sklearn.neighbors.RadiusNeighborsClassifier

class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', outlier_label=None, metric_params=None, n_jobs=None, **kwargs)[source]

Classifier implementing a vote among neighbors within a given radius

Read more in the User Guide.

Parameters
radiusfloat, default=1.0

Range of parameter space to use by default for 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 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.

pint, 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.

metricstr 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 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 sparse graph, in which case only “nonzero” elements may be considered neighbors.

outlier_label{manual label, ‘most_frequent’}, default=None

label for outlier samples (samples with no neighbors in given radius).

  • manual label: str or int label (should be the same type as y) or list of manual labels if multi-output is used.

  • ‘most_frequent’ : assign the most frequent label of y to outliers.

  • None : when any outlier is detected, ValueError will be raised.

metric_paramsdict, default=None

Additional keyword arguments for the metric function.

n_jobsint, default=None

The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Attributes
classes_ndarray of shape (n_classes,)

Class labels known to the classifier.

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.

outlier_label_int or array-like of shape (n_class,)

Label which is given for outlier samples (samples with no neighbors on given radius).

outputs_2d_bool

False when y’s shape is (n_samples, ) or (n_samples, 1) during fit otherwise True.

Notes

See 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

Examples

>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import RadiusNeighborsClassifier
>>> neigh = RadiusNeighborsClassifier(radius=1.0)
>>> neigh.fit(X, y)
RadiusNeighborsClassifier(...)
>>> print(neigh.predict([[1.5]]))
[0]
>>> print(neigh.predict_proba([[1.0]]))
[[0.66666667 0.33333333]]

Methods

fit(X, y)

Fit the radius neighbors classifier from the training dataset.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict the class labels for the provided data.

predict_proba(X)

Return probability estimates for the test data X.

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

score(X, y[, sample_weight])

Return the mean accuracy on the given test data and labels.

set_params(**params)

Set the parameters of this estimator.

fit(X, y)[source]

Fit the radius neighbors classifier 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.

y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)

Target values.

Returns
selfRadiusNeighborsClassifier

The fitted radius neighbors classifier.

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
paramsmapping of string to any

Parameter names mapped to their values.

predict(X)[source]

Predict the class labels for the provided data.

Parameters
Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

Test samples.

Returns
yndarray of shape (n_queries,) or (n_queries, n_outputs)

Class labels for each data sample.

predict_proba(X)[source]

Return probability estimates for the test data X.

Parameters
Xarray-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

Test samples.

Returns
pndarray of shape (n_queries, n_classes), or a list of n_outputs

of such arrays if n_outputs > 1. The class probabilities of the input samples. Classes are ordered by lexicographic order.

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, (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.

radiusfloat

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

return_distanceboolean, optional. Defaults to True.

If False, distances will not be returned.

sort_resultsboolean, 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.

New in version 0.22.

Returns
neigh_distarray, 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_indarray, 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

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_resultsboolean, 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’.

New in version 0.22.

Returns
Asparse graph in CSR format, 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.

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.]])
score(X, y, sample_weight=None)[source]

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns
scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(**params)[source]

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
**paramsdict

Estimator parameters.

Returns
selfobject

Estimator instance.