content: """Class to perform under-sampling by generating centroids based on
  clustering."""
  
  # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
  #          Fernando Nogueira
  #          Christos Aridas
  # License: MIT
  
  from __future__ import division, print_function
  
  import numpy as np
  from scipy import sparse
  
  from sklearn.cluster import KMeans
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import safe_indexing
  
  from ..base import BaseUnderSampler
  
  VOTING_KIND = ('auto', 'hard', 'soft')
  
  
  class ClusterCentroids(BaseUnderSampler):
      """Perform under-sampling 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
      ----------
      ratio : str, dict, or callable, optional (default='auto')
          Ratio to use for resampling the data set.
  
          - If ``str``, has to be one of: (i) ``'minority'``: resample the
            minority class; (ii) ``'majority'``: resample the majority class,
            (iii) ``'not minority'``: resample all classes apart of the minority
            class, (iv) ``'all'``: resample all classes, and (v) ``'auto'``:
            correspond to ``'all'`` with for over-sampling methods and ``'not
            minority'`` for under-sampling methods. The classes targeted will be
            over-sampled or under-sampled to achieve an equal number of sample
            with the majority or minority class.
          - If ``dict``, the keys correspond to the targeted classes. The values
            correspond to the desired number of samples.
          - If callable, function taking ``y`` and returns a ``dict``. The keys
            correspond to the targeted classes. The values correspond to the
            desired number of samples.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, ``random_state`` is the seed used by the random number
          generator; If ``RandomState`` instance, random_state is the random
          number generator; If ``None``, the random number generator is the
          ``RandomState`` instance used by ``np.random``.
  
      estimator : object, optional(default=KMeans())
          Pass a :class:`sklearn.cluster.KMeans` estimator.
  
      voting : str, optional (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 : int, optional (default=1)
          The number of threads to open if possible.
  
      Notes
      -----
      Supports mutli-class resampling by sampling each class independently.
  
      See :ref:`sphx_glr_auto_examples_under-sampling_plot_cluster_centroids.py`.
  
      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 {}'.format(Counter(y)))
      Original dataset shape Counter({1: 900, 0: 100})
      >>> cc = ClusterCentroids(random_state=42)
      >>> X_res, y_res = cc.fit_sample(X, y)
      >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
      ... # doctest: +ELLIPSIS
      Resampled dataset shape Counter({...})
  
      """
  
      def __init__(self,
                   ratio='auto',
                   random_state=None,
                   estimator=None,
                   voting='auto',
                   n_jobs=1):
          super(ClusterCentroids, self).__init__(
              ratio=ratio)
          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.estimator is None:
              self.estimator_ = KMeans(
                  random_state=self.random_state, n_jobs=self.n_jobs)
          elif isinstance(self.estimator, KMeans):
              self.estimator_ = self.estimator
          else:
              raise ValueError('`estimator` has to be a KMeans clustering.'
                               ' Got {} instead.'.format(type(self.estimator)))
  
      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)
              else:
                  X_new = centroids
          y_new = np.array([target_class] * centroids.shape[0])
  
          return X_new, y_new
  
      def _sample(self, X, y):
          """Resample the dataset.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Matrix containing the data which have to be sampled.
  
          y : array-like, shape (n_samples,)
              Corresponding label for each sample in X.
  
          Returns
          -------
          X_resampled : {ndarray, sparse matrix}, shape \
  (n_samples_new, n_features)
              The array containing the resampled data.
  
          y_resampled : ndarray, shape (n_samples_new,)
              The corresponding label of `X_resampled`
  
          """
          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("'voting' needs to be one of {}. Got {}"
                                   " instead.".format(VOTING_KIND, self.voting))
  
          X_resampled, y_resampled = [], []
          for target_class in np.unique(y):
              if target_class in self.ratio_.keys():
                  n_samples = self.ratio_[target_class]
                  self.estimator_.set_params(**{'n_clusters': n_samples})
                  self.estimator_.fit(X[y == target_class])
                  X_new, y_new = self._generate_sample(
                      X, y, self.estimator_.cluster_centers_, target_class)
                  X_resampled.append(X_new)
                  y_resampled.append(y_new)
              else:
                  target_class_indices = np.flatnonzero(y == target_class)
                  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)
  