#!/usr/bin/env python3
""" NearestNeighbors algorithm module. """
from sklearn.neighbors import NearestNeighbors as NearestNeighbors_CPU
from dasf.transforms.base import Fit, GetParams, SetParams
from dasf.utils.funcs import is_gpu_supported
try:
from cuml.neighbors import NearestNeighbors as NearestNeighbors_GPU
except ImportError:
pass
[docs]
class NearestNeighbors(Fit, GetParams, SetParams):
"""
Unsupervised learner for implementing neighbor searches.
Parameters
----------
n_neighbors : int, default=5
Number of neighbors to use by default for :meth:`kneighbors` queries.
radius : float, default=1.0
Range of parameter space to use by default for :meth:`radius_neighbors`
queries.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
based on the values passed to :meth:`fit` method.
Note: fitting on sparse input will override the setting of
this parameter, using brute force.
leaf_size : int, 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 : str or callable, default='minkowski'
Metric to use for distance computation. Default is "minkowski", which
results in the standard Euclidean distance when p = 2. See the
documentation of `scipy.spatial.distance
<https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
the metrics listed in
:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
values.
If metric is "precomputed", X is assumed to be a distance matrix and
must be square during fit. X may be a :term:`sparse graph`, in which
case only "nonzero" elements may be considered neighbors.
If metric is a callable function, it takes two arrays representing 1D
vectors as inputs and must return one value indicating the distance
between those vectors. This works for Scipy's metrics, but is less
efficient than passing the metric name as a string.
p : float (positive), 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, default=None
Additional keyword arguments for the metric function.
n_jobs : int, default=None
The number of parallel jobs to run for neighbors search.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
Attributes
----------
effective_metric_ : str
Metric used to compute distances to neighbors.
effective_metric_params_ : dict
Parameters for the metric used to compute distances to neighbors.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
n_samples_fit_ : int
Number of samples in the fitted data.
See Also
--------
KNeighborsClassifier : Classifier implementing the k-nearest neighbors
vote.
RadiusNeighborsClassifier : Classifier implementing a vote among neighbors
within a given radius.
KNeighborsRegressor : Regression based on k-nearest neighbors.
RadiusNeighborsRegressor : Regression based on neighbors within a fixed
radius.
BallTree : Space partitioning data structure for organizing points in a
multi-dimensional space, used for nearest neighbor search.
Notes
-----
See :ref:`Nearest Neighbors <neighbors>` in the online documentation
for a discussion of the choice of ``algorithm`` and ``leaf_size``.
https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
"""
def __init__(self, n_neighbors=5, radius=1.0, algorithm='auto',
leaf_size=30, metric='minkowski', p=2,
metric_params=None, n_jobs=None, handle=None, verbose=False,
output_type=None, **kwargs):
""" Constructor of the class NearestNeighbors. """
self.__nn_cpu = NearestNeighbors_CPU(n_neighbors=n_neighbors,
radius=radius,
algorithm=algorithm,
leaf_size=leaf_size,
metric=metric, p=p,
metric_params=metric_params,
n_jobs=n_jobs, **kwargs)
if is_gpu_supported():
self.__nn_gpu = NearestNeighbors_GPU(n_neighbors=n_neighbors,
radius=radius,
algorithm=algorithm,
leaf_size=leaf_size,
metric=metric, p=p,
metric_params=metric_params,
n_jobs=n_jobs,
handle=handle,
verbose=verbose,
output_type=output_type,
**kwargs)
else:
self.__nn_gpu = None
[docs]
def _fit_cpu(self, X, y=None, **kwargs):
"""
Fit the nearest neighbors estimator from the training dataset using
CPU only.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
(n_samples, n_samples) if metric='precomputed'
Training data.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : NearestNeighbors
The fitted nearest neighbors estimator.
"""
return self.__nn_cpu.fit(X=X, y=y)
[docs]
def _fit_gpu(self, X, y=None, **kwargs):
"""
Fit the nearest neighbors estimator from the training dataset using
GPU only.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
(n_samples, n_samples) if metric='precomputed'
Training data.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : NearestNeighbors
The fitted nearest neighbors estimator.
"""
if self.__nn_gpu is None:
raise NotImplementedError("GPU is not supported")
return self.__nn_gpu.fit(X=X, **kwargs)
[docs]
def _get_params_cpu(self, deep=True, **kwargs):
"""
Get parameters for this estimator using CPU only.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
return self.__nn_cpu.get_params(deep=deep)
[docs]
def _set_params_cpu(self, **params):
"""
Set the parameters of this estimator using CPU only.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
return self.__nn_cpu.set_params(**params)