"""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, _check_weights, NeighborsBase, KNeighborsMixin from ._base import RadiusNeighborsMixin, SupervisedFloatMixin from ..base import RegressorMixin from ..utils import check_array from ..utils.validation import _deprecate_positional_args 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. 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'} 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 ` 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'. 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 ` 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) @property def _pairwise(self): # For cross-validation routines to split data correctly return self.metric == 'precomputed' 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 = check_array(X, accept_sparse='csr') 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(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. 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'} 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 ` 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'. 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 ` 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 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 = check_array(X, accept_sparse='csr') 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