class_docstr: Dynamic ensemble selection-Performance(DES-P).
  
  This method selects all base classifiers that achieve a classification
  performance, in the region of competence, that is higher than the random
  classifier (RC). The performance of the random classifier is defined by
  RC = 1/L, where L is the number of classes in the problem.
  If no base classifier is selected, the whole pool is used for
  classification.
  
  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.
  
  
  mode : String (Default = "selection")
         Whether the technique will perform dynamic selection,
         dynamic weighting or an hybrid approach for classification.
  
  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
  ----------
  Woloszynski, Tomasz, et al. "A measure of competence based on random
  classification for dynamic ensemble selection."
  Information Fusion 13.3 (2012): 207-213.
  
  Woloszynski, Tomasz, and Marek Kurzynski. "A probabilistic model of
  classifier competence for dynamic ensemble selection."
  Pattern Recognition 44.10 (2011): 2656-2668.
  
  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.
code: def __init__(
      self,
      pool_classifiers=None,
      k=7,
      DFP=False,
      with_IH=False,
      safe_k=None,
      IH_rate=0.30,
      mode="selection",
      random_state=None,
      knn_classifier="knn",
      knn_metric="minkowski",
      knne=False,
      DSEL_perc=0.5,
      n_jobs=-1,
      voting="hard",
  ):
      super(DESP, self).__init__(
          pool_classifiers=pool_classifiers,
          k=k,
          DFP=DFP,
          with_IH=with_IH,
          safe_k=safe_k,
          IH_rate=IH_rate,
          mode=mode,
          random_state=random_state,
          knn_classifier=knn_classifier,
          knn_metric=knn_metric,
          knne=knne,
          DSEL_perc=DSEL_perc,
          n_jobs=n_jobs,
          voting=voting,
      )
  
imports: import numpy
  from deslib.des.base import BaseDES
label_desc: Use image node_id 1 to create a new DESP object from inherited base classes: BaseDES with example: obj = DESP(pool_classifiers, k, DFP, with_IH, safe_k, IH_rate, mode, random_state, knn_classifier, knn_metric, knne, DSEL_perc, n_jobs, voting)