class_docstr: Random Interval Spectral Ensemble (RISE).
  
  Input: n series length m
  For each tree
      - sample a random intervals
      - take the ACF and PS over this interval, and concatenate features
      - build tree on new features
  Ensemble the trees through averaging probabilities.
  
  Parameters
  ----------
  n_estimators : int, default=200
      The number of trees in the forest.
  min_interval : int, default=16
      The minimum width of an interval.
  acf_lag : int, default=100
      The maximum number of autocorrelation terms to use.
  acf_min_values : int, default=4
      Never use fewer than this number of terms to find a correlation.
  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, RandomState instance or None, 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`.
  
  Attributes
  ----------
  n_classes_ : int
      The number of classes.
  classes_ : list
      The classes labels.
  intervals_ : array of shape = [n_estimators][2]
      Stores indexes of start and end points for all classifiers.
  
  Notes
  -----
  For the Java version, see
  `TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/
  classifiers/interval_based/RISE.java>`_.
  
  References
  ----------
  .. [1] Jason Lines, Sarah Taylor and Anthony Bagnall, "Time Series Classification
     with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based
     Ensembles", ACM Transactions on Knowledge and Data Engineering, 12(5): 2018
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`.
      """
      if parameter_set == "results_comparison":
          return {"n_estimators": 10}
      else:
          return {
              "n_estimators": 2,
              "acf_lag": 10,
              "min_interval": 5,
          }
  
imports: import numpy
  from joblib import Parallel, delayed
  from sklearn.base import clone
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 6 for calling the RandomIntervalSpectralEnsemble obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict