class_docstr: Canonical Time-series Characteristics (catch22) classifier.
  
  This classifier simply transforms the input data using the Catch22 [1]
  transformer and builds a provided estimator using the transformed data.
  
  Shorthand for the pipeline `Catch22(outlier_norm, replace_nans) * estimator`
  
  Parameters
  ----------
  outlier_norm : bool, optional, default=False
      Normalise each series during the two outlier Catch22 features, which can take a
      while to process for large values.
  replace_nans : bool, optional, default=True
      Replace NaN or inf values from the Catch22 transform with 0.
  estimator : sklearn classifier, optional, default=None
      An sklearn estimator to be built using the transformed data.
      Defaults to sklearn RandomForestClassifier(n_estimators=200)
  n_jobs : int, optional, 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, optional, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes_)
      Holds the label for each class.
  estimator_ : ClassifierPipeline
      Catch22Classifier as a ClassifierPipeline, fitted to data internally
  
  See Also
  --------
  Catch22
  
  Notes
  -----
  Authors `catch22ForestClassifier <https://github.com/chlubba/sktime-catch22>`_.
  
  For the Java version, see `tsml <https://github.com/uea-machine-learning/tsml/blob
  /master/src/main/java/tsml/classifiers/hybrids/Catch22Classifier.java>`_.
  
  References
  ----------
  .. [1] Lubba, Carl H., et al. "catch22: Canonical time-series characteristics."
      Data Mining and Knowledge Discovery 33.6 (2019): 1821-1852.
      https://link.springer.com/article/10.1007/s10618-019-00647-x
  
  Examples
  --------
  >>> from sktime.classification.feature_based import Catch22Classifier
  >>> from sklearn.ensemble import RandomForestClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
  >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
  >>> clf = Catch22Classifier(
  ...     estimator=RandomForestClassifier(n_estimators=5),
  ...     outlier_norm=True,
  ... ) # doctest: +SKIP
  >>> clf.fit(X_train, y_train) # doctest: +SKIP
  Catch22Classifier(...)
  >>> y_pred = clf.predict(X_test) # 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.
      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 {
              "estimator": RandomForestClassifier(n_estimators=10),
              "outlier_norm": True,
          }
  
      from sklearn.dummy import DummyClassifier
  
      param1 = {"estimator": RandomForestClassifier(n_estimators=2)}
      param2 = {
          "estimator": DummyClassifier(),
          "outlier_norm": True,
          "replace_nans": False,
          "random_state": 42,
      }
  
      return [param1, param2]
  
file_path: sktime/sktime/classification/feature_based/_catch22_classifier.py
imports: from sklearn.ensemble import RandomForestClassifier
  from sktime.base._base import _clone_estimator
  from sktime.classification._delegate import _DelegatedClassifier
  from sktime.pipeline import make_pipeline
  from sktime.transformations.panel.catch22 import Catch22
label_desc: Use image node_id 2 for calling the Catch22Classifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list, dict