class_docstr: Transformer for extracting time series features via `tsfresh.extract_features`.
  
  Direct interface to `tsfresh.extract_features` [1] followed by the tsfresh
  FeatureSelector class as an `sktime` transformer.
  
  Parameters
  ----------
  default_fc_parameters : str, FCParameters object or None,
      default=None = tsfresh default = "comprehensive"
      Specifies pre-defined feature sets to be extracted
      If str, should be in ["minimal", "efficient", "comprehensive"]
      See [3] for more details.
  kind_to_fc_parameters : list or None, default=None
      List containing strings specifying selected features to be extracted.
      The naming convention from tsfresh applies, i.e. the strings
      should be structured as:
      {time_series_name}__{feature_name}__{param name 1}_
      {param value 1}__[..]__{param name k}_{param value k}.
      See [2] for more details and [4] for viable options.
      Either default_fc_parameters or kind_to_fc_parameters
      should be passed. If both are passed, only features specified
      in kind_to_fc_parameters are extracted. If neither
      is passed, it calculates the "comprehensive"
      feature set.
  n_jobs : int, default=1
      The number of processes to use for parallelization.
      If zero, no parallelization is used.
  chunksize : None or int, default=None
      The size of one chunk that is submitted to the worker
      process for the parallelisation.  Where one chunk is defined as a
      singular time series for one id and one kind. If you set the chunksize
      to 10, then it means that one task is to calculate all features for 10
      time series.  If it is set it to None, depending on distributor,
      heuristics are used to find the optimal chunksize. If you get out of
      memory exceptions, you can try it with the dask distributor and a
      smaller chunksize.
  show_warnings : bool, default=True
      Show warnings during the feature extraction (needed for debugging of
      calculators).
  disable_progressbar : bool, default=False
      Do not show a progressbar while doing the calculation.
  impute_function : None or Callable, default=None
      None, if no imputing should happen or the function to call for
      imputing the result dataframe. Imputing will never happen on the input data.
  profiling : bool, default=None
      Turn on profiling during feature extraction.
  profiling_sorting : basestring, default=None
      How to sort the profiling results (see the documentation of the tsfresh
      profiling package for more information).
  profiling_filename : basestring, default=None
      Where to save the profiling results.
  distributor : distributor class, default=None
      Advanced parameter: set this to a class name that you want to use as a
      distributor. See the tsfresh package utilities/distribution.py for more
      information.
      Leave to None, if you want TSFresh to choose the best distributor.
  test_for_binary_target_binary_feature : str or None, default=None
      Which test to be used for binary target, binary feature (currently unused).
  test_for_binary_target_real_feature : str or None, default=None
      Which test to be used for binary target, real feature.
  test_for_real_target_binary_feature : str or None, default=None
      Which test to be used for real target, binary feature (currently unused).
  test_for_real_target_binary_feature : str or None, default=None
      Which test to be used for real target, real feature (currently unused)
  fdr_level: floar or None, default=None
      The FDR level that should be respected, this is the theoretical expected
      percentage of irrelevant features among all created features.
  hypotheses_independent: bool or None, default=None
      Can the significance of the features be assumed to be independent? Normally,
      this should be set to False as the features are never independent (e.g. mean
      and median)
  ml_task: sre, default="auto"
      The intended machine learning task. Either `'classification'`, `'regression'`
      or `'auto'`.
      Defaults to `'auto'`, meaning the intended task is inferred from `y`.
      If `y` has a boolean, integer or object dtype, the task is assumed to be
      classification, else regression.
  
  References
  ----------
  .. [1]  https://github.com/blue-yonder/tsfresh
  .. [2]  https://tsfresh.readthedocs.io/en/v0.1.2/text/feature_naming.html
  .. [3]  https://tsfresh.readthedocs.io/en/latest/text/
          feature_extraction_settings.html
  .. [4]  https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html
          #module-tsfresh.feature_extraction.feature_calculators
  .. [5] Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018). Time Series
      FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh -- A Python
      package). Neurocomputing 307 (2018) 72-77
  
  Examples
  --------
  >>> from sklearn.model_selection import train_test_split
  >>> from sktime.datasets import load_arrow_head
  >>> from sktime.transformations.panel.tsfresh import (
  ...     TSFreshRelevantFeatureExtractor
  ... )
  >>> X, y = load_arrow_head(return_X_y=True)
  >>> X_train, X_test, y_train, y_test = train_test_split(X, y)
  >>> ts_eff = TSFreshRelevantFeatureExtractor(
  ...     default_fc_parameters="efficient", disable_progressbar=True
  ... ) # doctest: +SKIP
  >>> X_transform1 = ts_eff.fit_transform(X_train, y_train) # doctest: +SKIP
  >>> features_to_calc = [
  ...     "dim_0__quantile__q_0.6",
  ...     "dim_0__longest_strike_above_mean",
  ...     "dim_0__variance",
  ... ]
  >>> ts_custom = TSFreshRelevantFeatureExtractor(
  ...     kind_to_fc_parameters=features_to_calc, disable_progressbar=True
  ... ) # doctest: +SKIP
  >>> X_transform2 = ts_custom.fit_transform(X_train, y_train) # doctest: +SKIP
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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`
docstr_tok: ["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","`"]
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`
      """
      params = {
          "default_fc_parameters": "efficient",
          "disable_progressbar": True,
          "show_warnings": False,
          "fdr_level": 0.01,
      }
      params2 = {
          "default_fc_parameters": "minimal",
          "disable_progressbar": True,
          "show_warnings": False,
      }
      return [params, params2]
  
file_path: sktime/sktime/transformations/panel/tsfresh.py
imports: from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 6 for calling the TSFreshRelevantFeatureExtractor obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list