511 lines
18 KiB
Python
511 lines
18 KiB
Python
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"""Nearest Neighbor Regression."""
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# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
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# Fabian Pedregosa <fabian.pedregosa@inria.fr>
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# Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Sparseness support by Lars Buitinck
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# Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
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# Empty radius support by Andreas Bjerre-Nielsen
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#
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# License: BSD 3 clause (C) INRIA, University of Amsterdam,
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# University of Copenhagen
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import warnings
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import numpy as np
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from ..base import RegressorMixin, _fit_context
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from ..metrics import DistanceMetric
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from ..utils._param_validation import StrOptions
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from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin, _get_weights
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class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):
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"""Regression based on k-nearest neighbors.
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The target is predicted by local interpolation of the targets
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associated of the nearest neighbors in the training set.
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Read more in the :ref:`User Guide <regression>`.
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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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weights : {'uniform', 'distance'}, callable or None, default='uniform'
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Weight function used in prediction. Possible values:
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- 'uniform' : uniform weights. All points in each neighborhood
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are weighted equally.
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- 'distance' : weight points by the inverse of their distance.
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in this case, closer neighbors of a query point will have a
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greater influence than neighbors which are further away.
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- [callable] : a user-defined function which accepts an
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array of distances, and returns an array of the same shape
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containing the weights.
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Uniform weights are used by default.
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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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p : float, default=2
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Power parameter for the Minkowski metric. 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 : str, DistanceMetric object or callable, default='minkowski'
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Metric to use for distance computation. Default is "minkowski", which
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results in the standard Euclidean distance when p = 2. See the
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documentation of `scipy.spatial.distance
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<https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
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the metrics listed in
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:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
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values.
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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`, in which
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case only "nonzero" elements may be considered neighbors.
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If metric is a callable function, it takes two arrays representing 1D
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vectors as inputs and must return one value indicating the distance
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between those vectors. This works for Scipy's metrics, but is less
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efficient than passing the metric name as a string.
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If metric is a DistanceMetric object, it will be passed directly to
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the underlying computation routines.
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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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Doesn't affect :meth:`fit` method.
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Attributes
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----------
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effective_metric_ : str or callable
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The distance metric to use. It will be same as the `metric` parameter
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or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
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'minkowski' and `p` parameter set to 2.
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effective_metric_params_ : dict
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Additional keyword arguments for the metric function. For most metrics
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will be same with `metric_params` parameter, but may also contain the
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`p` parameter value if the `effective_metric_` attribute is set to
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'minkowski'.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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n_samples_fit_ : int
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Number of samples in the fitted data.
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See Also
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--------
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NearestNeighbors : Unsupervised learner for implementing neighbor searches.
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RadiusNeighborsRegressor : Regression based on neighbors within a fixed radius.
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KNeighborsClassifier : Classifier implementing the k-nearest neighbors vote.
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RadiusNeighborsClassifier : Classifier implementing
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a vote among neighbors within a given radius.
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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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.. warning::
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Regarding the Nearest Neighbors algorithms, if it is found that two
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neighbors, neighbor `k+1` and `k`, have identical distances but
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different labels, the results will depend on the ordering of the
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training data.
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https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
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Examples
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--------
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>>> X = [[0], [1], [2], [3]]
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>>> y = [0, 0, 1, 1]
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>>> from sklearn.neighbors import KNeighborsRegressor
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>>> neigh = KNeighborsRegressor(n_neighbors=2)
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>>> neigh.fit(X, y)
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KNeighborsRegressor(...)
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>>> print(neigh.predict([[1.5]]))
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[0.5]
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"""
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_parameter_constraints: dict = {
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**NeighborsBase._parameter_constraints,
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"weights": [StrOptions({"uniform", "distance"}), callable, None],
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}
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_parameter_constraints["metric"].append(DistanceMetric)
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_parameter_constraints.pop("radius")
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def __init__(
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self,
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n_neighbors=5,
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*,
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weights="uniform",
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algorithm="auto",
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leaf_size=30,
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p=2,
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metric="minkowski",
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metric_params=None,
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n_jobs=None,
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):
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super().__init__(
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n_neighbors=n_neighbors,
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algorithm=algorithm,
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leaf_size=leaf_size,
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metric=metric,
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p=p,
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metric_params=metric_params,
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n_jobs=n_jobs,
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)
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self.weights = weights
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def _more_tags(self):
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# For cross-validation routines to split data correctly
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return {"pairwise": self.metric == "precomputed"}
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@_fit_context(
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# KNeighborsRegressor.metric is not validated yet
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prefer_skip_nested_validation=False
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)
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def fit(self, X, y):
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"""Fit the k-nearest neighbors regressor from the training dataset.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
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(n_samples, n_samples) if metric='precomputed'
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Training data.
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y : {array-like, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_outputs)
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Target values.
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Returns
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-------
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self : KNeighborsRegressor
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The fitted k-nearest neighbors regressor.
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"""
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return self._fit(X, y)
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def predict(self, X):
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"""Predict the target for the provided data.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_queries, n_features), \
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or (n_queries, n_indexed) if metric == 'precomputed'
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Test samples.
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Returns
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-------
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y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int
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Target values.
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"""
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if self.weights == "uniform":
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# In that case, we do not need the distances to perform
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# the weighting so we do not compute them.
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neigh_ind = self.kneighbors(X, return_distance=False)
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neigh_dist = None
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else:
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neigh_dist, neigh_ind = self.kneighbors(X)
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weights = _get_weights(neigh_dist, self.weights)
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_y = self._y
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if _y.ndim == 1:
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_y = _y.reshape((-1, 1))
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if weights is None:
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y_pred = np.mean(_y[neigh_ind], axis=1)
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else:
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y_pred = np.empty((neigh_dist.shape[0], _y.shape[1]), dtype=np.float64)
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denom = np.sum(weights, axis=1)
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for j in range(_y.shape[1]):
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num = np.sum(_y[neigh_ind, j] * weights, axis=1)
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y_pred[:, j] = num / denom
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if self._y.ndim == 1:
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y_pred = y_pred.ravel()
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return y_pred
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class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBase):
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"""Regression based on neighbors within a fixed radius.
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The target is predicted by local interpolation of the targets
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associated of the nearest neighbors in the training set.
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Read more in the :ref:`User Guide <regression>`.
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.. versionadded:: 0.9
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Parameters
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----------
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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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weights : {'uniform', 'distance'}, callable or None, default='uniform'
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Weight function used in prediction. Possible values:
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- 'uniform' : uniform weights. All points in each neighborhood
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are weighted equally.
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- 'distance' : weight points by the inverse of their distance.
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in this case, closer neighbors of a query point will have a
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greater influence than neighbors which are further away.
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- [callable] : a user-defined function which accepts an
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array of distances, and returns an array of the same shape
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containing the weights.
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Uniform weights are used by default.
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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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|
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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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p : float, default=2
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Power parameter for the Minkowski metric. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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|
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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|
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metric : str or callable, default='minkowski'
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Metric to use for distance computation. Default is "minkowski", which
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results in the standard Euclidean distance when p = 2. See the
|
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|
|
documentation of `scipy.spatial.distance
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|
|
<https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
|
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|
|
the metrics listed in
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|
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:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
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|
|
values.
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|
|
|
||
|
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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.
|
||
|
|
|
||
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If metric is a callable function, it takes two arrays representing 1D
|
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|
|
vectors as inputs and must return one value indicating the distance
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|
|
between those vectors. This works for Scipy's metrics, but is less
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|
|
efficient than passing the metric name as a string.
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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 or callable
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The distance metric to use. It will be same as the `metric` parameter
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|
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or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
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'minkowski' and `p` parameter set to 2.
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effective_metric_params_ : dict
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Additional keyword arguments for the metric function. For most metrics
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will be same with `metric_params` parameter, but may also contain the
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`p` parameter value if the `effective_metric_` attribute is set to
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'minkowski'.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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n_samples_fit_ : int
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Number of samples in the fitted data.
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See Also
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--------
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NearestNeighbors : Unsupervised learner for implementing neighbor searches.
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KNeighborsRegressor : Regression based on k-nearest neighbors.
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KNeighborsClassifier : Classifier based on the k-nearest neighbors.
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RadiusNeighborsClassifier : Classifier based on neighbors within a given radius.
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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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Examples
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--------
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>>> X = [[0], [1], [2], [3]]
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>>> y = [0, 0, 1, 1]
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>>> from sklearn.neighbors import RadiusNeighborsRegressor
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>>> neigh = RadiusNeighborsRegressor(radius=1.0)
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>>> neigh.fit(X, y)
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RadiusNeighborsRegressor(...)
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>>> print(neigh.predict([[1.5]]))
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[0.5]
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"""
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_parameter_constraints: dict = {
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**NeighborsBase._parameter_constraints,
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"weights": [StrOptions({"uniform", "distance"}), callable, None],
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}
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_parameter_constraints.pop("n_neighbors")
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def __init__(
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self,
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radius=1.0,
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*,
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weights="uniform",
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algorithm="auto",
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leaf_size=30,
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p=2,
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metric="minkowski",
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metric_params=None,
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n_jobs=None,
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):
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super().__init__(
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radius=radius,
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algorithm=algorithm,
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leaf_size=leaf_size,
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p=p,
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metric=metric,
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metric_params=metric_params,
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n_jobs=n_jobs,
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)
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self.weights = weights
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@_fit_context(
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# RadiusNeighborsRegressor.metric is not validated yet
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prefer_skip_nested_validation=False
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)
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def fit(self, X, y):
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"""Fit the radius neighbors regressor from the training dataset.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
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(n_samples, n_samples) if metric='precomputed'
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Training data.
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y : {array-like, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_outputs)
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Target values.
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Returns
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-------
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self : RadiusNeighborsRegressor
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The fitted radius neighbors regressor.
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"""
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return self._fit(X, y)
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def predict(self, X):
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"""Predict the target for the provided data.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_queries, n_features), \
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or (n_queries, n_indexed) if metric == 'precomputed'
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Test samples.
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Returns
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-------
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y : ndarray of shape (n_queries,) or (n_queries, n_outputs), \
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dtype=double
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Target values.
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"""
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neigh_dist, neigh_ind = self.radius_neighbors(X)
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weights = _get_weights(neigh_dist, self.weights)
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_y = self._y
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if _y.ndim == 1:
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_y = _y.reshape((-1, 1))
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empty_obs = np.full_like(_y[0], np.nan)
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if weights is None:
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y_pred = np.array(
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[
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np.mean(_y[ind, :], axis=0) if len(ind) else empty_obs
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for (i, ind) in enumerate(neigh_ind)
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]
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)
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else:
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y_pred = np.array(
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[
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(
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np.average(_y[ind, :], axis=0, weights=weights[i])
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if len(ind)
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else empty_obs
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)
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for (i, ind) in enumerate(neigh_ind)
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]
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)
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if np.any(np.isnan(y_pred)):
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empty_warning_msg = (
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"One or more samples have no neighbors "
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"within specified radius; predicting NaN."
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)
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warnings.warn(empty_warning_msg)
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if self._y.ndim == 1:
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y_pred = y_pred.ravel()
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return y_pred
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