text: """Class to perform under-sampling by generating centroids based on
  clustering."""
  
  # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
  #          Fernando Nogueira
  #          Christos Aridas
  # License: MIT
  
  import warnings
  
  import numpy as np
  from scipy import sparse
  
  from sklearn.base import clone
  from sklearn.cluster import KMeans
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import _safe_indexing
  
  from ..base import BaseUnderSampler
  from ...utils import Substitution
  from ...utils._docstring import _n_jobs_docstring
  from ...utils._docstring import _random_state_docstring
  from ...utils._validation import _deprecate_positional_args
  
  VOTING_KIND = ("auto", "hard", "soft")
  
  
  @Substitution(
      sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
      n_jobs=_n_jobs_docstring,
      random_state=_random_state_docstring,
  )
  class ClusterCentroids(BaseUnderSampler):
      """Undersample by generating centroids based on clustering methods.
  
      Method that under samples the majority class by replacing a
      cluster of majority samples by the cluster centroid of a KMeans
      algorithm.  This algorithm keeps N majority samples by fitting the
      KMeans algorithm with N cluster to the majority class and using
      the coordinates of the N cluster centroids as the new majority
      samples.
  
      Read more in the :ref:`User Guide <cluster_centroids>`.
  
      Parameters
      ----------
      {sampling_strategy}
  
      {random_state}
  
      estimator : estimator object, default=None
          Pass a :class:`~sklearn.cluster.KMeans` estimator. By default, it will
          be a default :class:`~sklearn.cluster.KMeans` estimator.
  
      voting : {{"hard", "soft", "auto"}}, default='auto'
          Voting strategy to generate the new samples:
  
          - If ``'hard'``, the nearest-neighbors of the centroids found using the
            clustering algorithm will be used.
          - If ``'soft'``, the centroids found by the clustering algorithm will
            be used.
          - If ``'auto'``, if the input is sparse, it will default on ``'hard'``
            otherwise, ``'soft'`` will be used.
  
          .. versionadded:: 0.3.0
  
      {n_jobs}
  
        .. deprecated:: 0.7
           `n_jobs` was deprecated in 0.7 and will be removed in 0.9.
  
      See Also
      --------
      EditedNearestNeighbours : Under-sampling by editing samples.
  
      CondensedNearestNeighbour: Under-sampling by condensing samples.
  
      Notes
      -----
      Supports multi-class resampling by sampling each class independently.
  
      Examples
      --------
  
      >>> from collections import Counter
      >>> from sklearn.datasets import make_classification
      >>> from imblearn.under_sampling import \
  ClusterCentroids # doctest: +NORMALIZE_WHITESPACE
      >>> X, y = make_classification(n_classes=2, class_sep=2,
      ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
      ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
      >>> print('Original dataset shape %s' % Counter(y))
      Original dataset shape Counter({{1: 900, 0: 100}})
      >>> cc = ClusterCentroids(random_state=42)
      >>> X_res, y_res = cc.fit_resample(X, y)
      >>> print('Resampled dataset shape %s' % Counter(y_res))
      ... # doctest: +ELLIPSIS
      Resampled dataset shape Counter({{...}})
      """
  
      @_deprecate_positional_args
      def __init__(
          self,
          *,
          sampling_strategy="auto",
          random_state=None,
          estimator=None,
          voting="auto",
          n_jobs="deprecated",
      ):
          super().__init__(sampling_strategy=sampling_strategy)
          self.random_state = random_state
          self.estimator = estimator
          self.voting = voting
          self.n_jobs = n_jobs
  
      def _validate_estimator(self):
          """Private function to create the KMeans estimator"""
          if self.n_jobs != "deprecated":
              warnings.warn(
                  "'n_jobs' was deprecated in 0.7 and will be removed in 0.9",
                  FutureWarning,
              )
          if self.estimator is None:
              self.estimator_ = KMeans(random_state=self.random_state)
          elif isinstance(self.estimator, KMeans):
              self.estimator_ = clone(self.estimator)
          else:
              raise ValueError(
                  f"`estimator` has to be a KMeans clustering."
                  f" Got {type(self.estimator)} instead."
              )
  
      def _generate_sample(self, X, y, centroids, target_class):
          if self.voting_ == "hard":
              nearest_neighbors = NearestNeighbors(n_neighbors=1)
              nearest_neighbors.fit(X, y)
              indices = nearest_neighbors.kneighbors(centroids, return_distance=False)
              X_new = _safe_indexing(X, np.squeeze(indices))
          else:
              if sparse.issparse(X):
                  X_new = sparse.csr_matrix(centroids, dtype=X.dtype)
              else:
                  X_new = centroids
          y_new = np.array([target_class] * centroids.shape[0], dtype=y.dtype)
  
          return X_new, y_new
  
      def _fit_resample(self, X, y):
          self._validate_estimator()
  
          if self.voting == "auto":
              if sparse.issparse(X):
                  self.voting_ = "hard"
              else:
                  self.voting_ = "soft"
          else:
              if self.voting in VOTING_KIND:
                  self.voting_ = self.voting
              else:
                  raise ValueError(
                      f"'voting' needs to be one of {VOTING_KIND}. "
                      f"Got {self.voting} instead."
                  )
  
          X_resampled, y_resampled = [], []
          for target_class in np.unique(y):
              target_class_indices = np.flatnonzero(y == target_class)
              if target_class in self.sampling_strategy_.keys():
                  n_samples = self.sampling_strategy_[target_class]
                  self.estimator_.set_params(**{"n_clusters": n_samples})
                  self.estimator_.fit(_safe_indexing(X, target_class_indices))
                  X_new, y_new = self._generate_sample(
                      _safe_indexing(X, target_class_indices),
                      _safe_indexing(y, target_class_indices),
                      self.estimator_.cluster_centers_,
                      target_class,
                  )
                  X_resampled.append(X_new)
                  y_resampled.append(y_new)
              else:
                  X_resampled.append(_safe_indexing(X, target_class_indices))
                  y_resampled.append(_safe_indexing(y, target_class_indices))
  
          if sparse.issparse(X):
              X_resampled = sparse.vstack(X_resampled)
          else:
              X_resampled = np.vstack(X_resampled)
          y_resampled = np.hstack(y_resampled)
  
          return X_resampled, np.array(y_resampled, dtype=y.dtype)
  
      def _more_tags(self):
          return {"sample_indices": False}
  