class_docstr: Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V2.
  
  An ensemble of the STC, DrCIF, Arsenal and TDE classifiers from different feature
  representations using the CAWPE structure as described in [1].
  
  Parameters
  ----------
  stc_params : dict or None, default=None
      Parameters for the ShapeletTransformClassifier module. If None, uses the
      default parameters with a 2 hour transform contract.
  drcif_params : dict or None, default=None
      Parameters for the DrCIF module. If None, uses the default parameters with
      n_estimators set to 500.
  arsenal_params : dict or None, default=None
      Parameters for the Arsenal module. If None, uses the default parameters.
  tde_params : dict or None, default=None
      Parameters for the TemporalDictionaryEnsemble module. If None, uses the default
      parameters.
  time_limit_in_minutes : int, default=0
      Time contract to limit build time in minutes, overriding
      n_estimators/n_parameter_samples for each component.
      Default of 0 means n_estimators/n_parameter_samples for each component is used.
  save_component_probas : bool, default=False
      When predict/predict_proba is called, save each HIVE-COTEV2 component
      probability predictions in component_probas.
  verbose : int, default=0
      Level of output printed to the console (for information only).
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random number generation.
  
  Attributes
  ----------
  n_classes_ : int
      The number of classes.
  classes_ : list
      The unique class labels.
  stc_weight_ : float
      The weight for STC probabilities.
  drcif_weight_ : float
      The weight for DrCIF probabilities.
  arsenal_weight_ : float
      The weight for Arsenal probabilities.
  tde_weight_ : float
      The weight for TDE probabilities.
  component_probas : dict
      Only used if save_component_probas is true. Saved probability predictions for
      each HIVE-COTEV2 component.
  
  See Also
  --------
  HIVECOTEV1, ShapeletTransformClassifier, DrCIF, Arsenal, TemporalDictionaryEnsemble
  
  Notes
  -----
  For the Java version, see
  `https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
  tsml/classifiers/hybrids/HIVE_COTE.java`_.
  
  References
  ----------
  .. [1] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom,
     and Anthony Bagnall. "HIVE-COTE 2.0: a new meta ensemble for time series
     classification." Machine Learning (2021).
docstr: Predicts labels probabilities for sequences in X.
  
  Parameters
  ----------
  X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
      The data to make predict probabilities for.
  
  Returns
  -------
  y : array-like, shape = [n_instances, n_classes_]
      Predicted probabilities using the ordering in classes_.
docstr_tok: ["Predicts","labels","probabilities","for","sequences","in","X.","Parameters","--","--","--","--","--","X",":","3D","np.array","of","shape","=","[","n_instances",",","n_dimensions",",","series_length","]","The","data","to","make","predict","probabilities","for",".","Returns","--","--","--","-","y",":","array-like",",","shape","=","[","n_instances",",","n_classes_","]","Predicted","probabilities","using","the","ordering","in","classes_","."]
code: def _predict_proba(
      self, X, return_component_probas=False
  ) -> np.ndarray:
      """Predicts labels probabilities for sequences in X.
  
      Parameters
      ----------
      X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
          The data to make predict probabilities for.
  
      Returns
      -------
      y : array-like, shape = [n_instances, n_classes_]
          Predicted probabilities using the ordering in classes_.
      """
      dists = np.zeros((X.shape[0], self.n_classes_))
  
      # Call predict proba on each classifier, multiply the probabilities by the
      # classifiers weight then add them to the current HC2 probabilities
      stc_probas = self._stc.predict_proba(X)
      dists = np.add(
          dists,
          stc_probas * (np.ones(self.n_classes_) * self.stc_weight_),
      )
      drcif_probas = self._drcif.predict_proba(X)
      dists = np.add(
          dists,
          drcif_probas
          * (np.ones(self.n_classes_) * self.drcif_weight_),
      )
      arsenal_probas = self._arsenal.predict_proba(X)
      dists = np.add(
          dists,
          arsenal_probas
          * (np.ones(self.n_classes_) * self.arsenal_weight_),
      )
      tde_probas = self._tde.predict_proba(X)
      dists = np.add(
          dists,
          tde_probas * (np.ones(self.n_classes_) * self.tde_weight_),
      )
  
      if self.save_component_probas:
          self.component_probas = {
              "STC": stc_probas,
              "DrCIF": drcif_probas,
              "Arsenal": arsenal_probas,
              "TDE": tde_probas,
          }
  
      # Make each instances probability array sum to 1 and return
      return dists / dists.sum(axis=1, keepdims=True)
  
file_path: sktime/sktime/classification/hybrid/_hivecote_v2.py
imports: from datetime import datetime
  import numpy
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.classification.dictionary_based import TemporalDictionaryEnsemble
  from sktime.classification.interval_based._drcif import DrCIF
  from sktime.classification.kernel_based import Arsenal
  from sktime.classification.shapelet_based import ShapeletTransformClassifier
label_desc: Use image node_id 4 for calling the HIVECOTEV2 obj's underlying member method code with example usage: obj._predict_proba(X, return_component_probas) and returns: unknown