brute':
self._tree = None
self._index = None
elif self._fit_method == 'lsh':
self._index = PuffinnLSH(verbose=self.verbose, **self.algorithm_params)
self._index.fit(X)
self._tree = None
elif self._fit_method == 'falconn_lsh':
self._index = FalconnLSH(verbose=self.verbose, **self.algorithm_params)
self._index.fit(X)
self._tree = None
elif self._fit_method == 'onng':
self._index = NNG(verbose=self.verbose, **self.algorithm_params)
self._index.fit(X)
self._tree = None
elif self._fit_method == 'hnsw':
self._index = HNSW(verbose=self.verbose, **self.algorithm_params)
self._index.fit(X)
self._tree = None
elif self._fit_method == 'rptree':
self._index = RandomProjectionTree(verbose=self.verbose, **self.algorithm_params)
self._index.fit(X)
self._tree = None # because it's a tree, but not an sklearn tree...
else:
raise ValueError(f"algorithm = '{self.algorithm}' not recognized")
if self._hubness_reduction_method is None:
self._hubness_reduction = NoHubnessReduction()
else:
n_candidates = self.algorithm_params['n_candidates']
if 'include_self' in self.kwargs and self.kwargs['include_self']:
neigh_train = self.kcandidates(X, n_neighbors=n_candidates, return_distance=True)
else:
neigh_train = self.kcandidates(n_neighbors=n_candidates, return_distance=True)
# Remove self distances
neigh_dist_train = neigh_train[0] # [:, 1:]
neigh_ind_train = neigh_train[1] # [:, 1:]
if self._hubness_reduction_method == 'ls':
self._hubness_reduction = LocalScaling(verbose=self.verbose, **self.hubness_params)
elif self._hubness_reduction_method == 'mp':
self._hubness_reduction = MutualProximity(verbose=self.verbose, **self.hubness_params)
elif self._hubness_reduction_method == 'dsl':
self._hubness_reduction = DisSimLocal(verbose=self.verbose, **self.hubness_params)
elif self._hubness_reduction_method == 'snn':
raise NotImplementedError('feature not yet implemented')
elif self._hubness_reduction_method == 'simhubin':
raise NotImplementedError('feature not yet implemented')
else:
raise ValueError(f'Hubness reduction algorithm = "{self._hubness_reduction_method}" not recognized.')
self._hubness_reduction.fit(neigh_dist_train, neigh_ind_train, X=X, assume_sorted=False)
if self.n_neighbors is not None:
if self.n_neighbors <= 0:
raise ValueError(f"Expected n_neighbors > 0. Got {self.n_neighbors:d}")
else:
if not np.issubdtype(type(self.n_neighbors), np.integer):
raise TypeError(
f"n_neighbors does not take {type(self.n_neighbors)} value, "
f"enter integer value"
)
return self
def kcandidates(self, X=None, n_neighbors=None, return_distance=True) -> np.ndarray or (np.ndarray, np.ndarray):
"""Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
----------
X : array-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_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns
-------
dist : array
Array representing the lengths to points, only present if
return_distance=True
ind : array
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) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print(neigh.kneighbors([[1., 1., 1.]])) # doctest: +ELLIPSIS
(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) # doctest: +ELLIPSIS
array([[1],
[2]]...)
"""
check_is_fitted(self, "_fit_method")
if n_neighbors is None:
try:
n_neighbors = self.algorithm_params['n_candidates']
except KeyError:
n_neighbors = 1 if self.hubness is None else 100
elif n_neighbors <= 0:
raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}")
else:
if not np.issubdtype(type(n_neighbors), np.integer):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(n_neighbors))
# The number of candidates must not be less than the number of neighbors used downstream
if self.n_neighbors is not None:
if n_neighbors < self.n_neighbors:
n_neighbors = self.n_neighbors
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
X = self._fit_X
# Include an extra neighbor to account for the sample itself being
# returned, which is removed later
n_neighbors += 1
train_size = self._fit_X.shape[0]
if n_neighbors > train_size:
warnings.warn(f'n_candidates > n_samples. Setting n_candidates = n_samples.')
n_neighbors = train_size
n_samples, _ = X.shape
sample_range = np.arange(n_samples)[:, None]
n_jobs = effective_n_jobs(self.n_jobs)
if self._fit_method == 'brute':
# TODO handle sparse matrices here
reduce_func = partial(self._kneighbors_reduce_func,
n_neighbors=n_neighbors,
return_distance=return_distance)
# for efficiency, use squared euclidean distances
kwds = ({'squared': True} if self.effective_metric_ == 'euclidean'
else self.effective_metric_params_)
result = pairwise_distances_chunked(
X, self._fit_X, reduce_func=reduce_func,
metric=self.effective_metric_, n_jobs=n_jobs,
**kwds)
elif self._fit_method in ['ball_tree', 'kd_tree']:
if issparse(X):
raise ValueError(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
# require joblib >= 0.12
delayed_query = delayed(self._tree.query)
parallel_kwargs = {"prefer": "threads"}
result = Parallel(n_jobs, **parallel_kwargs)(
delayed_query(
X[s], n_neighbors, return_distance)
for s in gen_even_slices(X.shape[0], n_jobs)
)
elif self._fit_method in ['lsh', 'falconn_lsh', 'rptree', 'onng', ]:
# assume joblib>=0.12
delayed_query = delayed(self._index.kneighbors)
parallel_kwargs = {"prefer": "threads"}
result = Parallel(n_jobs, **parallel_kwargs)(
delayed_query(X[s], n_candidates=n_neighbors, return_distance=True)
for s in gen_even_slices(X.shape[0], n_jobs)
)
elif self._fit_method in ['hnsw']:
# XXX nmslib supports multiple threads natively, so no joblib used here
# Must pack results into list to match the output format of joblib
result = self._index.kneighbors(X, n_candidates=n_neighbors, return_distance=True)
result = [result, ]
else:
raise ValueError(f"internal: _fit_method not recognized: {self._fit_method}.")
if return_distance:
dist, neigh_ind = zip(*result)
result = [np.atleast_2d(arr) for arr in [np.vstack(dist), np.vstack(neigh_ind)]]
else:
result = np.atleast_2d(np.vstack(result))
if not query_is_train:
return result
else:
# If the query data is the same as the indexed data, we would like
# to ignore the first nearest neighbor of every sample, i.e
# the sample itself.
if return_distance:
dist, neigh_ind = result
else:
neigh_ind = result
sample_mask = neigh_ind != sample_range
# Corner case: When the number of duplicates are more
# than the number of neighbors, the first NN will not
# be the sample, but a duplicate.
# In that case mask the first duplicate.
dup_gr_nbrs = np.all(sample_mask, axis=1)
sample_mask[:, 0][dup_gr_nbrs] = False
neigh_ind = np.reshape(
neigh_ind[sample_mask], (n_samples, n_neighbors - 1))
neigh_ind = np.atleast_2d(neigh_ind)
if return_distance:
dist = np.reshape(
dist[sample_mask], (n_samples, n_neighbors - 1))
dist = np.atleast_2d(dist)
return dist, neigh_ind
return neigh_ind
class KNeighborsMixin(SklearnKNeighborsMixin):
"""Mixin for k-neighbors searches.
NOTE: adapted from scikit-learn. """
def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
""" TODO """
check_is_fitted(self, ["_fit_method", "_hubness_reduction"], all_or_any=any)
if n_neighbors is None:
n_neighbors = self.n_neighbors
elif n_neighbors <= 0:
raise ValueError(f"Expected n_neighbors > 0. Got {n_neighbors}")
else:
if not np.issubdtype(type(n_neighbors), np.integer):
raise TypeError(f"n_neighbors does not take {type(n_neighbors)} value, enter integer value")
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
# Include an extra neighbor to account for the sample itself being
# returned, which is removed later
n_neighbors += 1
train_size = self._fit_X.shape[0]
if n_neighbors > train_size:
raise ValueError(f"Expected n_neighbors <= n_samples, "
f"but n_samples = {train_size}, n_neighbors = {n_neighbors}")
# First obtain candidate neighbors
query_dist, query_ind = self.kcandidates(X, return_distance=True)
query_dist = np.atleast_2d(query_dist)
query_ind = np.atleast_2d(query_ind)
# Second, reduce hubness
hubness_reduced_query_dist, query_ind = self._hubness_reduction.transform(query_dist,
query_ind,
X=X, # required by e.g. DSL
assume_sorted=True,)
# Third, sort hubness reduced candidate neighbors to get the final k neighbors
if query_is_train:
n_neighbors -= 1
kth = np.arange(n_neighbors)
mask = np.argpartition(hubness_reduced_query_dist, kth=kth)[:, :n_neighbors]
hubness_reduced_query_dist = np.take_along_axis(hubness_reduced_query_dist, mask, axis=1)
query_ind = np.take_along_axis(query_ind, mask, axis=1)
if return_distance:
result = hubness_reduced_query_dist, query_ind
else:
result = query_ind
return result
class RadiusNeighborsMixin(SklearnRadiusNeighborsMixin):
"""Mixin for radius-based neighbors searches"""
def radius_neighbors(self, X=None, radius=None, return_distance=True):
"""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
----------
X : array-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.
radius : float
Limiting distance of neighbors to return.
(default is the value passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns
-------
dist : array, 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.
ind : array, 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.
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) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> rng = neigh.radius_neighbors([[1., 1., 1.]])
>>> print(np.asarray(rng[0][0])) # doctest: +ELLIPSIS
[1.5 0.5]
>>> print(np.asarray(rng[1][0])) # doctest: +ELLIPSIS
[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.
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.
"""
check_is_fitted(self, ["_fit_method", "_fit_X"], all_or_any=any)
if X is not None:
query_is_train = False
X = check_array(X, accept_sparse='csr')
else:
query_is_train = True
X = self._fit_X
if radius is None:
radius = self.radius
if self._fit_method == 'brute':
# for efficiency, use squared euclidean distances
if self.effective_metric_ == 'euclidean':
radius *= radius
kwds = {'squared': True}
else:
kwds = self.effective_metric_params_
reduce_func = partial(self._radius_neighbors_reduce_func,
radius=radius,
return_distance=return_distance)
results = pairwise_distances_chunked(
X, self._fit_X, reduce_func=reduce_func,
metric=self.effective_metric_, n_jobs=self.n_jobs,
**kwds)
if return_distance:
dist_chunks, neigh_ind_chunks = zip(*results)
dist_list = sum(dist_chunks, [])
neigh_ind_list = sum(neigh_ind_chunks, [])
# See https://github.com/numpy/numpy/issues/5456
# if you want to understand why this is initialized this way.
dist = np.empty(len(dist_list), dtype='object')
dist[:] = dist_list
neigh_ind = np.empty(len(neigh_ind_list), dtype='object')
neigh_ind[:] = neigh_ind_list
results = dist, neigh_ind
else:
neigh_ind_list = sum(results, [])
results = np.empty(len(neigh_ind_list), dtype='object')
results[:] = neigh_ind_list
elif self._fit_method in ['ball_tree', 'kd_tree']:
if issparse(X):
raise ValueError(f"{self._fit_method} does not work with sparse matrices. "
f"Densify the data, or set algorithm='brute'.")
n_jobs = effective_n_jobs(self.n_jobs)
delayed_query = delayed(_tree_query_radius_parallel_helper)
parallel_kwargs = {"prefer": "threads"}
results = Parallel(n_jobs, **parallel_kwargs)(
delayed_query(self._tree, X[s], radius, return_distance)
for s in gen_even_slices(X.shape[0], n_jobs)
)
if return_distance:
# Different order of neigh_ind, dist than usual!
neigh_ind, dist = tuple(zip(*results))
results = np.hstack(dist), np.hstack(neigh_ind)
else:
results = np.hstack(results)
elif self._fit_method in ['falconn_lsh']:
# assume joblib>=0.12
delayed_query = delayed(self._index.radius_neighbors)
parallel_kwargs = {"prefer": "threads"}
n_jobs = effective_n_jobs(self.n_jobs)
results = Parallel(n_jobs, **parallel_kwargs)(
delayed_query(X[s], radius=radius, return_distance=return_distance)
for s in gen_even_slices(X.shape[0], n_jobs)
)
elif self._fit_method in ALG_WITHOUT_RADIUS_QUERY:
raise ValueError(f'{self._fit_method} does not support radius queries.')
else:
raise ValueError(f"internal: _fit_method={self._fit_method} not recognized.")
if self._fit_method in ANN_ALG:
if return_distance:
# dist, neigh_ind = tuple(zip(*results))
# results = np.hstack(dist), np.hstack(neigh_ind)
dist, neigh_ind = zip(*results)
# results = [np.atleast_2d(arr) for arr in [np.hstack(dist), np.hstack(neigh_ind)]]
results = [np.hstack(dist), np.hstack(neigh_ind)]
else:
results = np.hstack(results)
if not query_is_train:
return results
else:
# If the query data is the same as the indexed data, we would like
# to ignore the first nearest neighbor of every sample, i.e
# the sample itself.
if return_distance:
dist, neigh_ind = results
else:
neigh_ind = results
for ind, ind_neighbor in enumerate(neigh_ind):
mask = ind_neighbor != ind
neigh_ind[ind] = ind_neighbor[mask]
if return_distance:
dist[ind] = dist[ind][mask]
if return_distance:
return dist, neigh_ind
return neigh_ind
def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'):
"""Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than
radius.
Parameters
----------
X : array-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.
radius : float
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
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples]
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.5)
>>> neigh.fit(X) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> A = neigh.radius_neighbors_graph(X)
>>> A.toarray()
array([[1., 0., 1.],
[0., 1., 0.],
[1., 0., 1.]])
See also
--------
kneighbors_graph
"""
check_is_fitted(self, ["_fit_method", "_fit_X"], all_or_any=any)
if X is not None:
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
n_samples2 = self._fit_X.shape[0]
if radius is None:
radius = self.radius
# construct CSR matrix representation of the NN graph
if mode == 'connectivity':
A_ind = self.radius_neighbors(X, radius,
return_distance=False)
A_data = None
elif mode == 'distance':
dist, A_ind = self.radius_neighbors(X, radius,
return_distance=True)
A_data = np.concatenate(list(dist))
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity", '
'or "distance" but got %s instead' % mode)
n_samples1 = A_ind.shape[0]
n_neighbors = np.array([len(a) for a in A_ind])
A_ind = np.concatenate(list(A_ind))
if A_data is None:
A_data = np.ones(len(A_ind))
A_indptr = np.concatenate((np.zeros(1, dtype=int),
np.cumsum(n_neighbors)))
return csr_matrix((A_data, A_ind, A_indptr),
shape=(n_samples1, n_samples2))
def _kneighbors_reduce_func(self, dist, start,
n_neighbors, return_distance):
"""Reduce a chunk of distances to the nearest neighbors
Callback to :func:`sklearn.metrics.pairwise.pairwise_distances_chunked`
Parameters
----------
dist : array of shape (n_samples_chunk, n_samples)
start : int
The index in X which the first row of dist corresponds to.
n_neighbors : int
return_distance : bool
Returns
-------
dist : array of shape (n_samples_chunk, n_neighbors), optional
Returned only if return_distance
neigh : array of shape (n_samples_chunk, n_neighbors)
Notes
-----
This is required until radius_candidates is implemented in addition to kcandiates.
"""
sample_range = np.arange(dist.shape[0])[:, None]
neigh_ind = np.argpartition(dist, n_neighbors - 1, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors]
# argpartition doesn't guarantee sorted order, so we sort again
neigh_ind = neigh_ind[
sample_range, np.argsort(dist[sample_range, neigh_ind])]
if return_distance:
if self.effective_metric_ == 'euclidean':
result = np.sqrt(dist[sample_range, neigh_ind]), neigh_ind
else:
result = dist[sample_range, neigh_ind], neigh_ind
else:
result = neigh_ind
return result