class_docstr: Undersample based on the condensed nearest neighbour method.
  
  Read more in the :ref:`User Guide <condensed_nearest_neighbors>`.
  
  Parameters
  ----------
  {sampling_strategy}
  
  {random_state}
  
  n_neighbors : int or estimator object, default=None
      If ``int``, size of the neighbourhood to consider to compute the
      nearest neighbors. If object, an estimator that inherits from
      :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
      find the nearest-neighbors.  If `None`, a
      :class:`~sklearn.neighbors.KNeighborsClassifier` with a 1-NN rules will
      be used.
  
  n_seeds_S : int, default=1
      Number of samples to extract in order to build the set S.
  
  {n_jobs}
  
  Attributes
  ----------
  sampling_strategy_ : dict
      Dictionary containing the information to sample the dataset. The keys
      corresponds to the class labels from which to sample and the values
      are the number of samples to sample.
  
  estimator_ : estimator object
      The validated K-nearest neighbor estimator created from `n_neighbors` parameter.
  
      .. deprecated:: 0.12
         `estimator_` is deprecated in 0.12 and will be removed in 0.14. Use
         `estimators_` instead that contains the list of all K-nearest
         neighbors estimator used for each pair of class.
  
  estimators_ : list of estimator objects of shape (n_resampled_classes - 1,)
      Contains the K-nearest neighbor estimator used for per of classes.
  
      .. versionadded:: 0.12
  
  sample_indices_ : ndarray of shape (n_new_samples,)
      Indices of the samples selected.
  
      .. versionadded:: 0.4
  
  n_features_in_ : int
      Number of features in the input dataset.
  
      .. versionadded:: 0.9
  
  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.
  
      .. versionadded:: 0.10
  
  See Also
  --------
  EditedNearestNeighbours : Undersample by editing samples.
  
  RepeatedEditedNearestNeighbours : Undersample by repeating ENN algorithm.
  
  AllKNN : Undersample using ENN and various number of neighbours.
  
  Notes
  -----
  The method is based on [1]_.
  
  Supports multi-class resampling: a strategy one (minority) vs. each other
  classes is applied.
  
  References
  ----------
  .. [1] P. Hart, "The condensed nearest neighbor rule,"
     In Information Theory, IEEE Transactions on, vol. 14(3),
     pp. 515-516, 1968.
  
  Examples
  --------
  >>> from collections import Counter  # doctest: +SKIP
  >>> from sklearn.datasets import fetch_openml  # doctest: +SKIP
  >>> from sklearn.preprocessing import scale  # doctest: +SKIP
  >>> from imblearn.under_sampling import CondensedNearestNeighbour  # doctest: +SKIP
  >>> X, y = fetch_openml('diabetes', version=1, return_X_y=True)  # doctest: +SKIP
  >>> X = scale(X)  # doctest: +SKIP
  >>> print('Original dataset shape %s' % Counter(y))  # doctest: +SKIP
  Original dataset shape Counter({{'tested_negative': 500,         'tested_positive': 268}})  # doctest: +SKIP
  >>> cnn = CondensedNearestNeighbour(random_state=42)  # doctest: +SKIP
  >>> X_res, y_res = cnn.fit_resample(X, y)  #doctest: +SKIP
  >>> print('Resampled dataset shape %s' % Counter(y_res))  # doctest: +SKIP
  Resampled dataset shape Counter({{'tested_positive': 268,         'tested_negative': 181}})  # doctest: +SKIP
class_docstr_tok: ["Undersample","based","on","the","condensed","nearest","neighbour","method",".","Read","more","in","the",":","ref",":","`","User","Guide","<","condensed_nearest_neighbors",">","`",".","Parameters","--","--","--","--","--","{","sampling_strategy","}","{","random_state","}","n_neighbors",":","int","or","estimator","object",",","default=None","If","``","int","``",",","size","of","the","neighbourhood","to","consider","to","compute","the","nearest","neighbors",".","If","object",",","an","estimator","that","inherits","from",":","class",":","`","~sklearn.neighbors.base.KNeighborsMixin","`","that","will","be","used","to","find","the","nearest-neighbors",".","If","`","None","`",",","a",":","class",":","`","~sklearn.neighbors.KNeighborsClassifier","`","with","a","1-NN","rules","will","be","used",".","n_seeds_S",":","int",",","default=1","Number","of","samples","to","extract","in","order","to","build","the","set","S.","{","n_jobs","}","Attributes","--","--","--","--","--","sampling_strategy_",":","dict","Dictionary","containing","the","information","to","sample","the","dataset",".","The","keys","corresponds","to","the","class","labels","from","which","to","sample","and","the","values","are","the","number","of","samples","to","sample",".","estimator_",":","estimator","object","The","validated","K-nearest","neighbor","estimator","created","from","`","n_neighbors","`","parameter",".","..","deprecated",":",":","0.12","`","estimator_","`","is","deprecated","in","0.12","and","will","be","removed","in","0.14",".","Use","`","estimators_","`","instead","that","contains","the","list","of","all","K-nearest","neighbors","estimator","used","for","each","pair","of","class",".","estimators_",":","list","of","estimator","objects","of","shape","(","n_resampled_classes","-","1",",",")","Contains","the","K-nearest","neighbor","estimator","used","for","per","of","classes",".","..","versionadded",":",":","0.12","sample_indices_",":","ndarray","of","shape","(","n_new_samples",",",")","Indices","of","the","samples","selected",".","..","versionadded",":",":","0.4","n_features_in_",":","int","Number","of","features","in","the","input","dataset",".","..","versionadded",":",":","0.9","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",".","..","versionadded",":",":","0.10","See","Also","--","--","--","--","EditedNearestNeighbours",":","Undersample","by","editing","samples",".","RepeatedEditedNearestNeighbours",":","Undersample","by","repeating","ENN","algorithm",".","AllKNN",":","Undersample","using","ENN","and","various","number","of","neighbours",".","Notes","--","--","-","The","method","is","based","on","[","1","]","_",".","Supports","multi-class","resampling",":","a","strategy","one","(","minority",")","vs.","each","other","classes","is","applied",".","References","--","--","--","--","--","..","[","1","]","P.","Hart",",","``","The","condensed","nearest","neighbor","rule",",","''","In","Information","Theory",",","IEEE","Transactions","on",",","vol",".","14","(","3",")",",","pp",".","515-516",",","1968",".","Examples","--","--","--","--",">",">",">","from","collections","import","Counter","#","doctest",":","+SKIP",">",">",">","from","sklearn.datasets","import","fetch_openml","#","doctest",":","+SKIP",">",">",">","from","sklearn.preprocessing","import","scale","#","doctest",":","+SKIP",">",">",">","from","imblearn.under_sampling","import","CondensedNearestNeighbour","#","doctest",":","+SKIP",">",">",">","X",",","y","=","fetch_openml","(","'diabetes","'",",","version=1",",","return_X_y=True",")","#","doctest",":","+SKIP",">",">",">","X","=","scale","(","X",")","#","doctest",":","+SKIP",">",">",">","print","(","'Original","dataset","shape","%","s","'","%","Counter","(","y",")",")","#","doctest",":","+SKIP","Original","dataset","shape","Counter","(","{","{","'tested_negative","'",":","500",",","'tested_positive","'",":","268","}","}",")","#","doctest",":","+SKIP",">",">",">","cnn","=","CondensedNearestNeighbour","(","random_state=42",")","#","doctest",":","+SKIP",">",">",">","X_res",",","y_res","=","cnn.fit_resample","(","X",",","y",")","#","doctest",":","+SKIP",">",">",">","print","(","'Resampled","dataset","shape","%","s","'","%","Counter","(","y_res",")",")","#","doctest",":","+SKIP","Resampled","dataset","shape","Counter","(","{","{","'tested_positive","'",":","268",",","'tested_negative","'",":","181","}","}",")","#","doctest",":","+SKIP"]
code: def _fit_resample(self, X, y):
      estimator = self._validate_estimator()
  
      random_state = check_random_state(self.random_state)
      target_stats = Counter(y)
      class_minority = min(target_stats, key=target_stats.get)
      idx_under = np.empty((0,), dtype=int)
  
      self.estimators_ = []
      for target_class in np.unique(y):
          if target_class in self.sampling_strategy_.keys():
              # Randomly get one sample from the majority class
              # Generate the index to select
              idx_maj = np.flatnonzero(y == target_class)
              idx_maj_sample = idx_maj[
                  random_state.randint(
                      low=0,
                      high=target_stats[target_class],
                      size=self.n_seeds_S,
                  )
              ]
  
              # Create the set C - One majority samples and all minority
              C_indices = np.append(
                  np.flatnonzero(y == class_minority), idx_maj_sample
              )
              C_x = _safe_indexing(X, C_indices)
              C_y = _safe_indexing(y, C_indices)
  
              # Create the set S - all majority samples
              S_indices = np.flatnonzero(y == target_class)
              S_x = _safe_indexing(X, S_indices)
              S_y = _safe_indexing(y, S_indices)
  
              # fit knn on C
              self.estimators_.append(clone(estimator).fit(C_x, C_y))
  
              good_classif_label = idx_maj_sample.copy()
              # Check each sample in S if we keep it or drop it
              for idx_sam, (x_sam, y_sam) in enumerate(zip(S_x, S_y)):
                  # Do not select sample which are already well classified
                  if idx_sam in good_classif_label:
                      continue
  
                  # Classify on S
                  if not issparse(x_sam):
                      x_sam = x_sam.reshape(1, -1)
                  pred_y = self.estimators_[-1].predict(x_sam)
  
                  # If the prediction do not agree with the true label
                  # append it in C_x
                  if y_sam != pred_y:
                      # Keep the index for later
                      idx_maj_sample = np.append(
                          idx_maj_sample, idx_maj[idx_sam]
                      )
  
                      # Update C
                      C_indices = np.append(C_indices, idx_maj[idx_sam])
                      C_x = _safe_indexing(X, C_indices)
                      C_y = _safe_indexing(y, C_indices)
  
                      # fit a knn on C
                      self.estimators_[-1].fit(C_x, C_y)
  
                      # This experimental to speed up the search
                      # Classify all the element in S and avoid to test the
                      # well classified elements
                      pred_S_y = self.estimators_[-1].predict(S_x)
                      good_classif_label = np.unique(
                          np.append(
                              idx_maj_sample,
                              np.flatnonzero(pred_S_y == S_y),
                          )
                      )
  
              idx_under = np.concatenate(
                  (idx_under, idx_maj_sample), axis=0
              )
          else:
              idx_under = np.concatenate(
                  (idx_under, np.flatnonzero(y == target_class)), axis=0
              )
  
      self.sample_indices_ = idx_under
  
      return _safe_indexing(X, idx_under), _safe_indexing(y, idx_under)
  
file_path: imbalanced-learn/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
imports: import numbers
  import warnings
  from collections import Counter
  import numpy
  from scipy.sparse import issparse
  from sklearn.base import clone
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.utils import _safe_indexing, check_random_state
  from ...utils import Substitution
  from ...utils._docstring import _n_jobs_docstring, _random_state_docstring
  from ...utils._param_validation import HasMethods, Interval
  from ..base import BaseCleaningSampler
label_desc: Use image node_id 3 for calling the CondensedNearestNeighbour obj's underlying member method code with example usage: obj._fit_resample(X, y) and returns: _safe_indexing,  _safe_indexing