class_docstr: Class to perform under-sampling based on one-sided selection 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
      Validated K-nearest neighbors estimator created from parameter `n_neighbors`.
  
      .. 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 noisy samples.
  
  Notes
  -----
  The method is based on [1]_.
  
  Supports multi-class resampling. A one-vs.-one scheme is used when sampling
  a class as proposed in [1]_. For each class to be sampled, all samples of
  this class and the minority class are used during the sampling procedure.
  
  References
  ----------
  .. [1] M. Kubat, S. Matwin, "Addressing the curse of imbalanced training
     sets: one-sided selection," In ICML, vol. 97, pp. 179-186, 1997.
  
  Examples
  --------
  
  >>> from collections import Counter
  >>> from sklearn.datasets import make_classification
  >>> from imblearn.under_sampling import OneSidedSelection
  >>> 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}})
  >>> oss = OneSidedSelection(random_state=42)
  >>> X_res, y_res = oss.fit_resample(X, y)
  >>> print('Resampled dataset shape %s' % Counter(y_res))
  Resampled dataset shape Counter({{1: 496, 0: 100}})
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docstr: Last fitted k-NN estimator.
docstr_tok: ["Last","fitted","k-NN","estimator","."]
code: def estimator_(self):
      """Last fitted k-NN estimator."""
      warnings.warn(
          "`estimator_` attribute has been deprecated in 0.12 and will be "
          "removed in 0.14. Use `estimators_` instead.",
          FutureWarning,
      )
      return self.estimators_[-1]
  
file_path: imbalanced-learn/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py
imports: import numbers
  import warnings
  from collections import Counter
  import numpy
  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
  from ._tomek_links import TomekLinks
label_desc: Use image node_id 4 for calling the OneSidedSelection obj's underlying member method code with example usage: obj.estimator_() and returns: self