class_docstr: Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1.
  
  An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature
  representations using the CAWPE structure as described in [1]_. The default
  implementation differs from the one described in [1]_, in that the STC component
  uses the out of bag error (OOB) estimates for weights (described in [2]_) rather
  than the cross validation estimate. OOB is an order of magnitude faster and on
  average as good as CV. This means that this version of HIVE COTE is a bit faster
  than HC2, although less accurate on average.
  
  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.
  tsf_params : dict or None, default=None
      Parameters for the TimeSeriesForestClassifier module. If None, uses the default
      parameters with n_estimators set to 500.
  rise_params : dict or None, default=None
      Parameters for the RandomIntervalSpectralForest module. If None, uses the
      default parameters with n_estimators set to 500.
  cboss_params : dict or None, default=None
      Parameters for the ContractableBOSS module. If None, uses the default
      parameters.
  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.
  tsf_weight_ : float
      The weight for TSF probabilities.
  rise_weight_ : float
      The weight for RISE probabilities.
  cboss_weight_ : float
      The weight for cBOSS probabilities.
  
  See Also
  --------
  HIVECOTEV2, ShapeletTransformClassifier, TimeSeriesForestClassifier,
  RandomIntervalSpectralForest, ContractableBOSS
  
  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] Anthony Bagnall, Michael Flynn, James Large, Jason Lines and
     Matthew Middlehurst. "On the usage and performance of the Hierarchical Vote
     Collective of Transformation-based Ensembles version 1.0 (hive-cote v1.0)"
     International Workshop on Advanced Analytics and Learning on Temporal Data 2020
  .. [2] 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: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      For classifiers, a "default" set of parameters should be provided for
      general testing, and a "results_comparison" set for comparing against
      previously recorded results if the general set does not produce suitable
      probabilities to compare against.
  
  Returns
  -------
  params : dict or list of dict, default={}
      Parameters to create testing instances of the class.
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`.
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          For classifiers, a "default" set of parameters should be provided for
          general testing, and a "results_comparison" set for comparing against
          previously recorded results if the general set does not produce suitable
          probabilities to compare against.
  
      Returns
      -------
      params : dict or list of dict, default={}
          Parameters to create testing instances of the class.
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`.
      """
      from sklearn.ensemble import RandomForestClassifier
  
      if parameter_set == "results_comparison":
          return {
              "stc_params": {
                  "estimator": RandomForestClassifier(n_estimators=3),
                  "n_shapelet_samples": 50,
                  "max_shapelets": 5,
                  "batch_size": 10,
              },
              "tsf_params": {"n_estimators": 3},
              "rise_params": {"n_estimators": 3},
              "cboss_params": {
                  "n_parameter_samples": 5,
                  "max_ensemble_size": 3,
              },
          }
      else:
          return {
              "stc_params": {
                  "estimator": RandomForestClassifier(n_estimators=1),
                  "n_shapelet_samples": 5,
                  "max_shapelets": 5,
                  "batch_size": 5,
              },
              "tsf_params": {"n_estimators": 1},
              "rise_params": {"n_estimators": 1},
              "cboss_params": {
                  "n_parameter_samples": 1,
                  "max_ensemble_size": 1,
              },
          }
  
file_path: sktime/sktime/classification/hybrid/_hivecote_v1.py
imports: from datetime import datetime
  import numpy
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import cross_val_predict
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.classification.dictionary_based import ContractableBOSS
  from sktime.classification.interval_based import RandomIntervalSpectralEnsemble, TimeSeriesForestClassifier
  from sktime.classification.shapelet_based import ShapeletTransformClassifier
label_desc: Use image node_id 5 for calling the HIVECOTEV1 obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict