8.20.5. sklearn.neighbors.RadiusNeighborsRegressor¶
- class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30)¶
Regression based on neighbors within a fixed radius.
The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
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 RadiusNeighborsRegressor >>> neigh = RadiusNeighborsRegressor(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsRegressor(...) >>> print neigh.predict([[1.5]]) [ 0.5]
Methods
fit(X, y) Fit the model using X as training data and y as target values predict(X) Predict the target 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 coefficient of determination R^2 of the prediction. 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 float values.
- predict(X)¶
Predict the target for the provided data
Parameters : X : array
A 2-D array representing the test data.
Returns : y: array :
List of target values (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.
See also
Examples
>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.radius_neighbors_graph(X) >>> A.todense() matrix([[ 1., 0., 1.], [ 0., 1., 0.], [ 1., 0., 1.]])
- score(X, y)¶
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.
Parameters : X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Returns : z : float
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :