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"""Nearest Neighbor Regression"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
#
# License: BSD, (C) INRIA, University of Amsterdam
import numpy as np
from .base import \
_get_weights, _check_weights, \
NeighborsBase, KNeighborsMixin, \
RadiusNeighborsMixin, SupervisedFloatMixin
from ..base import RegressorMixin
from ..utils import atleast2d_or_csr
class KNeighborsRegressor(NeighborsBase, KNeighborsMixin,
SupervisedFloatMixin,
RegressorMixin):
"""Regression based on k-nearest neighbors.
The target is predicted by local interpolation of the targets
associated of the nearest neighbors in the training set.
Parameters
----------
n_neighbors : int, optional (default = 5)
Number of neighbors to use by default for :meth:`k_neighbors` queries.
weights : str or callable
weight function used in prediction. Possible values:
- 'uniform' : uniform weights. All points in each neighborhood
are weighted equally.
- 'distance' : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.
- [callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.
Uniform weights are used by default.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`scipy.spatial.cKDtree`
- '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, optional (default = 30)
Leaf size passed to BallTree or cKDTree. 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.
warn_on_equidistant : boolean, optional. Defaults to True.
Generate a warning if equidistant neighbors are discarded.
For classification or regression based on k-neighbors, if
neighbor k and neighbor k+1 have identical distances but
different labels, then the result will be dependent on the
ordering of the training data.
If the fit method is ``'kd_tree'``, no warnings will be generated.
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.
Examples
--------
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import KNeighborsRegressor
>>> neigh = KNeighborsRegressor(n_neighbors=2)
>>> neigh.fit(X, y) # doctest: +ELLIPSIS
KNeighborsRegressor(...)
>>> print neigh.predict([[1.5]])
[ 0.5]
See also
--------
NearestNeighbors
RadiusNeighborsRegressor
KNeighborsClassifier
RadiusNeighborsClassifier
Notes
-----
See :ref:`Nearest Neighbors <neighbors>` in the online documentation
for a discussion of the choice of ``algorithm`` and ``leaf_size``.
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
def __init__(self, n_neighbors=5, weights='uniform',
algorithm='auto', leaf_size=30, warn_on_equidistant=True,
p=2):
self._init_params(n_neighbors=n_neighbors,
algorithm=algorithm,
leaf_size=leaf_size,
warn_on_equidistant=warn_on_equidistant,
p=p)
self.weights = _check_weights(weights)
def predict(self, X):
"""Predict the target for the provided data
Parameters
----------
X : array
A 2-D array representing the test data.
Returns
-------
y: array
List of target values (one for each data sample).
"""
X = atleast2d_or_csr(X)
neigh_dist, neigh_ind = self.kneighbors(X)
weights = _get_weights(neigh_dist, self.weights)
if weights is None:
return np.mean(self._y[neigh_ind], axis=1)
else:
num = np.sum(self._y[neigh_ind] * weights, axis=1)
denom = np.sum(weights, axis=1)
return num / denom
class RadiusNeighborsRegressor(NeighborsBase, RadiusNeighborsMixin,
SupervisedFloatMixin,
RegressorMixin):
"""Regression based on neighbors within a fixed radius.
The target is predicted by local interpolation of the targets
associated of the nearest neighbors in the training set.
Parameters
----------
radius : float, optional (default = 1.0)
Range of parameter space to use by default for :meth`radius_neighbors`
queries.
weights : str or callable
weight function used in prediction. Possible values:
- 'uniform' : uniform weights. All points in each neighborhood
are weighted equally.
- 'distance' : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.
- [callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.
Uniform weights are used by default.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`scipy.spatial.cKDtree`
- '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, optional (default = 30)
Leaf size passed to BallTree or cKDTree. 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.
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.
Examples
--------
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import RadiusNeighborsRegressor
>>> neigh = RadiusNeighborsRegressor(radius=1.0)
>>> neigh.fit(X, y) # doctest: +ELLIPSIS
RadiusNeighborsRegressor(...)
>>> print neigh.predict([[1.5]])
[ 0.5]
See also
--------
NearestNeighbors
KNeighborsRegressor
KNeighborsClassifier
RadiusNeighborsClassifier
Notes
-----
See :ref:`Nearest Neighbors <neighbors>` in the online documentation
for a discussion of the choice of ``algorithm`` and ``leaf_size``.
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
def __init__(self, radius=1.0, weights='uniform',
algorithm='auto', leaf_size=30, p=2):
self._init_params(radius=radius,
algorithm=algorithm,
leaf_size=leaf_size,
p=p)
self.weights = _check_weights(weights)
def predict(self, X):
"""Predict the target for the provided data
Parameters
----------
X : array
A 2-D array representing the test data.
Returns
-------
y: array
List of target values (one for each data sample).
"""
X = atleast2d_or_csr(X)
neigh_dist, neigh_ind = self.radius_neighbors(X)
weights = _get_weights(neigh_dist, self.weights)
if weights is None:
return np.array([np.mean(self._y[ind])
for ind in neigh_ind])
else:
return np.array([(np.sum(self._y[ind] * weights[i])
/ np.sum(weights[i]))
for (i, ind) in enumerate(neigh_ind)])
|
