"""Nearest Neighbor Regression.""" # Authors: Jake Vanderplas # Fabian Pedregosa # Alexandre Gramfort # Sparseness support by Lars Buitinck # Multi-output support by Arnaud Joly # 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 from ._base import NeighborsBase, KNeighborsMixin, RadiusNeighborsMixin from ..base import RegressorMixin from ..utils._param_validation import StrOptions 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 `. .. versionadded:: 0.9 Parameters ---------- n_neighbors : int, default=5 Number of neighbors to use by default for :meth:`kneighbors` queries. weights : {'uniform', 'distance'}, callable or None, 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' 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 `_ 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. 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 ` 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_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 -------- NearestNeighbors : Unsupervised learner for implementing neighbor searches. RadiusNeighborsRegressor : Regression based on neighbors within a fixed radius. KNeighborsClassifier : Classifier implementing the k-nearest neighbors vote. RadiusNeighborsClassifier : Classifier implementing a vote among neighbors within a given radius. Notes ----- See :ref:`Nearest 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_neighbors_algorithm 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] """ _parameter_constraints: dict = { **NeighborsBase._parameter_constraints, "weights": [StrOptions({"uniform", "distance"}), callable, None], } _parameter_constraints.pop("radius") def __init__( self, n_neighbors=5, *, weights="uniform", algorithm="auto", leaf_size=30, p=2, metric="minkowski", metric_params=None, n_jobs=None, ): super().__init__( n_neighbors=n_neighbors, algorithm=algorithm, leaf_size=leaf_size, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs, ) self.weights = weights def _more_tags(self): # For cross-validation routines to split data correctly return {"pairwise": 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. """ self._validate_params() return self._fit(X, y) def predict(self, X): """Predict the target for the provided data. Parameters ---------- X : {array-like, sparse matrix} 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. """ if self.weights == "uniform": # In that case, we do not need the distances to perform # the weighting so we do not compute them. neigh_ind = self.kneighbors(X, return_distance=False) neigh_dist = None else: 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((neigh_dist.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 `. .. 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'}, callable or None, 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' 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 `_ 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. 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 ` 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_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 -------- NearestNeighbors : Unsupervised learner for implementing neighbor searches. KNeighborsRegressor : Regression based on k-nearest neighbors. KNeighborsClassifier : Classifier based on the k-nearest neighbors. RadiusNeighborsClassifier : Classifier based on neighbors within a given radius. Notes ----- See :ref:`Nearest 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 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] """ _parameter_constraints: dict = { **NeighborsBase._parameter_constraints, "weights": [StrOptions({"uniform", "distance"}), callable, None], } _parameter_constraints.pop("n_neighbors") def __init__( self, radius=1.0, *, weights="uniform", algorithm="auto", leaf_size=30, p=2, metric="minkowski", metric_params=None, n_jobs=None, ): super().__init__( radius=radius, algorithm=algorithm, leaf_size=leaf_size, p=p, metric=metric, metric_params=metric_params, n_jobs=n_jobs, ) self.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. """ self._validate_params() return self._fit(X, y) def predict(self, X): """Predict the target for the provided data. Parameters ---------- X : {array-like, sparse matrix} 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. """ 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