docstr: Wrapper for SkTime's Prophet interface.
   
  :param stop_after: time budget in seconds
  :param target: column containing target time series
  :param dtype_dict: data types for each dataset column
  :param horizon: forecast length
  :param ts_analysis: lightwood-produced stats about input time series
  :param auto_size: whether to filter out old data points if training split is bigger than a certain threshold (defined by the dataset sampling frequency). Enabled by default to avoid long training times in big datasets.
  :param use_stl: Whether to use de-trenders and de-seasonalizers fitted in the timeseries analysis phase.
  
  For the rest of the parameters, please refer to SkTime's documentation.
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code: def __init__(
      self,
      stop_after: float,
      target: str,
      dtype_dict: Dict[str, str],
      horizon: int,
      ts_analysis: Dict,
      auto_size: bool = True,
      use_stl: bool = False,
      add_seasonality: Optional[dict] = None,
      add_country_holidays: Optional[dict] = None,
      growth: str = "linear",
      growth_floor: float = 0,
      growth_cap: Optional[float] = None,
      changepoints: Optional[list] = None,
      n_changepoints: int = 25,
      changepoint_range: float = 0.8,
      yearly_seasonality: Union[str, bool, int] = "auto",
      weekly_seasonality: Union[str, bool, int] = "auto",
      daily_seasonality: Union[str, bool, int] = "auto",
      holidays: Optional[pd.DataFrame] = None,
      seasonality_mode: str = "additive",
      seasonality_prior_scale: float = 10.0,
      holidays_prior_scale: float = 10.0,
      changepoint_prior_scale: float = 0.05,
      mcmc_samples: int = 0,
      alpha: float = 0.05,
      uncertainty_samples: int = 1000,
  ):
      """
      Wrapper for SkTime's Prophet interface.
  
      :param stop_after: time budget in seconds
      :param target: column containing target time series
      :param dtype_dict: data types for each dataset column
      :param horizon: forecast length
      :param ts_analysis: lightwood-produced stats about input time series
      :param auto_size: whether to filter out old data points if training split is bigger than a certain threshold (defined by the dataset sampling frequency). Enabled by default to avoid long training times in big datasets.
      :param use_stl: Whether to use de-trenders and de-seasonalizers fitted in the timeseries analysis phase.
  
      For the rest of the parameters, please refer to SkTime's documentation.
      """  # noqa
  
      # overrides
      sp = 1
      hyperparam_search = False
      model_path = "fbprophet.Prophet"
      model_kwargs = {
          "add_seasonality": add_seasonality,
          "add_country_holidays": add_country_holidays,
          "growth": growth,
          "growth_floor": growth_floor,
          "growth_cap": growth_cap,
          "changepoints": changepoints,
          "n_changepoints": n_changepoints,
          "changepoint_range": changepoint_range,
          "yearly_seasonality": yearly_seasonality,
          "weekly_seasonality": weekly_seasonality,
          "daily_seasonality": daily_seasonality,
          "holidays": holidays,
          "seasonality_mode": seasonality_mode,
          "seasonality_prior_scale": seasonality_prior_scale,
          "holidays_prior_scale": holidays_prior_scale,
          "changepoint_prior_scale": changepoint_prior_scale,
          "mcmc_samples": mcmc_samples,
          "alpha": alpha,
          "uncertainty_samples": uncertainty_samples,
      }
  
      # setup sktime base mixer
      super().__init__(
          stop_after,
          target,
          dtype_dict,
          horizon,
          ts_analysis,
          model_path,
          model_kwargs,
          auto_size,
          sp,
          hyperparam_search,
          use_stl,
      )
      self.name = "Prophet"
      self.stable = False
  
imports: from typing import Dict, Optional, Union
  import pandas
  from lightwood.mixer.sktime import SkTime
label_desc: Use image node_id 1 to create a new ProphetMixer object from inherited base classes: SkTime with example: obj = ProphetMixer(stop_after, target, dtype_dict, horizon, ts_analysis, auto_size, use_stl, add_seasonality, add_country_holidays, growth, growth_floor, growth_cap, changepoints, n_changepoints, changepoint_range, yearly_seasonality, weekly_seasonality, daily_seasonality, holidays, seasonality_mode, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, alpha, uncertainty_samples)