class_docstr: Prophet forecaster by wrapping Facebook's prophet algorithm [1]_.
  
  Direct interface to Facebook prophet, using the sktime interface.
  All hyper-parameters are exposed via the constructor.
  
  Data can be passed in one of the sktime compatible formats,
  naming a column `ds` such as in the prophet package is not necessary.
  
  Unlike vanilla `prophet`, also supports integer/range and period index:
  * integer/range index is interpreted as days since Jan 1, 2000
  * `PeriodIndex` is converted using the `pandas` method `to_timestamp`
  
  Parameters
  ----------
  freq: str, default=None
      A DatetimeIndex frequency. For possible values see
      https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
  add_seasonality: dict or None, default=None
      Dict with args for Prophet.add_seasonality().
      Dict can have the following keys/values:
          * name: string name of the seasonality component.
          * period: float number of days in one period.
          * fourier_order: int number of Fourier components to use.
          * prior_scale: optional float prior scale for this component.
          * mode: optional 'additive' or 'multiplicative'
          * condition_name: string name of the seasonality condition.
  add_country_holidays: dict or None, default=None
      Dict with args for Prophet.add_country_holidays().
      Dict can have the following keys/values:
          country_name: Name of the country, like 'UnitedStates' or 'US'
  growth: str, default="linear"
      String 'linear' or 'logistic' to specify a linear or logistic
      trend. If 'logistic' specified float for 'growth_cap' must be provided.
  growth_floor: float, default=0
      Growth saturation minimum value.
      Used only if `growth="logistic"`, has no effect otherwise
      (if `growth` is not `"logistic"`).
  growth_cap: float, default=None
      Growth saturation maximum aka carrying capacity.
      Mandatory (float) iff `growth="logistic"`, has no effect and is optional,
      otherwise (if `growth` is not `"logistic"`).
  changepoints: list or None, default=None
      List of dates at which to include potential changepoints. If
      not specified, potential changepoints are selected automatically.
  n_changepoints: int, default=25
      Number of potential changepoints to include. Not used
      if input `changepoints` is supplied. If `changepoints` is not supplied,
      then n_changepoints potential changepoints are selected uniformly from
      the first `changepoint_range` proportion of the history.
  changepoint_range: float, default=0.8
      Proportion of history in which trend changepoints will
      be estimated. Defaults to 0.8 for the first 80%. Not used if
      `changepoints` is specified.
  yearly_seasonality: str or bool or int, default="auto"
      Fit yearly seasonality.
      Can be 'auto', True, False, or a number of Fourier terms to generate.
  weekly_seasonality: str or bool or int, default="auto"
      Fit weekly seasonality.
      Can be 'auto', True, False, or a number of Fourier terms to generate.
  daily_seasonality: str or bool or int, default="auto"
      Fit daily seasonality.
      Can be 'auto', True, False, or a number of Fourier terms to generate.
  holidays: pd.DataFrame or None, default=None
      pd.DataFrame with columns holiday (string) and ds (date type)
      and optionally columns lower_window and upper_window which specify a
      range of days around the date to be included as holidays.
      lower_window=-2 will include 2 days prior to the date as holidays. Also
      optionally can have a column prior_scale specifying the prior scale for
      that holiday.
  seasonality_mode: str, default='additive'
      Take one of 'additive' or 'multiplicative'.
  seasonality_prior_scale: float, default=10.0
      Parameter modulating the strength of the seasonality model.
      Larger values allow the model to fit larger seasonal
      fluctuations, smaller values dampen the seasonality. Can be specified
      for individual seasonalities using add_seasonality.
  holidays_prior_scale: float, default=10.0
      Parameter modulating the strength of the holiday
      components model, unless overridden in the holidays input.
  changepoint_prior_scale: float, default=0.05
      Parameter modulating the flexibility of the
      automatic changepoint selection. Large values will allow many
      changepoints, small values will allow few changepoints.
  mcmc_samples: int, default=0
      If greater than 0, will do full Bayesian inference
      with the specified number of MCMC samples. If 0, will do MAP
      estimation.
  alpha: float, default=0.05
      Width of the uncertainty intervals provided
      for the forecast. If mcmc_samples=0, this will be only the uncertainty
      in the trend using the MAP estimate of the extrapolated generative
      model. If mcmc.samples>0, this will be integrated over all model
      parameters, which will include uncertainty in seasonality.
  uncertainty_samples: int, default=1000
      Number of simulated draws used to estimate uncertainty intervals.
      Settings this value to 0 or False will disable
      uncertainty estimation and speed up the calculation.
  stan_backend: str or None, default=None
      str as defined in StanBackendEnum. If None, will try to
      iterate over all available backends and find the working one.
  fit_kwargs: dict or None, default=None
      Dict with args for Prophet.fit().
      These are additional arguments passed to the optimizing or sampling
      functions in Stan.
  
  References
  ----------
  .. [1] https://facebook.github.io/prophet
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.fbprophet import Prophet
  >>> # Prophet requires to have data with a pandas.DatetimeIndex
  >>> y = load_airline().to_timestamp(freq='M')
  >>> forecaster = Prophet(  # doctest: +SKIP
  ...     seasonality_mode='multiplicative',
  ...     n_changepoints=int(len(y) / 12),
  ...     add_country_holidays={'country_name': 'Germany'},
  ...     yearly_seasonality=True)
  >>> forecaster.fit(y)  # doctest: +SKIP
  Prophet(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])  # 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
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"]
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
      """
      params = {
          "n_changepoints": 0,
          "yearly_seasonality": False,
          "weekly_seasonality": False,
          "daily_seasonality": False,
          "uncertainty_samples": 10,
          "verbose": False,
          "fit_kwargs": {"seed": 12345},
      }
      return params
  
imports: from sktime.forecasting.base._base import DEFAULT_ALPHA
  from sktime.forecasting.base.adapters import _ProphetAdapter
label_desc: Use image node_id 3 for calling the Prophet obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params