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# `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)

  Classifier implementing a vote among neighbors within a given radius.

  Read more in the User Guide.

  Parameters:
    radius float, 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_size int, 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 float, 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 str 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.

    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_params dict, default=None

      Additional keyword arguments for the metric function.

    n_jobs int, 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_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.

    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.

  See also

  `KNeighborsClassifier`

    Classifier implementing the k-nearest neighbors vote.

  `RadiusNeighborsRegressor`

    Regression based on neighbors within a fixed radius.

  `KNeighborsRegressor`

    Regression based on k-nearest neighbors.

  `NearestNeighbors`

    Unsupervised learner for implementing neighbor searches.

  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_metadata_routing`() | Get metadata routing of this object.
  `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, ...]) | 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 mean accuracy on the given test data and labels.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y)

    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:
      self RadiusNeighborsClassifier

        The fitted radius neighbors classifier.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    Predict the class labels 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:
      y ndarray of shape (n_queries,) or (n_queries, n_outputs)

        Class labels for each data sample.

  predict_proba(X)

    Return probability estimates for the test data X.

    Parameters:
      X{array-like, sparse matrix} of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

        Test samples.

    Returns:
      p ndarray 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)

    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.

      radius float, default=None

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

      return_distance bool, default=True

        Whether or not to return the distances.

      sort_results bool, 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`, setting `sort_results=True` will result in an error.

        New in version 0.22.

    Returns:
      neigh_dist ndarray 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 the `metric` constructor parameter.

      neigh_ind ndarray 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)

    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.

      radius float, 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_results bool, 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:
      A sparse-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 connecting `i` to `j`. 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)

    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:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        Mean accuracy of `self.predict(X)` w.r.t. `y`.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RadiusNeighborsClassifier

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.