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_regression.py
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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
# Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
# Empty radius support by Andreas Bjerre-Nielsen
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam,
# University of Copenhagen
import warnings
import numpy as np
from ._base import _get_weights, _check_weights
from ._base import NeighborsBase, KNeighborsMixin, RadiusNeighborsMixin
from ..base import RegressorMixin
from ..utils.validation import _deprecate_positional_args
from ..utils.deprecation import deprecated
class KNeighborsRegressor(KNeighborsMixin,
RegressorMixin,
NeighborsBase):
"""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.
Read more in the :ref:`User Guide <regression>`.
.. versionadded:: 0.9
Parameters
----------
n_neighbors : int, default=5
Number of neighbors to use by default for :meth:`kneighbors` queries.
weights : {'uniform', 'distance'} or callable, default='uniform'
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'}, 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.
p : int, default=2
Power parameter for the Minkowski metric. 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 : str or callable, default='minkowski'
the distance metric to use for the tree. The default metric is
minkowski, and with p=2 is equivalent to the standard Euclidean
metric. See the documentation of :class:`DistanceMetric` for a
list of available metrics.
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.
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.
Doesn't affect :meth:`fit` method.
Attributes
----------
effective_metric_ : str or callable
The distance metric to use. It will be same as the `metric` parameter
or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
'minkowski' and `p` parameter set to 2.
effective_metric_params_ : dict
Additional keyword arguments for the metric function. For most metrics
will be same with `metric_params` parameter, but may also contain the
`p` parameter value if the `effective_metric_` attribute is set to
'minkowski'.
n_samples_fit_ : int
Number of samples in the fitted data.
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)
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``.
.. warning::
Regarding the Nearest Neighbors algorithms, if it is found that two
neighbors, neighbor `k+1` and `k`, have identical distances but
different labels, the results will depend on the ordering of the
training data.
https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
@_deprecate_positional_args
def __init__(self, n_neighbors=5, *, weights='uniform',
algorithm='auto', leaf_size=30,
p=2, metric='minkowski', metric_params=None, n_jobs=None,
**kwargs):
super().__init__(
n_neighbors=n_neighbors,
algorithm=algorithm,
leaf_size=leaf_size, metric=metric, p=p,
metric_params=metric_params, n_jobs=n_jobs, **kwargs)
self.weights = _check_weights(weights)
def _more_tags(self):
# For cross-validation routines to split data correctly
return {'pairwise': self.metric == 'precomputed'}
# TODO: Remove in 1.1
# mypy error: Decorated property not supported
@deprecated("Attribute _pairwise was deprecated in " # type: ignore
"version 0.24 and will be removed in 1.1 (renaming of 0.26).")
@property
def _pairwise(self):
# For cross-validation routines to split data correctly
return self.metric == 'precomputed'
def fit(self, X, y):
"""Fit the k-nearest neighbors regressor from the training dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
(n_samples, n_samples) if metric='precomputed'
Training data.
y : {array-like, sparse matrix} of shape (n_samples,) or \
(n_samples, n_outputs)
Target values.
Returns
-------
self : KNeighborsRegressor
The fitted k-nearest neighbors regressor.
"""
return self._fit(X, y)
def predict(self, X):
"""Predict the target for the provided data
Parameters
----------
X : array-like of shape (n_queries, n_features), \
or (n_queries, n_indexed) if metric == 'precomputed'
Test samples.
Returns
-------
y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int
Target values.
"""
X = self._validate_data(X, accept_sparse='csr', reset=False)
neigh_dist, neigh_ind = self.kneighbors(X)
weights = _get_weights(neigh_dist, self.weights)
_y = self._y
if _y.ndim == 1:
_y = _y.reshape((-1, 1))
if weights is None:
y_pred = np.mean(_y[neigh_ind], axis=1)
else:
y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float64)
denom = np.sum(weights, axis=1)
for j in range(_y.shape[1]):
num = np.sum(_y[neigh_ind, j] * weights, axis=1)
y_pred[:, j] = num / denom
if self._y.ndim == 1:
y_pred = y_pred.ravel()
return y_pred
class RadiusNeighborsRegressor(RadiusNeighborsMixin,
RegressorMixin,
NeighborsBase):
"""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.
Read more in the :ref:`User Guide <regression>`.
.. versionadded:: 0.9
Parameters
----------
radius : float, default=1.0
Range of parameter space to use by default for :meth:`radius_neighbors`
queries.
weights : {'uniform', 'distance'} or callable, default='uniform'
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'}, 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.
p : int, default=2
Power parameter for the Minkowski metric. 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 : str or callable, default='minkowski'
the distance metric to use for the tree. The default metric is
minkowski, and with p=2 is equivalent to the standard Euclidean
metric. See the documentation of :class:`DistanceMetric` for a
list of available metrics.
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.
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 or callable
The distance metric to use. It will be same as the `metric` parameter
or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
'minkowski' and `p` parameter set to 2.
effective_metric_params_ : dict
Additional keyword arguments for the metric function. For most metrics
will be same with `metric_params` parameter, but may also contain the
`p` parameter value if the `effective_metric_` attribute is set to
'minkowski'.
n_samples_fit_ : int
Number of samples in the fitted data.
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)
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``.
https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
@_deprecate_positional_args
def __init__(self, radius=1.0, *, weights='uniform',
algorithm='auto', leaf_size=30,
p=2, metric='minkowski', metric_params=None, n_jobs=None,
**kwargs):
super().__init__(
radius=radius,
algorithm=algorithm,
leaf_size=leaf_size,
p=p, metric=metric, metric_params=metric_params,
n_jobs=n_jobs, **kwargs)
self.weights = _check_weights(weights)
def fit(self, X, y):
"""Fit the radius neighbors regressor from the training dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
(n_samples, n_samples) if metric='precomputed'
Training data.
y : {array-like, sparse matrix} of shape (n_samples,) or \
(n_samples, n_outputs)
Target values.
Returns
-------
self : RadiusNeighborsRegressor
The fitted radius neighbors regressor.
"""
return self._fit(X, y)
def predict(self, X):
"""Predict the target for the provided data
Parameters
----------
X : array-like of shape (n_queries, n_features), \
or (n_queries, n_indexed) if metric == 'precomputed'
Test samples.
Returns
-------
y : ndarray of shape (n_queries,) or (n_queries, n_outputs), \
dtype=double
Target values.
"""
X = self._validate_data(X, accept_sparse='csr', reset=False)
neigh_dist, neigh_ind = self.radius_neighbors(X)
weights = _get_weights(neigh_dist, self.weights)
_y = self._y
if _y.ndim == 1:
_y = _y.reshape((-1, 1))
empty_obs = np.full_like(_y[0], np.nan)
if weights is None:
y_pred = np.array([np.mean(_y[ind, :], axis=0)
if len(ind) else empty_obs
for (i, ind) in enumerate(neigh_ind)])
else:
y_pred = np.array([np.average(_y[ind, :], axis=0,
weights=weights[i])
if len(ind) else empty_obs
for (i, ind) in enumerate(neigh_ind)])
if np.any(np.isnan(y_pred)):
empty_warning_msg = ("One or more samples have no neighbors "
"within specified radius; predicting NaN.")
warnings.warn(empty_warning_msg)
if self._y.ndim == 1:
y_pred = y_pred.ravel()
return y_pred