"""Nearest Neighbors graph functions""" # Author: Jake Vanderplas # # License: BSD 3 clause (C) INRIA, University of Amsterdam from .base import KNeighborsMixin, RadiusNeighborsMixin from .unsupervised import NearestNeighbors def _check_params(X, metric, p, metric_params): """Check the validity of the input parameters""" params = zip(['metric', 'p', 'metric_params'], [metric, p, metric_params]) est_params = X.get_params() for param_name, func_param in params: if func_param != est_params[param_name]: raise ValueError( "Got %s for %s, while the estimator has %s for " "the same parameter." % ( func_param, param_name, est_params[param_name])) def _query_include_self(X, include_self): """Return the query based on include_self param""" if include_self: query = X._fit_X else: query = None return query def kneighbors_graph(X, n_neighbors, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=1): """Computes the (weighted) graph of k-Neighbors for points in X Read more in the :ref:`User Guide `. Parameters ---------- X : array-like or BallTree, shape = [n_samples, n_features] Sample data, in the form of a numpy array or a precomputed :class:`BallTree`. n_neighbors : int Number of neighbors for each sample. mode : {'connectivity', 'distance'}, optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. metric : string, default 'minkowski' The distance metric used to calculate the k-Neighbors for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is 'euclidean' ('minkowski' metric with the p param equal to 2.) include_self: bool, default=False. Whether or not to mark each sample as the first nearest neighbor to itself. If `None`, then True is used for mode='connectivity' and False for mode='distance' as this will preserve backwards compatibilty. 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_params: dict, optional additional keyword arguments for the metric function. n_jobs : int, optional (default = 1) The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Returns ------- A : sparse matrix in CSR format, shape = [n_samples, n_samples] A[i, j] is assigned the weight of edge that connects i to j. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import kneighbors_graph >>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True) >>> A.toarray() array([[ 1., 0., 1.], [ 0., 1., 1.], [ 1., 0., 1.]]) See also -------- radius_neighbors_graph """ if not isinstance(X, KNeighborsMixin): X = NearestNeighbors(n_neighbors, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs).fit(X) else: _check_params(X, metric, p, metric_params) query = _query_include_self(X, include_self) return X.kneighbors_graph(X=query, n_neighbors=n_neighbors, mode=mode) def radius_neighbors_graph(X, radius, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=1): """Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Read more in the :ref:`User Guide `. Parameters ---------- X : array-like or BallTree, shape = [n_samples, n_features] Sample data, in the form of a numpy array or a precomputed :class:`BallTree`. radius : float Radius of neighborhoods. mode : {'connectivity', 'distance'}, optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. metric : string, default 'minkowski' The distance metric used to calculate the neighbors within a given radius for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is 'euclidean' ('minkowski' metric with the param equal to 2.) include_self: bool, default=False Whether or not to mark each sample as the first nearest neighbor to itself. If `None`, then True is used for mode='connectivity' and False for mode='distance' as this will preserve backwards compatibilty. 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_params: dict, optional additional keyword arguments for the metric function. n_jobs : int, optional (default = 1) The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Returns ------- A : sparse matrix in CSR format, shape = [n_samples, n_samples] A[i, j] is assigned the weight of edge that connects i to j. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import radius_neighbors_graph >>> A = radius_neighbors_graph(X, 1.5, mode='connectivity', include_self=True) >>> A.toarray() array([[ 1., 0., 1.], [ 0., 1., 0.], [ 1., 0., 1.]]) See also -------- kneighbors_graph """ if not isinstance(X, RadiusNeighborsMixin): X = NearestNeighbors(radius=radius, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs).fit(X) else: _check_params(X, metric, p, metric_params) query = _query_include_self(X, include_self) return X.radius_neighbors_graph(query, radius, mode)