119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
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"""Unsupervised nearest neighbors learner"""
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from ._base import NeighborsBase
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from ._base import KNeighborsMixin
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from ._base import RadiusNeighborsMixin
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from ._base import UnsupervisedMixin
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from ..utils.validation import _deprecate_positional_args
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class NearestNeighbors(KNeighborsMixin, RadiusNeighborsMixin,
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UnsupervisedMixin, NeighborsBase):
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"""Unsupervised learner for implementing neighbor searches.
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Read more in the :ref:`User Guide <unsupervised_neighbors>`.
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.. versionadded:: 0.9
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Parameters
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----------
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n_neighbors : int, default=5
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Number of neighbors to use by default for :meth:`kneighbors` queries.
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radius : float, default=1.0
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Range of parameter space to use by default for :meth:`radius_neighbors`
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queries.
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree`
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- 'kd_tree' will use :class:`KDTree`
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- 'brute' will use a brute-force search.
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- 'auto' will attempt to decide the most appropriate algorithm
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based on the values passed to :meth:`fit` method.
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Note: fitting on sparse input will override the setting of
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this parameter, using brute force.
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leaf_size : int, default=30
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Leaf size passed to BallTree or KDTree. This can affect the
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speed of the construction and query, as well as the memory
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required to store the tree. The optimal value depends on the
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nature of the problem.
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metric : str or callable, default='minkowski'
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the distance metric to use for the tree. The default metric is
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minkowski, and with p=2 is equivalent to the standard Euclidean
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metric. See the documentation of :class:`DistanceMetric` for a
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list of available metrics.
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If metric is "precomputed", X is assumed to be a distance matrix and
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must be square during fit. X may be a :term:`sparse graph`,
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in which case only "nonzero" elements may be considered neighbors.
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p : int, default=2
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Parameter for the Minkowski metric from
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sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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metric_params : dict, default=None
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Additional keyword arguments for the metric function.
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n_jobs : int, default=None
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The number of parallel jobs to run for neighbors search.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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Attributes
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----------
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effective_metric_ : str
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Metric used to compute distances to neighbors.
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effective_metric_params_ : dict
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Parameters for the metric used to compute distances to neighbors.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.neighbors import NearestNeighbors
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>>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]
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>>> neigh = NearestNeighbors(n_neighbors=2, radius=0.4)
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>>> neigh.fit(samples)
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NearestNeighbors(...)
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>>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
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array([[2, 0]]...)
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>>> nbrs = neigh.radius_neighbors([[0, 0, 1.3]], 0.4, return_distance=False)
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>>> np.asarray(nbrs[0][0])
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array(2)
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See also
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--------
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KNeighborsClassifier
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RadiusNeighborsClassifier
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KNeighborsRegressor
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RadiusNeighborsRegressor
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BallTree
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Notes
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-----
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See :ref:`Nearest Neighbors <neighbors>` in the online documentation
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for a discussion of the choice of ``algorithm`` and ``leaf_size``.
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https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
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"""
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@_deprecate_positional_args
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def __init__(self, *, n_neighbors=5, radius=1.0,
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algorithm='auto', leaf_size=30, metric='minkowski',
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p=2, metric_params=None, n_jobs=None):
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super().__init__(
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n_neighbors=n_neighbors,
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radius=radius,
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algorithm=algorithm,
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leaf_size=leaf_size, metric=metric, p=p,
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metric_params=metric_params, n_jobs=n_jobs)
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