8.21.3. sklearn.neighbors.RadiusNeighborsClassifier¶

class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30)¶

Classifier implementing a vote among neighbors within a given radius

Parameters :

radius : float, optional (default = 1.0)

Range of parameter space to use by default for :meth`radius_neighbors` queries.

weights : str or callable

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’}, optional

Algorithm used to compute the nearest neighbors:

‘ball_tree’ will use BallTree

‘kd_tree’ will use scipy.spatial.cKDtree

‘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_size : int, optional (default = 30)

Leaf size passed to BallTree or cKDTree. 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.

Notes

See Nearest Neighbors in the online documentation for a discussion of the choice of algorithm and leaf_size.

http://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]

Methods

`fit`(X, y)	Fit the model using X as training data and y as target values
`get_params`([deep])	Get parameters for the estimator
`predict`(X)	Predict the class labels for the provided data
`radius_neighbors`(X[, radius, return_distance])	Finds the neighbors of a point within a given radius.
`radius_neighbors_graph`(X[, radius, mode])	Computes the (weighted) graph of Neighbors for points in X
`score`(X, y)	Returns the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of the estimator.

__init__(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30)¶

fit(X, y)¶

Fit the model using X as training data and y as target values

Parameters :

X : {array-like, sparse matrix, BallTree, cKDTree}

Training data. If array or matrix, then the shape is [n_samples, n_features]

y : {array-like, sparse matrix}, shape = [n_samples]

Target values, array of integer values.

get_params(deep=True)¶

Get parameters for the estimator

Parameters :

deep: boolean, optional :

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

predict(X)¶

Predict the class labels for the provided data

Parameters :

X: array :

A 2-D array representing the test points.

Returns :

labels: array :

List of class labels (one for each data sample).

radius_neighbors(X, radius=None, return_distance=True)¶

Finds the neighbors of a point within a given radius.

Returns distance

Parameters :

X : array-like, last dimension same as that of fit data

The new point.

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

Returns :

dist : array

Array representing the lengths to point, only present if return_distance=True

ind : array

Indices of the nearest points in the population matrix.

Examples

In the following example, we construnct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]

>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(radius=1.6)
>>> neigh.fit(samples) 
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print neigh.radius_neighbors([1., 1., 1.]) 
(array([[ 1.5,  0.5]]...), array([[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. Because the number of neighbors of each point is not necessarily equal, radius_neighbors returns an array of objects, where each object is a 1D array of indices.

radius_neighbors_graph(X, radius=None, mode='connectivity')¶

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, shape = [n_samples, n_features]

Sample data

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.

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.

Citing

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8.21.3. sklearn.neighbors.RadiusNeighborsClassifier¶