content: """Nearest Neighbor Classification"""
  
  # Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
  #          Fabian Pedregosa <fabian.pedregosa@inria.fr>
  #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #          Sparseness support by Lars Buitinck
  #          Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
  #
  # License: BSD 3 clause (C) INRIA, University of Amsterdam
  
  import numpy as np
  from scipy import stats
  from ..utils.extmath import weighted_mode
  from ..utils.validation import _is_arraylike, _num_samples
  
  import warnings
  from ._base import _check_weights, _get_weights
  from ._base import NeighborsBase, KNeighborsMixin, RadiusNeighborsMixin
  from ..base import ClassifierMixin
  from ..utils import check_array
  from ..utils.validation import _deprecate_positional_args
  
  
  class KNeighborsClassifier(KNeighborsMixin,
                             ClassifierMixin,
                             NeighborsBase):
      """Classifier implementing the k-nearest neighbors vote.
  
      Read more in the :ref:`User Guide <classification>`.
  
      Parameters
      ----------
      n_neighbors : int, default=5
          Number of neighbors to use by default for :meth:`kneighbors` queries.
  
      weights : {'uniform', 'distance'} or callable, 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.
  
      algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
          Algorithm used to compute the nearest neighbors:
  
          - 'ball_tree' will use :class:`BallTree`
          - 'kd_tree' will use :class:`KDTree`
          - 'brute' will use a brute-force search.
          - 'auto' will attempt to decide the most appropriate algorithm
            based on the values passed to :meth:`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 : int, 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'
          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 :class:`DistanceMetric` for a
          list of available metrics.
          If metric is "precomputed", X is assumed to be a distance matrix and
          must be square during fit. X may be a :term:`sparse graph`,
          in which case only "nonzero" elements may be considered neighbors.
  
      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 :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          for more details.
          Doesn't affect :meth:`fit` method.
  
      Attributes
      ----------
      classes_ : array of shape (n_classes,)
          Class labels known to the classifier
  
      effective_metric_ : str or callble
          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_samples_fit_ : int
          Number of samples in the fitted data.
  
      outputs_2d_ : bool
          False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
          otherwise True.
  
      Examples
      --------
      >>> X = [[0], [1], [2], [3]]
      >>> y = [0, 0, 1, 1]
      >>> from sklearn.neighbors import KNeighborsClassifier
      >>> neigh = KNeighborsClassifier(n_neighbors=3)
      >>> neigh.fit(X, y)
      KNeighborsClassifier(...)
      >>> print(neigh.predict([[1.1]]))
      [0]
      >>> print(neigh.predict_proba([[0.9]]))
      [[0.66666667 0.33333333]]
  
      See also
      --------
      RadiusNeighborsClassifier
      KNeighborsRegressor
      RadiusNeighborsRegressor
      NearestNeighbors
  
      Notes
      -----
      See :ref:`Nearest Neighbors <neighbors>` in the online documentation
      for a discussion of the choice of ``algorithm`` and ``leaf_size``.
  
      .. warning::
  
         Regarding the Nearest Neighbors algorithms, if it is found that two
         neighbors, neighbor `k+1` and `k`, have identical distances
         but different labels, the results will depend on the ordering of the
         training data.
  
      https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
      """
  
      @_deprecate_positional_args
      def __init__(self, n_neighbors=5, *,
                   weights='uniform', algorithm='auto', leaf_size=30,
                   p=2, metric='minkowski', metric_params=None, n_jobs=None,
                   **kwargs):
          super().__init__(
              n_neighbors=n_neighbors,
              algorithm=algorithm,
              leaf_size=leaf_size, metric=metric, p=p,
              metric_params=metric_params,
              n_jobs=n_jobs, **kwargs)
          self.weights = _check_weights(weights)
  
      def fit(self, X, y):
          """Fit the k-nearest 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 : KNeighborsClassifier
              The fitted k-nearest neighbors classifier.
          """
          return self._fit(X, y)
  
      def predict(self, X):
          """Predict the class labels for the provided data.
  
          Parameters
          ----------
          X : array-like 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.
          """
          X = check_array(X, accept_sparse='csr')
  
          neigh_dist, neigh_ind = self.kneighbors(X)
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
  
          n_outputs = len(classes_)
          n_queries = _num_samples(X)
          weights = _get_weights(neigh_dist, self.weights)
  
          y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)
          for k, classes_k in enumerate(classes_):
              if weights is None:
                  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
              else:
                  mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)
  
              mode = np.asarray(mode.ravel(), dtype=np.intp)
              y_pred[:, k] = classes_k.take(mode)
  
          if not self.outputs_2d_:
              y_pred = y_pred.ravel()
  
          return y_pred
  
      def predict_proba(self, X):
          """Return probability estimates for the test data X.
  
          Parameters
          ----------
          X : array-like 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.
          """
          X = check_array(X, accept_sparse='csr')
  
          neigh_dist, neigh_ind = self.kneighbors(X)
  
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
  
          n_queries = _num_samples(X)
  
          weights = _get_weights(neigh_dist, self.weights)
          if weights is None:
              weights = np.ones_like(neigh_ind)
  
          all_rows = np.arange(X.shape[0])
          probabilities = []
          for k, classes_k in enumerate(classes_):
              pred_labels = _y[:, k][neigh_ind]
              proba_k = np.zeros((n_queries, classes_k.size))
  
              # a simple ':' index doesn't work right
              for i, idx in enumerate(pred_labels.T):  # loop is O(n_neighbors)
                  proba_k[all_rows, idx] += weights[:, i]
  
              # normalize 'votes' into real [0,1] probabilities
              normalizer = proba_k.sum(axis=1)[:, np.newaxis]
              normalizer[normalizer == 0.0] = 1.0
              proba_k /= normalizer
  
              probabilities.append(proba_k)
  
          if not self.outputs_2d_:
              probabilities = probabilities[0]
  
          return probabilities
  
  
  class RadiusNeighborsClassifier(RadiusNeighborsMixin,
                                  ClassifierMixin,
                                  NeighborsBase):
      """Classifier implementing a vote among neighbors within a given radius
  
      Read more in the :ref:`User Guide <classification>`.
  
      Parameters
      ----------
      radius : float, default=1.0
          Range of parameter space to use by default for :meth:`radius_neighbors`
          queries.
  
      weights : {'uniform', 'distance'} or callable, 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 :class:`BallTree`
          - 'kd_tree' will use :class:`KDTree`
          - 'brute' will use a brute-force search.
          - 'auto' will attempt to decide the most appropriate algorithm
            based on the values passed to :meth:`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 : int, 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'
          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 :class:`DistanceMetric` for a
          list of available metrics.
          If metric is "precomputed", X is assumed to be a distance matrix and
          must be square during fit. X may be a :term:`sparse graph`,
          in which case only "nonzero" elements may be considered neighbors.
  
      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 :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          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_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.
  
      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]]
  
      See also
      --------
      KNeighborsClassifier
      RadiusNeighborsRegressor
      KNeighborsRegressor
      NearestNeighbors
  
      Notes
      -----
      See :ref:`Nearest Neighbors <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
      """
  
      @_deprecate_positional_args
      def __init__(self, radius=1.0, *, weights='uniform',
                   algorithm='auto', leaf_size=30, p=2, metric='minkowski',
                   outlier_label=None, metric_params=None, n_jobs=None,
                   **kwargs):
          super().__init__(
                radius=radius,
                algorithm=algorithm,
                leaf_size=leaf_size,
                metric=metric, p=p, metric_params=metric_params,
                n_jobs=n_jobs, **kwargs)
          self.weights = _check_weights(weights)
          self.outlier_label = outlier_label
  
      def fit(self, 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.
          """
          self._fit(X, y)
  
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
  
          if self.outlier_label is None:
              outlier_label_ = None
  
          elif self.outlier_label == 'most_frequent':
              outlier_label_ = []
              # iterate over multi-output, get the most frequent label for each
              # output.
              for k, classes_k in enumerate(classes_):
                  label_count = np.bincount(_y[:, k])
                  outlier_label_.append(classes_k[label_count.argmax()])
  
          else:
              if (_is_arraylike(self.outlier_label) and
                 not isinstance(self.outlier_label, str)):
                  if len(self.outlier_label) != len(classes_):
                      raise ValueError("The length of outlier_label: {} is "
                                       "inconsistent with the output "
                                       "length: {}".format(self.outlier_label,
                                                           len(classes_)))
                  outlier_label_ = self.outlier_label
              else:
                  outlier_label_ = [self.outlier_label] * len(classes_)
  
              for classes, label in zip(classes_, outlier_label_):
                  if (_is_arraylike(label) and
                     not isinstance(label, str)):
                      # ensure the outlier lable for each output is a scalar.
                      raise TypeError("The outlier_label of classes {} is "
                                      "supposed to be a scalar, got "
                                      "{}.".format(classes, label))
                  if np.append(classes, label).dtype != classes.dtype:
                      # ensure the dtype of outlier label is consistent with y.
                      raise TypeError("The dtype of outlier_label {} is "
                                      "inconsistent with classes {} in "
                                      "y.".format(label, classes))
  
          self.outlier_label_ = outlier_label_
  
          return self
  
      def predict(self, X):
          """Predict the class labels for the provided data.
  
          Parameters
          ----------
          X : array-like 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.
          """
  
          probs = self.predict_proba(X)
          classes_ = self.classes_
  
          if not self.outputs_2d_:
              probs = [probs]
              classes_ = [self.classes_]
  
          n_outputs = len(classes_)
          n_queries = probs[0].shape[0]
          y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)
  
          for k, prob in enumerate(probs):
              # iterate over multi-output, assign labels based on probabilities
              # of each output.
              max_prob_index = prob.argmax(axis=1)
              y_pred[:, k] = classes_[k].take(max_prob_index)
  
              outlier_zero_probs = (prob == 0).all(axis=1)
              if outlier_zero_probs.any():
                  zero_prob_index = np.flatnonzero(outlier_zero_probs)
                  y_pred[zero_prob_index, k] = self.outlier_label_[k]
  
          if not self.outputs_2d_:
              y_pred = y_pred.ravel()
  
          return y_pred
  
      def predict_proba(self, X):
          """Return probability estimates for the test data X.
  
          Parameters
          ----------
          X : array-like 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.
          """
  
          X = check_array(X, accept_sparse='csr')
          n_queries = _num_samples(X)
  
          neigh_dist, neigh_ind = self.radius_neighbors(X)
          outlier_mask = np.zeros(n_queries, dtype=bool)
          outlier_mask[:] = [len(nind) == 0 for nind in neigh_ind]
          outliers = np.flatnonzero(outlier_mask)
          inliers = np.flatnonzero(~outlier_mask)
  
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
  
          if self.outlier_label_ is None and outliers.size > 0:
              raise ValueError('No neighbors found for test samples %r, '
                               'you can try using larger radius, '
                               'giving a label for outliers, '
                               'or considering removing them from your dataset.'
                               % outliers)
  
          weights = _get_weights(neigh_dist, self.weights)
          if weights is not None:
              weights = weights[inliers]
  
          probabilities = []
          # iterate over multi-output, measure probabilities of the k-th output.
          for k, classes_k in enumerate(classes_):
              pred_labels = np.zeros(len(neigh_ind), dtype=object)
              pred_labels[:] = [_y[ind, k] for ind in neigh_ind]
  
              proba_k = np.zeros((n_queries, classes_k.size))
              proba_inl = np.zeros((len(inliers), classes_k.size))
  
              # samples have different size of neighbors within the same radius
              if weights is None:
                  for i, idx in enumerate(pred_labels[inliers]):
                      proba_inl[i, :] = np.bincount(idx,
                                                    minlength=classes_k.size)
              else:
                  for i, idx in enumerate(pred_labels[inliers]):
                      proba_inl[i, :] = np.bincount(idx,
                                                    weights[i],
                                                    minlength=classes_k.size)
              proba_k[inliers, :] = proba_inl
  
              if outliers.size > 0:
                  _outlier_label = self.outlier_label_[k]
                  label_index = np.flatnonzero(classes_k == _outlier_label)
                  if label_index.size == 1:
                      proba_k[outliers, label_index[0]] = 1.0
                  else:
                      warnings.warn('Outlier label {} is not in training '
                                    'classes. All class probabilities of '
                                    'outliers will be assigned with 0.'
                                    ''.format(self.outlier_label_[k]))
  
              # normalize 'votes' into real [0,1] probabilities
              normalizer = proba_k.sum(axis=1)[:, np.newaxis]
              normalizer[normalizer == 0.0] = 1.0
              proba_k /= normalizer
  
              probabilities.append(proba_k)
  
          if not self.outputs_2d_:
              probabilities = probabilities[0]
  
          return probabilities
  