class_docstr: ClaSP (Classification Score Profile) Transformer.
  
  Implementation of the Classification Score Profile of a time series.
  ClaSP hierarchically splits a TS into two parts, where each split point is
  determined by training a binary TS classifier for each possible split point and
  selecting the one with highest accuracy, i.e., the one that is best at identifying
  subsequences to be from either of the partitions.
  
  Parameters
  ----------
  window_length :       int, default = 10
      size of window for sliding.
  scoring_metric :      string, default = ROC_AUC
      the scoring metric to use in ClaSP - choose from ROC_AUC or F1
  exclusion_radius : int
      Exclusion Radius for change points to be non-trivial matches
  
  Notes
  -----
  As described in
  @inproceedings{clasp2021,
    title={ClaSP - Time Series Segmentation},
    author={Sch"afer, Patrick and Ermshaus, Arik and Leser, Ulf},
    booktitle={CIKM},
    year={2021}
  }
  
  Examples
  --------
  >>> from sktime.transformations.series.clasp import ClaSPTransformer
  >>> from sktime.annotation.clasp import find_dominant_window_sizes
  >>> from sktime.datasets import load_electric_devices_segmentation
  >>> X, true_period_size, true_cps = load_electric_devices_segmentation()
  >>> dominant_period_size = find_dominant_window_sizes(X) # doctest: +SKIP
  >>> clasp = ClaSPTransformer(window_length=dominant_period_size) # doctest: +SKIP
  >>> clasp.fit(X) # doctest: +SKIP
  >>> profile = clasp.transform(X) # doctest: +SKIP
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.
  
  
  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.
  
  
      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`
      """
      return {"window_length": 5}
  
imports: from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 4 for calling the ClaSPTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict