class_docstr: Dynamic ensemble Selection for multi-class imbalanced datasets (DES-MI).
  
  Parameters
  ----------
   pool_classifiers : list of classifiers (Default = None)
      The generated_pool of classifiers trained for the corresponding
      classification problem. Each base classifiers should support the method
      "predict". If None, then the pool of classifiers is a bagging
      classifier.
  
  k : int (Default = 7)
      Number of neighbors used to estimate the competence of the base
      classifiers.
  
  DFP : Boolean (Default = False)
      Determines if the dynamic frienemy pruning is applied.
  
  with_IH : Boolean (Default = False)
      Whether the hardness level of the region of competence is used to
      decide between using the DS algorithm or the KNN for classification of
      a given query sample.
  
  safe_k : int (default = None)
      The size of the indecision region.
  
  IH_rate : float (default = 0.3)
      Hardness threshold. If the hardness level of the competence region is
      lower than the IH_rate the KNN classifier is used. Otherwise, the DS
      algorithm is used for classification.
  
  alpha : float (Default = 0.9)
          Scaling coefficient to regulate the weight value
  
  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`.
  
  knn_classifier : {'knn', 'faiss', None} (Default = 'knn')
       The algorithm used to estimate the region of competence:
  
       - 'knn' will use :class:`KNeighborsClassifier` from sklearn
        :class:`KNNE` available on `deslib.utils.knne`
  
       - 'faiss' will use Facebook's Faiss similarity search through the
         class :class:`FaissKNNClassifier`
  
       - None, will use sklearn :class:`KNeighborsClassifier`.
  
  knn_metric : {'minkowski', 'cosine', 'mahalanobis'} (Default = 'minkowski')
      The metric used by the k-NN classifier to estimate distances.
  
      - 'minkowski' will use minkowski distance.
  
      - 'cosine' will use the cosine distance.
  
      - 'mahalanobis' will use the mahalonibis distance.
  
  knne : bool (Default=False)
      Whether to use K-Nearest Neighbor Equality (KNNE) for the region
      of competence estimation.
  
  DSEL_perc : float (Default = 0.5)
      Percentage of the input data used to fit DSEL.
      Note: This parameter is only used if the pool of classifier is None or
      unfitted.
  
  voting : {'hard', 'soft'}, default='hard'
          If 'hard', uses predicted class labels for majority rule voting.
          Else if 'soft', predicts the class label based on the argmax of
          the sums of the predicted probabilities, which is recommended for
          an ensemble of well-calibrated classifiers.
  
  n_jobs : int, default=-1
      The number of parallel jobs to run. None means 1 unless in
      a joblib.parallel_backend context. -1 means using all processors.
      Doesn’t affect fit method.
  
  References
  ----------
  García, S.; Zhang, Z.-L.; Altalhi, A.; Alshomrani, S. & Herrera, F.
  "Dynamic ensemble selection for multi-class
  imbalanced datasets." Information Sciences, 2018, 445-446, 22 - 37
  
  Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. "Dynamic selection
  of classifiers—a comprehensive review."
  Pattern Recognition 47.11 (2014): 3665-3680
  
  R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier
  selection: Recent advances and perspectives,”
  Information Fusion, vol. 41, pp. 195 – 216, 2018.
docstr: Predicts the posterior probabilities of the corresponding
  query sample.
  
  Parameters
  ----------
  predictions : array of shape (n_samples, n_classifiers)
      Predictions of the base classifiers for all test examples.
  
  probabilities : array of shape (n_samples, n_classifiers, n_classes)
      Probabilities estimates of each base classifier for all test
      examples.
  
  competence_region : array of shape (n_samples, n_neighbors)
      Indices of the k nearest neighbors according for each test sample.
  
  distances : array of shape (n_samples, n_neighbors)
      Distances from the k nearest neighbors to each test
      sample.
  
  DFP_mask : array of shape (n_samples, n_classifiers)
      Mask containing 1 for the selected base classifier and 0 otherwise.
  
  Returns
  -------
  predicted_proba : array = [n_samples, n_classes]
                    Probability estimates for all test examples.
code: def predict_proba_with_ds(
      self,
      predictions,
      probabilities,
      competence_region=None,
      distances=None,
      DFP_mask=None,
  ):
      """Predicts the posterior probabilities of the corresponding
      query sample.
  
      Parameters
      ----------
      predictions : array of shape (n_samples, n_classifiers)
          Predictions of the base classifiers for all test examples.
  
      probabilities : array of shape (n_samples, n_classifiers, n_classes)
          Probabilities estimates of each base classifier for all test
          examples.
  
      competence_region : array of shape (n_samples, n_neighbors)
          Indices of the k nearest neighbors according for each test sample.
  
      distances : array of shape (n_samples, n_neighbors)
          Distances from the k nearest neighbors to each test
          sample.
  
      DFP_mask : array of shape (n_samples, n_classifiers)
          Mask containing 1 for the selected base classifier and 0 otherwise.
  
      Returns
      -------
      predicted_proba : array = [n_samples, n_classes]
                        Probability estimates for all test examples.
      """
      accuracy = self.estimate_competence(
          competence_region=competence_region, distances=distances
      )
  
      if self.DFP:
          accuracy = accuracy * DFP_mask
  
      selected_classifiers = self.select(accuracy)
      if self.voting == "hard":
          votes = predictions[
              np.arange(predictions.shape[0])[:, None],
              selected_classifiers,
          ]
          votes = sum_votes_per_class(votes, self.n_classes_)
          predicted_proba = votes / votes.sum(axis=1)[:, None]
  
      else:
          ensemble_proba = probabilities[
              np.arange(probabilities.shape[0])[:, None],
              selected_classifiers,
              :,
          ]
  
          predicted_proba = np.mean(ensemble_proba, axis=1)
  
      return predicted_proba
  
file_path: DESlib/deslib/des/des_mi.py
imports: import numpy
  from sklearn.preprocessing import normalize
  from deslib.base import BaseDS
  from deslib.util.aggregation import sum_votes_per_class
label_desc: Use image node_id 5 for calling the DESMI obj's underlying member method code with example usage: obj.predict_proba_with_ds(predictions, probabilities, competence_region, distances, DFP_mask) and returns: predicted_proba