class_docstr: Wrapper for `~greykite.algo.forecast.silverkite.forecast_simple_silverkite.forecast_simple_silverkite`.
  
  Parameters
  ----------
  score_func : callable, optional, default mean_squared_error
      See `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`.
  coverage : `float` between [0.0, 1.0] or None, optional
      See `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`.
  null_model_params : `dict` or None, optional
      Dictionary with arguments to define ``DummyRegressor`` null model, default is `None`.
      See `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`.
  fit_algorithm_dict : `dict` or None, optional
      How to fit the model. A dictionary with the following optional keys.
  
          ``"fit_algorithm"`` : `str`, optional, default "ridge"
              The type of predictive model used in fitting.
  
              See `~greykite.algo.common.ml_models.fit_model_via_design_matrix`
              for available options and their parameters.
          ``"fit_algorithm_params"`` : `dict` or None, optional, default None
              Parameters passed to the requested fit_algorithm.
              If None, uses the defaults in `~greykite.algo.common.ml_models.fit_model_via_design_matrix`.
  
  uncertainty_dict : `dict` or `str` or None, optional
      How to fit the uncertainty model.
      See `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`.
      Note that this is allowed to be "auto". If None or "auto", will be set to
      a default value by ``coverage`` before calling ``forecast_silverkite``.
      See ``BaseForecastEstimator`` for details.
  
  kwargs : additional parameters
  
      Other parameters are the same as in
      `~greykite.algo.forecast.silverkite.forecast_simple_silverkite.forecast_simple_silverkite`.
  
      See source code ``__init__`` for the parameter names, and refer to
      `~greykite.algo.forecast.silverkite.forecast_simple_silverkite.forecast_simple_silverkite` for
      their description.
  
      If this Estimator is called from
      :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`,
      ``train_test_thresh`` and ``training_fraction`` should almost
      always be `None`, because train/test is handled outside
      this Estimator.
  
  Notes
  -----
  Attributes match those of
  `~greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator`.
  
  See Also
  --------
  `~greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator`
      For attributes and details on fit, predict, and component plots.
  `~greykite.algo.forecast.silverkite.forecast_simple_silverkite.forecast_simple_silverkite`
      Function to transform the parameters to call ``forecast_silverkite`` fit.
  `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`
      Functions performing the fit and predict.
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code: def __init__(
      self,
      silverkite: SimpleSilverkiteForecast = SimpleSilverkiteForecast(),
      score_func: callable = mean_squared_error,
      coverage: float = None,
      null_model_params: Optional[Dict] = None,
      time_properties: Optional[Dict] = None,
      freq: Optional[str] = None,
      forecast_horizon: Optional[int] = None,
      origin_for_time_vars: Optional[float] = None,
      train_test_thresh: Optional[datetime] = None,
      training_fraction: Optional[float] = None,
      fit_algorithm_dict: Optional[Dict] = None,
      auto_holiday: bool = False,
      holidays_to_model_separately: Optional[
          Union[str, List[str]]
      ] = "auto",
      holiday_lookup_countries: Optional[
          Union[str, List[str]]
      ] = "auto",
      holiday_pre_num_days: int = 2,
      holiday_post_num_days: int = 2,
      holiday_pre_post_num_dict: Optional[Dict] = None,
      daily_event_df_dict: Optional[Dict] = None,
      auto_holiday_params: Optional[Dict] = None,
      daily_event_neighbor_impact: Optional[
          Union[int, List[int], callable]
      ] = None,
      daily_event_shifted_effect: Optional[List[str]] = None,
      auto_growth: bool = False,
      changepoints_dict: Optional[Dict] = None,
      auto_seasonality: bool = False,
      yearly_seasonality: Union[bool, str, int] = "auto",
      quarterly_seasonality: Union[bool, str, int] = "auto",
      monthly_seasonality: Union[bool, str, int] = "auto",
      weekly_seasonality: Union[bool, str, int] = "auto",
      daily_seasonality: Union[bool, str, int] = "auto",
      max_daily_seas_interaction_order: Optional[int] = None,
      max_weekly_seas_interaction_order: Optional[int] = None,
      autoreg_dict: Optional[Dict] = None,
      past_df: Optional[pd.DataFrame] = None,
      lagged_regressor_dict: Optional[Dict] = None,
      seasonality_changepoints_dict: Optional[Dict] = None,
      min_admissible_value: Optional[float] = None,
      max_admissible_value: Optional[float] = None,
      uncertainty_dict: Optional[Dict] = None,
      normalize_method: Optional[str] = None,
      growth_term: Optional[str] = cst.GrowthColEnum.linear.name,
      regressor_cols: Optional[List[str]] = None,
      feature_sets_enabled: Optional[
          Union[bool, Dict[str, bool]]
      ] = None,
      extra_pred_cols: Optional[List[str]] = None,
      drop_pred_cols: Optional[List[str]] = None,
      explicit_pred_cols: Optional[List[str]] = None,
      regression_weight_col: Optional[str] = None,
      simulation_based: Optional[bool] = False,
      simulation_num: int = 10,
      fast_simulation: bool = False,
      remove_intercept: bool = False,
  ):
      # every subclass of BaseSilverkiteEstimator must call super().__init__
      super().__init__(
          silverkite=silverkite,
          score_func=score_func,
          coverage=coverage,
          null_model_params=null_model_params,
          uncertainty_dict=uncertainty_dict,
      )
  
      # necessary to set parameters, to ensure get_params() works
      # (used in grid search)
      self.score_func = score_func
      self.coverage = coverage
      self.null_model_params = null_model_params
      self.time_properties = time_properties
      self.freq = freq
      self.forecast_horizon = forecast_horizon
      self.origin_for_time_vars = origin_for_time_vars
      self.train_test_thresh = train_test_thresh
      self.training_fraction = training_fraction
      self.fit_algorithm_dict = fit_algorithm_dict
      self.auto_holiday = auto_holiday
      self.holidays_to_model_separately = holidays_to_model_separately
      self.holiday_lookup_countries = holiday_lookup_countries
      self.holiday_pre_num_days = holiday_pre_num_days
      self.holiday_post_num_days = holiday_post_num_days
      self.holiday_pre_post_num_dict = holiday_pre_post_num_dict
      self.daily_event_df_dict = daily_event_df_dict
      self.auto_holiday_params = auto_holiday_params
      self.daily_event_neighbor_impact = daily_event_neighbor_impact
      self.daily_event_shifted_effect = daily_event_shifted_effect
      self.auto_growth = auto_growth
      self.changepoints_dict = changepoints_dict
      self.auto_seasonality = auto_seasonality
      self.yearly_seasonality = yearly_seasonality
      self.quarterly_seasonality = quarterly_seasonality
      self.monthly_seasonality = monthly_seasonality
      self.weekly_seasonality = weekly_seasonality
      self.daily_seasonality = daily_seasonality
      self.max_daily_seas_interaction_order = (
          max_daily_seas_interaction_order
      )
      self.max_weekly_seas_interaction_order = (
          max_weekly_seas_interaction_order
      )
      self.autoreg_dict = autoreg_dict
      self.past_df = past_df
      self.lagged_regressor_dict = lagged_regressor_dict
      self.seasonality_changepoints_dict = seasonality_changepoints_dict
      self.min_admissible_value = min_admissible_value
      self.max_admissible_value = max_admissible_value
      self.uncertainty_dict = uncertainty_dict
      self.normalize_method = normalize_method
      self.growth_term = growth_term
      self.regressor_cols = regressor_cols
      self.feature_sets_enabled = feature_sets_enabled
      self.extra_pred_cols = extra_pred_cols
      self.drop_pred_cols = drop_pred_cols
      self.explicit_pred_cols = explicit_pred_cols
      self.regression_weight_col = regression_weight_col
      self.simulation_based = simulation_based
      self.simulation_num = simulation_num
      self.fast_simulation = fast_simulation
      self.remove_intercept = remove_intercept
  
file_path: greykite/greykite/sklearn/estimator/simple_silverkite_estimator.py
imports: from datetime import datetime
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Union
  import pandas
  from sklearn.metrics import mean_squared_error
  from greykite.algo.forecast.silverkite.forecast_simple_silverkite import SimpleSilverkiteForecast
  from greykite.common import constants
  from greykite.common.python_utils import update_dictionary
  from greykite.sklearn.estimator.base_silverkite_estimator import BaseSilverkiteEstimator
  from greykite.sklearn.uncertainty.uncertainty_methods import UncertaintyMethodEnum
label_desc: Use image node_id 1 to create a new SimpleSilverkiteEstimator object from inherited base classes: BaseSilverkiteEstimator with example: obj = SimpleSilverkiteEstimator(silverkite, score_func, coverage, null_model_params, time_properties, freq, forecast_horizon, origin_for_time_vars, train_test_thresh, training_fraction, fit_algorithm_dict, auto_holiday, holidays_to_model_separately, holiday_lookup_countries, holiday_pre_num_days, holiday_post_num_days, holiday_pre_post_num_dict, daily_event_df_dict, auto_holiday_params, daily_event_neighbor_impact, daily_event_shifted_effect, auto_growth, changepoints_dict, auto_seasonality, yearly_seasonality, quarterly_seasonality, monthly_seasonality, weekly_seasonality, daily_seasonality, max_daily_seas_interaction_order, max_weekly_seas_interaction_order, autoreg_dict, past_df, lagged_regressor_dict, seasonality_changepoints_dict, min_admissible_value, max_admissible_value, uncertainty_dict, normalize_method, growth_term, regressor_cols, feature_sets_enabled, extra_pred_cols, drop_pred_cols, explicit_pred_cols, regression_weight_col, simulation_based, simulation_num, fast_simulation, remove_intercept)