skhubness.neighbors.RadiusNeighborsRegressor¶

class
skhubness.neighbors.
RadiusNeighborsRegressor
(radius=1.0, weights='uniform', algorithm: str = 'auto', algorithm_params: dict = None, hubness: str = None, hubness_params: dict = None, leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)[source]¶ 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.
Read more in the scikitlearn User Guide.
 Parameters
 radius: float, optional (default = 1.0)
Range of parameter space to use by default for
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 userdefined 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’, ‘falconn_lsh’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
‘falconn_lsh’ will use
FalconnLSH
‘ball_tree’ will use
BallTree
‘kd_tree’ will use
KDTree
‘brute’ will use a bruteforce 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.
 algorithm_params: dict, optional
Override default parameters of the NN algorithm. For example, with algorithm=’lsh’ and algorithm_params={n_candidates: 100} one hundred approximate neighbors are retrieved with LSH. If parameter hubness is set, the candidate neighbors are further reordered with hubness reduction. Finally, n_neighbors objects are used from the (optionally reordered) candidates.
 hubness: {‘mutual_proximity’, ‘local_scaling’, ‘dis_sim_local’, None}, optional
Hubness reduction algorithm
‘mutual_proximity’ or ‘mp’ will use
MutualProximity
‘local_scaling’ or ‘ls’ will use
LocalScaling
‘dis_sim_local’ or ‘dsl’ will use
DisSimLocal
If None, no hubness reduction will be performed (=vanilla kNN).
 hubness_params: dict, optional
Override default parameters of the selected hubness reduction algorithm. For example, with hubness=’mp’ and hubness_params={‘method’: ‘normal’} a mutual proximity variant is used, which models distance distributions with independent Gaussians.
 leaf_size: int, optional (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.
 p: integer, optional (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.
 metric: string 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 the DistanceMetric class for a list of available metrics.
 metric_params: dict, optional (default = None)
Additional keyword arguments for the metric function.
 n_jobs: int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See scikitlearn Glossary for more details.
Notes
See Nearest Neighbors in the scikitlearn online documentation for a discussion of the choice of
algorithm
andleaf_size
.https://en.wikipedia.org/wiki/Knearest_neighbor_algorithm
Examples
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from skhubness.neighbors import RadiusNeighborsRegressor >>> neigh = RadiusNeighborsRegressor(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [0.5]

__init__
(radius=1.0, weights='uniform', algorithm: str = 'auto', algorithm_params: dict = None, hubness: str = None, hubness_params: dict = None, leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([radius, weights, algorithm, …])Initialize self.
fit
(X, y)Fit the model using X as training data and y as target values
get_params
([deep])Get parameters for this estimator.
kcandidates
([X, n_neighbors, return_distance])Finds the Kneighbors of a point.
predict
(X)Predict the target for the provided data
radius_neighbors
([X, radius, return_distance])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 coefficient of determination R^2 of the prediction.
set_params
(**params)Set the parameters of this estimator.

fit
(X, y)¶ Fit the model using X as training data and y as target values
 Parameters
 X{arraylike, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric=’precomputed’.
 y{arraylike, sparse matrix}
 Target values, array of float values, shape = [n_samples]
or [n_samples, n_outputs]

get_params
(deep=True)¶ 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.

kcandidates
(X=None, n_neighbors=None, return_distance=True) → numpy.ndarray[source]¶ Finds the Kneighbors of a point. Returns indices of and distances to the neighbors of each point.
 Parameters
 Xarraylike, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’
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.
 n_neighborsint
Number of neighbors to get (default is the value passed to the constructor).
 return_distanceboolean, optional. Defaults to True.
If False, distances will not be returned
 Returns
 distarray
Array representing the lengths to points, only present if return_distance=True
 indarray
Indices of the nearest points in the population matrix.
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]
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from skhubness.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=1) >>> neigh.fit(samples) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]]))
As you can see, it returns [[0.5]], and [[2]], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points:
>>> X = [[0., 1., 0.], [1., 0., 1.]] >>> neigh.kneighbors(X, return_distance=False) array([[1], [2]]...)

predict
(X)[source]¶ Predict the target for the provided data
 Parameters
 X: arraylike, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’
Test samples.
 Returns
 y: array of float, shape = [n_samples] or [n_samples, n_outputs]
Target values

radius_neighbors
(X=None, radius=None, return_distance=True)[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
 Xarraylike, (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
 Returns
 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. 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 skhubness.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.6) >>> neigh.fit(samples) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> 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')[source]¶ Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than radius.
 Parameters
 Xarraylike, shape = [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
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
 Asparse 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.toarray() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]])

score
(X, y, sample_weight=None)¶ Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**params)¶ 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.