package sklearn

Transform X into a (weighted) graph of neighbors nearer than a radius

The transformed data is a sparse graph as returned by radius_neighbors_graph.

Read more in the :ref:`User Guide <neighbors_transformer>`.

.. versionadded:: 0.22

Parameters ---------- mode : 'distance', 'connectivity', default='distance' 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.

radius : float, default=1. Radius of neighborhood in the transformed sparse graph.

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.

metric : string or callable, default='minkowski' metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used.

If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string.

Distance matrices are not supported.

Valid values for metric are:

• from scikit-learn: 'cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'

• from scipy.spatial.distance: 'braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'

See the documentation for scipy.spatial.distance for details on these metrics.

p : int, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. 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, default=None Additional keyword arguments for the metric function.

n_jobs : int, 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.

Examples -------- >>> from sklearn.cluster import DBSCAN >>> from sklearn.neighbors import RadiusNeighborsTransformer >>> from sklearn.pipeline import make_pipeline >>> estimator = make_pipeline( ... RadiusNeighborsTransformer(radius=42.0, mode='distance'), ... DBSCAN(min_samples=30, metric='precomputed'))

Fit the model using X as training data

Parameters ---------- X : array-like, sparse matrix, BallTree, KDTree Training data. If array or matrix, shape n_samples, n_features, or n_samples, n_samples if metric='precomputed'.

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters ---------- X : array-like of shape (n_samples, n_features) Training set.

y : ignored

Returns ------- Xt : CSR sparse graph, shape (n_samples, n_samples) Xti, j is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit.

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Finds the neighbors within a given radius of a point or points.

Return the indices and distances of each point from the dataset lying in a ball with size ``radius`` around the points of the query array. Points lying on the boundary are included in the results.

The result points are *not* necessarily sorted by distance to their query point.

Parameters ---------- X : array-like, (n_samples, n_features), optional The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

radius : float Limiting distance of neighbors to return. (default is the value passed to the constructor).

return_distance : boolean, optional. Defaults to True. If False, distances will not be returned.

sort_results : boolean, optional. Defaults to False. If True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. If return_distance == False, setting sort_results = True will result in an error.

.. versionadded:: 0.22

Returns ------- neigh_dist : array, shape (n_samples,) of arrays Array representing the distances to each point, only present if return_distance=True. The distance values are computed according to the ``metric`` constructor parameter.

neigh_ind : array, shape (n_samples,) of arrays An array of arrays of indices of the approximate nearest points from the population matrix that lie within a ball of size ``radius`` around the query points.

Examples -------- In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who's the closest point to 1, 1, 1:

>>> import numpy as np >>> samples = [0., 0., 0.], [0., .5, 0.], [1., 1., .5] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.6) >>> neigh.fit(samples) NearestNeighbors(radius=1.6) >>> rng = neigh.radius_neighbors([1., 1., 1.]) >>> print(np.asarray(rng00)) 1.5 0.5 >>> print(np.asarray(rng10)) 1 2

The first array returned contains the distances to all points which are closer than 1.6, while the second array returned contains their indices. In general, multiple points can be queried at the same time.

Notes ----- Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. For efficiency, `radius_neighbors` returns arrays of objects, where each object is a 1D array of indices or distances.

Computes the (weighted) graph of Neighbors for points in X

Neighborhoods are restricted the points at a distance lower than radius.

Parameters ---------- X : array-like of shape (n_samples, n_features), default=None The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

radius : float Radius of neighborhoods. (default is the value passed to the constructor).

mode : 'connectivity', 'distance', optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points.

sort_results : boolean, optional. Defaults to False. If True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. Only used with mode='distance'.

.. versionadded:: 0.22

Returns ------- A : sparse graph in CSR format, shape = n_queries, n_samples_fit n_samples_fit is the number of samples in the fitted data Ai, j is assigned the weight of edge that connects i to j.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) NearestNeighbors(radius=1.5) >>> A = neigh.radius_neighbors_graph(X) >>> A.toarray() array([1., 0., 1.], [0., 1., 0.], [1., 0., 1.])

See also -------- kneighbors_graph

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Computes the (weighted) graph of Neighbors for points in X

Parameters ---------- X : array-like of shape (n_samples_transform, n_features) Sample data

Returns ------- Xt : CSR sparse graph of shape (n_samples_transform, n_samples_fit) Xti, j is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.