Learn more »
Push, build, and install
RubyGems
npm packages
Python packages
Maven artifacts
PHP packages
Go Modules
Bower components
Debian packages
RPM packages
NuGet packages
/ neighbors / _base.py
"""Base and mixin classes for nearest neighbors"""
# 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>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
from functools import partial
from distutils.version import LooseVersion
import warnings
from abc import ABCMeta, abstractmethod
import numbers
import numpy as np
from scipy.sparse import csr_matrix, issparse
import joblib
from joblib import Parallel, delayed, effective_n_jobs
from ._ball_tree import BallTree
from ._kd_tree import KDTree
from ..base import BaseEstimator, MultiOutputMixin
from ..metrics import pairwise_distances_chunked
from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
from ..utils import check_X_y, check_array, gen_even_slices
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted
from ..utils.validation import check_non_negative
from ..exceptions import DataConversionWarning, EfficiencyWarning
VALID_METRICS = dict(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(ball_tree=[],
kd_tree=[],
brute=(PAIRWISE_DISTANCE_FUNCTIONS.keys() -
{'haversine', 'nan_euclidean'}))
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")
def _is_sorted_by_data(graph):
"""Returns whether the graph's non-zero entries are sorted by data
The non-zero entries are stored in graph.data and graph.indices.
For each row (or sample), the non-zero entries can be either:
- sorted by indices, as after graph.sort_indices()
- sorted by data, as after _check_precomputed(graph)
- not sorted.
Parameters
----------
graph : CSR sparse matrix, shape (n_samples, n_samples)
Neighbors graph as given by kneighbors_graph or radius_neighbors_graph
Returns
-------
res : boolean
Whether input graph is sorted by data
"""
assert graph.format == 'csr'
out_of_order = graph.data[:-1] > graph.data[1:]
line_change = np.unique(graph.indptr[1:-1] - 1)
line_change = line_change[line_change < out_of_order.shape[0]]
return (out_of_order.sum() == out_of_order[line_change].sum())
def _check_precomputed(X):
"""Check precomputed distance matrix
If the precomputed distance matrix is sparse, it checks that the non-zero
entries are sorted by distances. If not, the matrix is copied and sorted.
Parameters
----------
X : {sparse matrix, array-like}, (n_samples, n_samples)
Distance matrix to other samples. X may be a sparse matrix, in which
case only non-zero elements may be considered neighbors.
Returns
-------
X : {sparse matrix, array-like}, (n_samples, n_samples)
Distance matrix to other samples. X may be a sparse matrix, in which
case only non-zero elements may be considered neighbors.
"""
if not issparse(X):
X = check_array(X)
check_non_negative(X, whom="precomputed distance matrix.")
return X
else:
graph = X
if graph.format not in ('csr', 'csc', 'coo', 'lil'):
raise TypeError('Sparse matrix in {!r} format is not supported due to '
'its handling of explicit zeros'.format(graph.format))
copied = graph.format != 'csr'
graph = check_array(graph, accept_sparse='csr')
check_non_negative(graph, whom="precomputed distance matrix.")
if not _is_sorted_by_data(graph):
warnings.warn('Precomputed sparse input was not sorted by data.',
EfficiencyWarning)
if not copied:
graph = graph.copy()
# if each sample has the same number of provided neighbors
row_nnz = np.diff(graph.indptr)
if row_nnz.max() == row_nnz.min():
n_samples = graph.shape[0]
distances = graph.data.reshape(n_samples, -1)
order = np.argsort(distances, kind='mergesort')
order += np.arange(n_samples)[:, None] * row_nnz[0]
order = order.ravel()
graph.data = graph.data[order]
graph.indices = graph.indices[order]
else:
for start, stop in zip(graph.indptr, graph.indptr[1:]):
order = np.argsort(graph.data[start:stop], kind='mergesort')
graph.data[start:stop] = graph.data[start:stop][order]
graph.indices[start:stop] = graph.indices[start:stop][order]
return graph
def _kneighbors_from_graph(graph, n_neighbors, return_distance):
"""Decompose a nearest neighbors sparse graph into distances and indices
Parameters
----------
graph : CSR sparse matrix, shape (n_samples, n_samples)
Neighbors graph as given by kneighbors_graph or radius_neighbors_graph
n_neighbors : int
Number of neighbors required for each sample.
return_distance : boolean
If False, distances will not be returned
Returns
-------
neigh_dist : array, shape (n_samples, n_neighbors)
Distances to nearest neighbors. Only present if return_distance=True.
neigh_ind : array, shape (n_samples, n_neighbors)
Indices of nearest neighbors.
"""
n_samples = graph.shape[0]
assert graph.format == 'csr'
# number of neighbors by samples
row_nnz = np.diff(graph.indptr)
row_nnz_min = row_nnz.min()
if n_neighbors is not None and row_nnz_min < n_neighbors:
raise ValueError(
'%d neighbors per samples are required, but some samples have only'
' %d neighbors in precomputed graph matrix. Decrease number of '
'neighbors used or recompute the graph with more neighbors.'
% (n_neighbors, row_nnz_min))
def extract(a):
# if each sample has the same number of provided neighbors
if row_nnz.max() == row_nnz_min:
return a.reshape(n_samples, -1)[:, :n_neighbors]
else:
idx = np.tile(np.arange(n_neighbors), (n_samples, 1))
idx += graph.indptr[:-1, None]
return a.take(idx, mode='clip').reshape(n_samples, n_neighbors)
if return_distance:
return extract(graph.data), extract(graph.indices)
else:
return extract(graph.indices)
def _radius_neighbors_from_graph(graph, radius, return_distance):
"""Decompose a nearest neighbors sparse graph into distances and indices
Parameters
----------
graph : CSR sparse matrix, shape (n_samples, n_samples)
Neighbors graph as given by kneighbors_graph or radius_neighbors_graph
radius : float > 0
Radius of neighborhoods.
return_distance : boolean
If False, distances will not be returned
Returns
-------
neigh_dist : array, shape (n_samples,) of arrays
Distances to nearest neighbors. Only present if return_distance=True.
neigh_ind :array, shape (n_samples,) of arrays
Indices of nearest neighbors.
"""
assert graph.format == 'csr'
no_filter_needed = bool(graph.data.max() <= radius)
if no_filter_needed:
data, indices, indptr = graph.data, graph.indices, graph.indptr
else:
mask = graph.data <= radius
if return_distance:
data = np.compress(mask, graph.data)
indices = np.compress(mask, graph.indices)
indptr = np.concatenate(([0], np.cumsum(mask)))[graph.indptr]
indices = indices.astype(np.intp, copy=no_filter_needed)
if return_distance:
neigh_dist = np.array(np.split(data, indptr[1:-1]))
neigh_ind = np.array(np.split(indices, indptr[1:-1]))
if return_distance:
return neigh_dist, neigh_ind
else:
return neigh_ind
class NeighborsBase(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
"""Base class for nearest neighbors estimators."""
@abstractmethod
def __init__(self, n_neighbors=None, radius=None,
algorithm='auto', leaf_size=30, metric='minkowski',
p=2, metric_params=None, n_jobs=None):
self.n_neighbors = n_neighbors
self.radius = radius
self.algorithm = algorithm
self.leaf_size = leaf_size
self.metric = metric
self.metric_params = metric_params
self.p = p
self.n_jobs = n_jobs
self._check_algorithm_metric()
def _check_algorithm_metric(self):
if self.algorithm not in ['auto', 'brute',
'kd_tree', 'ball_tree']:
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 == 'kd_tree':
# callable metric is only valid for brute force and ball_tree
raise ValueError(
"kd_tree does not support callable metric '%s'"
"Function call overhead will result"
"in very poor performance."
% self.metric)
elif self.metric not in VALID_METRICS[alg_check]:
raise ValueError("Metric '%s' not valid. Use "
"sorted(sklearn.neighbors.VALID_METRICS['%s']) "
"to get valid options. "
"Metric can also be a callable function."
% (self.metric, alg_check))
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 < 1:
raise ValueError("p must be greater than one for minkowski metric")
def _fit(self, X):
self._check_algorithm_metric()
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 < 1:
raise ValueError("p must be greater than one "
"for minkowski metric")
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.n_samples_fit_ = X.n_samples_fit_
return self
elif isinstance(X, BallTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'ball_tree'
self.n_samples_fit_ = X.data.shape[0]
return self
elif isinstance(X, KDTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'kd_tree'
self.n_samples_fit_ = X.data.shape[0]
return self
if self.effective_metric_ == 'precomputed':
X = _check_precomputed(X)
else:
X = check_array(X, accept_sparse='csr')
n_samples = X.shape[0]
if n_samples == 0:
raise ValueError("n_samples must be greater than 0")
# Precomputed matrix X must be squared
if self.metric == 'precomputed' and X.shape[0] != X.shape[1]:
raise ValueError("Precomputed matrix must be a square matrix."
" Input is a {}x{} matrix."
.format(X.shape[0], X.shape[1]))
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("Metric '%s' not valid for sparse input. "
"Use sorted(sklearn.neighbors."
"VALID_METRICS_SPARSE['brute']) "
"to get valid options. "
"Metric can also be a callable function."
% (self.effective_metric_))
self._fit_X = X.copy()
self._tree = None
self._fit_method = 'brute'
self.n_samples_fit_ = X.shape[0]
return self
self._fit_method = self.algorithm
self._fit_X = X
self.n_samples_fit_ = X.shape[0]
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'
if self._fit_method == 'ball_tree':
self._tree = BallTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
elif self._fit_method == 'kd_tree':
self._tree = KDTree(X, self.leaf_size,
metric=self.effective_metric_,
**self.effective_metric_params_)
elif self._fit_method == 'brute':
self._tree = None
else:
raise ValueError("algorithm = '%s' not recognized"
% self.algorithm)
if self.n_neighbors is not None:
if self.n_neighbors <= 0:
raise ValueError(
"Expected n_neighbors > 0. Got %d" %
self.n_neighbors
)
else:
if not isinstance(self.n_neighbors, numbers.Integral):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(self.n_neighbors))
return self
@property
def _pairwise(self):
# For cross-validation routines to split data correctly
return self.metric == 'precomputed'
def _tree_query_parallel_helper(tree, *args, **kwargs):
"""Helper for the Parallel calls in KNeighborsMixin.kneighbors
The Cython method tree.query is not directly picklable by cloudpickle
under PyPy.
"""
return tree.query(*args, **kwargs)
class KNeighborsMixin:
"""Mixin for k-neighbors searches"""
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)
"""
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
def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
"""Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
----------
X : array-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_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
-------
neigh_dist : array, shape (n_queries, n_neighbors)
Array representing the lengths to points, only present if
return_distance=True
neigh_ind : array, shape (n_queries, n_neighbors)
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 sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples)
NearestNeighbors(n_neighbors=1)
>>> 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]]...)
"""
check_is_fitted(self)
if n_neighbors is None:
n_neighbors = self.n_neighbors
elif n_neighbors <= 0:
raise ValueError(
"Expected n_neighbors > 0. Got %d" %
n_neighbors
)
else:
if not isinstance(n_neighbors, numbers.Integral):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(n_neighbors))
if X is not None:
query_is_train = False
if self.effective_metric_ == 'precomputed':
X = _check_precomputed(X)
else:
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
n_samples_fit = self.n_samples_fit_
if n_neighbors > n_samples_fit:
raise ValueError(
"Expected n_neighbors <= n_samples, "
" but n_samples = %d, n_neighbors = %d" %
(n_samples_fit, n_neighbors)
)
n_jobs = effective_n_jobs(self.n_jobs)
chunked_results = None
if (self._fit_method == 'brute' and
self.effective_metric_ == 'precomputed' and issparse(X)):
results = _kneighbors_from_graph(
X, n_neighbors=n_neighbors,
return_distance=return_distance)
elif self._fit_method == 'brute':
reduce_func = partial(self._kneighbors_reduce_func,
n_neighbors=n_neighbors,
return_distance=return_distance)
# for efficiency, use squared euclidean distances
if self.effective_metric_ == 'euclidean':
kwds = {'squared': True}
else:
kwds = self.effective_metric_params_
chunked_results = list(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)
old_joblib = (
LooseVersion(joblib.__version__) < LooseVersion('0.12'))
if old_joblib:
# Deal with change of API in joblib
check_pickle = False if old_joblib else None
delayed_query = delayed(_tree_query_parallel_helper,
check_pickle=check_pickle)
parallel_kwargs = {"backend": "threading"}
else:
delayed_query = delayed(_tree_query_parallel_helper)
parallel_kwargs = {"prefer": "threads"}
chunked_results = Parallel(n_jobs, **parallel_kwargs)(
delayed_query(
self._tree, X[s], n_neighbors, return_distance)
for s in gen_even_slices(X.shape[0], n_jobs)
)
else:
raise ValueError("internal: _fit_method not recognized")
if chunked_results is not None:
if return_distance:
neigh_dist, neigh_ind = zip(*chunked_results)
results = np.vstack(neigh_dist), np.vstack(neigh_ind)
else:
results = np.vstack(chunked_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:
neigh_dist, neigh_ind = results
else:
neigh_ind = results
n_queries, _ = X.shape
sample_range = np.arange(n_queries)[:, None]
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_queries, n_neighbors - 1))
if return_distance:
neigh_dist = np.reshape(
neigh_dist[sample_mask], (n_queries, n_neighbors - 1))
return neigh_dist, neigh_ind
return neigh_ind
def kneighbors_graph(self, X=None, n_neighbors=None,
mode='connectivity'):
"""Computes the (weighted) graph of k-Neighbors for points in X
Parameters
----------
X : array-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_neighbors : int
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
-------
A : sparse 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.
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.]])
See also
--------
NearestNeighbors.radius_neighbors_graph
"""
check_is_fitted(self)
if n_neighbors is None:
n_neighbors = self.n_neighbors
# check the input only in self.kneighbors
# construct CSR matrix representation of the k-NN graph
if mode == 'connectivity':
A_ind = self.kneighbors(X, n_neighbors, return_distance=False)
n_queries = A_ind.shape[0]
A_data = np.ones(n_queries * n_neighbors)
elif mode == 'distance':
A_data, A_ind = self.kneighbors(
X, n_neighbors, return_distance=True)
A_data = np.ravel(A_data)
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity" '
'or "distance" but got "%s" instead' % mode)
n_queries = A_ind.shape[0]
n_samples_fit = self.n_samples_fit_
n_nonzero = n_queries * n_neighbors
A_indptr = np.arange(0, n_nonzero + 1, n_neighbors)
kneighbors_graph = csr_matrix((A_data, A_ind.ravel(), A_indptr),
shape=(n_queries, n_samples_fit))
return kneighbors_graph
def _tree_query_radius_parallel_helper(tree, *args, **kwargs):
"""Helper for the Parallel calls in RadiusNeighborsMixin.radius_neighbors
The Cython method tree.query_radius is not directly picklable by
cloudpickle under PyPy.
"""
return tree.query_radius(*args, **kwargs)
class RadiusNeighborsMixin:
"""Mixin for radius-based neighbors searches"""
def _radius_neighbors_reduce_func(self, dist, start,
radius, 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.
radius : float
return_distance : bool
Returns
-------
dist : list of n_samples_chunk 1d arrays, optional
Returned only if return_distance
neigh : list of n_samples_chunk 1d arrays
"""
neigh_ind = [np.where(d <= radius)[0] for d in dist]
if return_distance:
if self.effective_metric_ == 'euclidean':
dist = [np.sqrt(d[neigh_ind[i]])
for i, d in enumerate(dist)]
else:
dist = [d[neigh_ind[i]]
for i, d in enumerate(dist)]
results = dist, neigh_ind
else:
results = neigh_ind
return results
def radius_neighbors(self, X=None, radius=None, return_distance=True,
sort_results=False):
"""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.
sort_results : boolean, optional. Defaults to False.
If True, the distances and indices will be sorted before being
returned. If False, the results will not be sorted. If
return_distance == False, setting sort_results = True will
result in an error.
.. versionadded:: 0.22
Returns
-------
neigh_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.
neigh_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 sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.6)
>>> neigh.fit(samples)
NearestNeighbors(radius=1.6)
>>> 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.
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)
if X is not None:
query_is_train = False
if self.effective_metric_ == 'precomputed':
X = _check_precomputed(X)
else:
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' and
self.effective_metric_ == 'precomputed' and issparse(X)):
results = _radius_neighbors_from_graph(
X, radius=radius, return_distance=return_distance)
elif 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)
chunked_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:
neigh_dist_chunks, neigh_ind_chunks = zip(*chunked_results)
neigh_dist_list = sum(neigh_dist_chunks, [])
neigh_ind_list = sum(neigh_ind_chunks, [])
# See https://github.com/numpy/numpy/issues/5456
# to understand why this is initialized this way.
neigh_dist = np.empty(len(neigh_dist_list), dtype='object')
neigh_dist[:] = neigh_dist_list
neigh_ind = np.empty(len(neigh_ind_list), dtype='object')
neigh_ind[:] = neigh_ind_list
results = neigh_dist, neigh_ind
else:
neigh_ind_list = sum(chunked_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(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
n_jobs = effective_n_jobs(self.n_jobs)
if LooseVersion(joblib.__version__) < LooseVersion('0.12'):
# Deal with change of API in joblib
delayed_query = delayed(_tree_query_radius_parallel_helper,
check_pickle=False)
parallel_kwargs = {"backend": "threading"}
else:
delayed_query = delayed(_tree_query_radius_parallel_helper)
parallel_kwargs = {"prefer": "threads"}
chunked_results = Parallel(n_jobs, **parallel_kwargs)(
delayed_query(self._tree, X[s], radius, return_distance,
sort_results=sort_results)
for s in gen_even_slices(X.shape[0], n_jobs)
)
if return_distance:
neigh_ind, neigh_dist = tuple(zip(*chunked_results))
results = np.hstack(neigh_dist), np.hstack(neigh_ind)
else:
results = np.hstack(chunked_results)
else:
raise ValueError("internal: _fit_method not recognized")
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:
neigh_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:
neigh_dist[ind] = neigh_dist[ind][mask]
if return_distance:
return neigh_dist, neigh_ind
return neigh_ind
def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity',
sort_results=False):
"""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 of shape (n_samples, n_features), default=None
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.
sort_results : boolean, optional. Defaults to False.
If True, the distances and indices will be sorted before being
returned. If False, the results will not be sorted.
Only used with mode='distance'.
.. versionadded:: 0.22
Returns
-------
A : sparse 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.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.5)
>>> neigh.fit(X)
NearestNeighbors(radius=1.5)
>>> 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)
# check the input only in self.radius_neighbors
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,
sort_results=sort_results)
A_data = np.concatenate(list(dist))
else:
raise ValueError(
'Unsupported mode, must be one of "connectivity", '
'or "distance" but got %s instead' % mode)
n_queries = A_ind.shape[0]
n_samples_fit = self.n_samples_fit_
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_queries, n_samples_fit))
class SupervisedFloatMixin:
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
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'.
y : {array-like, sparse matrix}
Target values, array of float values, shape = [n_samples]
or [n_samples, n_outputs]
"""
if not isinstance(X, (KDTree, BallTree)):
X, y = check_X_y(X, y, "csr", multi_output=True)
self._y = y
return self._fit(X)
class SupervisedIntegerMixin:
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
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'.
y : {array-like, sparse matrix}
Target values of shape = [n_samples] or [n_samples, n_outputs]
"""
if not isinstance(X, (KDTree, BallTree)):
X, y = check_X_y(X, y, "csr", multi_output=True)
if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:
if y.ndim != 1:
warnings.warn("A column-vector y was passed when a 1d array "
"was expected. Please change the shape of y to "
"(n_samples, ), for example using ravel().",
DataConversionWarning, stacklevel=2)
self.outputs_2d_ = False
y = y.reshape((-1, 1))
else:
self.outputs_2d_ = True
check_classification_targets(y)
self.classes_ = []
self._y = np.empty(y.shape, dtype=np.int)
for k in range(self._y.shape[1]):
classes, self._y[:, k] = np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes)
if not self.outputs_2d_:
self.classes_ = self.classes_[0]
self._y = self._y.ravel()
return self._fit(X)
class UnsupervisedMixin:
def fit(self, 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'.
"""
return self._fit(X)