sklearn.neighbors
.RadiusNeighborsRegressor¶
- class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)[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 User Guide.
New in version 0.9.
- Parameters:
- radiusfloat, default=1.0
Range of parameter space to use by default for
radius_neighbors
queries.- weights{‘uniform’, ‘distance’}, callable or None, 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’
Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance and the metrics listed in
distance_metrics
for valid metric values.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.
If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string.
- 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 ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.
- Attributes:
- effective_metric_str or callable
The distance metric to use. It will be same as the
metric
parameter or a synonym of it, e.g. ‘euclidean’ if themetric
parameter set to ‘minkowski’ andp
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 thep
parameter value if theeffective_metric_
attribute is set to ‘minkowski’.- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
- n_samples_fit_int
Number of samples in the fitted data.
See also
NearestNeighbors
Unsupervised learner for implementing neighbor searches.
KNeighborsRegressor
Regression based on k-nearest neighbors.
KNeighborsClassifier
Classifier based on the k-nearest neighbors.
RadiusNeighborsClassifier
Classifier based on neighbors within a given radius.
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of
algorithm
andleaf_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 RadiusNeighborsRegressor >>> neigh = RadiusNeighborsRegressor(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [0.5]
Methods
fit
(X, y)Fit the radius neighbors regressor from the training dataset.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict the target for the provided data.
radius_neighbors
([X, radius, ...])Find the neighbors within a given radius of a point or points.
radius_neighbors_graph
([X, radius, mode, ...])Compute the (weighted) graph of Neighbors for points in X.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
- fit(X, y)[source]¶
Fit the radius neighbors regressor 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:
- selfRadiusNeighborsRegressor
The fitted radius neighbors regressor.
- 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.
- predict(X)[source]¶
Predict the target for the provided data.
- Parameters:
- X{array-like, sparse matrix} 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), dtype=double
Target values.
- radius_neighbors(X=None, radius=None, return_distance=True, sort_results=False)[source]¶
Find 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:
- X{array-like, sparse matrix} 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
, settingsort_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 themetric
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]¶
Compute the (weighted) graph of Neighbors for points in X.
Neighborhoods are restricted the points at a distance lower than radius.
- Parameters:
- X{array-like, sparse matrix} 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 distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class.
- 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]
gives the weight of the edge connectingi
toj
. The matrix is of CSR format.
See also
kneighbors_graph
Compute the (weighted) graph of k-Neighbors for points in X.
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 coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
- Xarray-like 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 with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
\(R^2\) of
self.predict(X)
w.r.t.y
.
Notes
The \(R^2\) 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)[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.