class_docstr: Holiday features extraction.
  
  HolidayFeatures uses a dictionary of holidays (which could be a custom made dict
  or imported as HolidayBase object from holidays package) to extract
  holiday features from a datetime index.
  
  Parameters
  ----------
  calendar : HolidayBase object or Dict[date, str]
      Calendar object from holidays package [1]_.
  holiday_windows : Dict[str, tuple], default=None
      Dictionary for specifying a window of days around holidays, with keys
      being holiday names and values being (n_days_before, n_days_after) tuples.
  include_bridge_days: bool, default=False
      If True, include bridge days. Bridge days include Monday if a holiday
      is on Tuesday and Friday if a holiday is on Thursday.
  include_weekend: bool, default=False
      If True, include weekends as holidays.
  return_dummies : bool, default=True
      Whether or not to return a dummy variable for each holiday.
  return_categorical : bool, default=False
      Whether or not to return a categorical variable with holidays
      beings categories.
  return_indicator : bool, default=False
      Whether or not to return an indicator variable equal to 1 if a time
      point is a holiday or not.
  keep_original_columns : bool, default=False
      Keep original columns in X passed to `.transform()`.
  
  Examples
  --------
  >>> import numpy as np  # doctest: +SKIP
  >>> import pandas as pd  # doctest: +SKIP
  >>> from datetime import date  # doctest: +SKIP
  >>> from holidays import country_holidays, financial_holidays  # doctest: +SKIP
  >>> values = np.random.normal(size=365)  # doctest: +SKIP
  >>> index = pd.date_range("2000-01-01", periods=365, freq="D")  # doctest: +SKIP
  >>> X = pd.DataFrame(values, index=index)  # doctest: +SKIP
  
  Returns country holiday features with custom holiday windows
  
  >>> transformer = HolidayFeatures(
  ...    calendar=country_holidays(country="FR"),
  ...    return_categorical=True,
  ...    holiday_windows={"Noël": (1, 3), "Jour de l'an": (1, 0)})  # doctest: +SKIP
  >>> yt = transformer.fit_transform(X)  # doctest: +SKIP
  
  Returns financial holiday features
  
  >>> transformer = HolidayFeatures(
  ...    calendar=financial_holidays(market="NYSE"),
  ...    return_categorical=True,
  ...    include_weekend=True)  # doctest: +SKIP
  >>> yt = transformer.fit_transform(X)  # doctest: +SKIP
  
  Returns custom made holiday features
  
  >>> transformer = HolidayFeatures(
  ...    calendar={date(2000,1,14): "Regional Holiday",
  ...              date(2000, 1, 26): "Regional Holiday"},
  ...    return_categorical=True)  # doctest: +SKIP
  >>> yt = transformer.fit_transform(X)  # doctest: +SKIP
  
  References
  ----------
  .. [1] https://pypi.org/project/holidays/
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docstr: Return testing parameter settings for the estimator.
  
  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",".","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.
  
      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 datetime import date
  
      if _check_soft_dependencies("holidays", severity="none"):
          from holidays import country_holidays, financial_holidays
  
          params = [
              {
                  "calendar": dict(country_holidays(country="GB")),
                  "include_weekend": True,
                  "return_categorical": True,
                  "return_dummies": True,
              },
              {
                  "calendar": dict(financial_holidays(market="NYSE")),
                  "return_indicator": True,
                  "include_bridge_days": True,
                  "return_dummies": False,
              },
          ]
      else:
          params = []
  
      params += [
          {
              "calendar": {date(2022, 5, 15): "Regional Holiday"},
              "return_indicator": True,
          },
      ]
      return params
  
file_path: sktime/sktime/transformations/series/holiday/_holidayfeats.py
imports: import datetime
  from collections import defaultdict
  from datetime import date
  from typing import Dict
  import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 3 for calling the HolidayFeatures obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params