`sklearn.neighbors`.RadiusNeighborsTransformer#

class sklearn.neighbors.RadiusNeighborsTransformer(*, mode='distance', radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None)[source]#

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.

See also

kneighbors_graph: Compute the weighted graph of k-neighbors for points in X.
KNeighborsTransformer: Transform X into a weighted graph of k nearest neighbors.

Examples

>>> import numpy as np
>>> from sklearn.datasets import load_wine
>>> from sklearn.cluster import DBSCAN
>>> from sklearn.neighbors import RadiusNeighborsTransformer
>>> from sklearn.pipeline import make_pipeline
>>> X, _ = load_wine(return_X_y=True)
>>> estimator = make_pipeline(
...     RadiusNeighborsTransformer(radius=42.0, mode='distance'),
...     DBSCAN(eps=25.0, metric='precomputed'))
>>> X_clustered = estimator.fit_predict(X)
>>> clusters, counts = np.unique(X_clustered, return_counts=True)
>>> print(counts)
[ 29  15 111  11  12]

Methods

`fit`(X[, y])	Fit the radius neighbors transformer from the training dataset.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`radius_neighbors`([X, radius, ...])	Find the neighbors within a given radius of a point or points.
`radius_neighbors_graph`([X, radius, mode, ...])	Compute the (weighted) graph of Neighbors for points in X.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Compute the (weighted) graph of Neighbors for points in X.

fit(X, y=None)[source]#

Fit the radius neighbors transformer 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.
yIgnored: Not used, present for API consistency by convention.

Returns:

selfRadiusNeighborsTransformer: The fitted radius neighbors transformer.

fit_transform(X, y=None)[source]#

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:

Xarray-like of shape (n_samples, n_features): Training set.
yIgnored: Not used, present for API consistency by convention.

Returns:

Xtsparse matrix of shape (n_samples, n_samples): Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].

Parameters:

input_featuresarray-like of str or None, default=None: Only used to validate feature names with the names seen in fit.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

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

Returns:

paramsdict: Parameter names mapped to their values.

radius_neighbors(X=None, radius=None, return_distance=True, sort_results=False)[source]#

Find 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, sparse matrix} of (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.
radiusfloat, default=None: Limiting distance of neighbors to return. The default is the value passed to the constructor.
return_distancebool, default=True: Whether or not to return the distances.
sort_resultsbool, default=False: If True, the distances and indices will be sorted by increasing distances before being returned. If False, the results may not be sorted. If return_distance=False, setting sort_results=True will result in an error.

New in version 0.22.

Returns:

neigh_distndarray of 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_indndarray of 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.

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.

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(rng[0][0]))
[1.5 0.5]
>>> print(np.asarray(rng[1][0]))
[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.

radius_neighbors_graph(X=None, radius=None, mode='connectivity', sort_results=False)[source]#

Compute the (weighted) graph of Neighbors for points in X.

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

Parameters:

X{array-like, sparse matrix} 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.
radiusfloat, default=None: Radius of neighborhoods. The default is the value passed to the constructor.
mode{‘connectivity’, ‘distance’}, default=’connectivity’: Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class.
sort_resultsbool, default=False: If True, in each row of the result, the non-zero entries will be sorted by increasing distances. If False, the non-zero entries may not be sorted. Only used with mode=’distance’.

New in version 0.22.

Returns:

Asparse-matrix of shape (n_queries, n_samples_fit): n_samples_fit is the number of samples in the fitted data. A[i, j] gives the weight of the edge connecting i to j. The matrix is of CSR format.

sklearn.neighbors.RadiusNeighborsTransformer#

`sklearn.neighbors`.RadiusNeighborsTransformer#