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=1) [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. Read more in the User Guide.  New in version 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.  
radiusfloat, 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 BallTree
 ‘kd_tree’ will use KDTree
 ‘brute’ will use a brute-force search. ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit method.  Note: fitting on sparse input will override the setting of this parameter, using brute force.  
leaf_sizeint, 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.  
metricstr 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.  
pint, 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_paramsdict, default=None 

Additional keyword arguments for the metric function.  
n_jobsint, 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.    Attributes 
 
effective_metric_str or callable 

The distance metric used. 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_samples_fit_int 

Number of samples in the fitted data.     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'))
 Methods  
fit(X[, y]) Fit the radius neighbors transformer from the training dataset.  
fit_transform(X[, y]) Fit to data, then transform it.  
get_params([deep]) Get parameters for this estimator.  
radius_neighbors([X, radius, …]) Finds the neighbors within a given radius of a point or points.  
radius_neighbors_graph([X, radius, mode, …]) Computes the (weighted) graph of Neighbors for points in X  
set_params(**params) Set the parameters of this estimator.  
transform(X) Computes 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.    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 
  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_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]
 
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 
 
Xarray-like 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]
 
Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius.  Parameters 
 
Xarray-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.  
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 Euclidean distance between points.  
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] is assigned the weight of edge that connects i to j. The matrix if of format CSR.      See also  
kneighbors_graph

  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.]])
 
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Computes the (weighted) graph of Neighbors for points in X  Parameters 
 
Xarray-like of shape (n_samples_transform, n_features) 

Sample data    Returns 
 
Xtsparse matrix of shape (n_samples_transform, n_samples_fit) 

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.