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).
code: def __init__(
      self,
      stc_params=None,
      drcif_params=None,
      arsenal_params=None,
      tde_params=None,
      time_limit_in_minutes=0,
      save_component_probas=False,
      verbose=0,
      n_jobs=1,
      random_state=None,
  ):
      self.stc_params = stc_params
      self.drcif_params = drcif_params
      self.arsenal_params = arsenal_params
      self.tde_params = tde_params
  
      self.time_limit_in_minutes = time_limit_in_minutes
  
      self.save_component_probas = save_component_probas
      self.verbose = verbose
      self.n_jobs = n_jobs
      self.random_state = random_state
  
      self.stc_weight_ = 0
      self.drcif_weight_ = 0
      self.arsenal_weight_ = 0
      self.tde_weight_ = 0
      self.component_probas = {}
  
      self._stc_params = stc_params
      self._drcif_params = drcif_params
      self._arsenal_params = arsenal_params
      self._tde_params = tde_params
      self._stc = None
      self._drcif = None
      self._arsenal = None
      self._tde = None
  
      super().__init__()
  
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 1 to create a new HIVECOTEV2 object from inherited base classes: BaseClassifier with example: obj = HIVECOTEV2(stc_params, drcif_params, arsenal_params, tde_params, time_limit_in_minutes, save_component_probas, verbose, n_jobs, random_state)