class_docstr: Wrapper for `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`.
  
  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 "linear"
              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.
  fs_components_df : `pandas.DataFrame` or None, optional
      A dataframe with information about fourier series generation.
      If provided, it must contain columns with following names:
  
      - `"name"`: name of the timeseries feature (e.g. ``tod``, ``tow`` etc.).
      - `"period"`: Period of the fourier series.
      - `"order"`: Order of the fourier series.
        `"seas_names"`: Label for the type of seasonality (e.g. ``daily``, ``weekly`` etc.)
        and should be unique.
      - `~greykite.sklearn.estimator.silverkite_estimator.SilverkiteEstimator.validate_fs_components_df`
        checks for it, so that component plots don't have duplicate y-axis labels.
  
      This differs from the expected input of `forecast_silverkite` where `"period"`, `"order"`
      and `"seas_names"` are optional. This restriction is to facilitate appropriate computation
      of component (e.g. trend, seasonalities and holidays) effects. See Notes section in this
      docstring for a more detailed explanation with examples.
  
  
  Other parameters are the same as in
  `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`.
  
  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.
  
  The attributes are the same as
  `~greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator`.
  
  See Also
  --------
  `~greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator`
      For details on fit, predict, and component plots.
  `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`
      Functions performing the fit and predict.
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code: def __init__(
      self,
      silverkite: SilverkiteForecast = SilverkiteForecast(),
      score_func=mean_squared_error,
      coverage=None,
      null_model_params=None,
      freq=None,
      origin_for_time_vars=None,
      extra_pred_cols=None,
      drop_pred_cols=None,
      explicit_pred_cols=None,
      train_test_thresh=None,
      training_fraction=None,
      fit_algorithm_dict=None,
      daily_event_df_dict=None,
      daily_event_neighbor_impact=None,
      daily_event_shifted_effect=None,
      fs_components_df=pd.DataFrame(
          {
              "name": [
                  cst.TimeFeaturesEnum.tod.value,
                  cst.TimeFeaturesEnum.tow.value,
                  cst.TimeFeaturesEnum.conti_year.value,
              ],
              "period": [24.0, 7.0, 1.0],
              "order": [3, 3, 5],
              "seas_names": ["daily", "weekly", "yearly"],
          }
      ),
      autoreg_dict=None,
      past_df=None,
      lagged_regressor_dict=None,
      changepoints_dict=None,
      seasonality_changepoints_dict=None,
      changepoint_detector=None,
      min_admissible_value=None,
      max_admissible_value=None,
      uncertainty_dict=None,
      normalize_method=None,
      adjust_anomalous_dict=None,
      impute_dict=None,
      regression_weight_col=None,
      forecast_horizon=None,
      simulation_based=False,
      simulation_num=10,
      fast_simulation=False,
      remove_intercept=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.freq = freq
      self.origin_for_time_vars = origin_for_time_vars
      self.extra_pred_cols = extra_pred_cols
      self.drop_pred_cols = drop_pred_cols
      self.explicit_pred_cols = explicit_pred_cols
      self.train_test_thresh = train_test_thresh
      self.fit_algorithm_dict = fit_algorithm_dict
      self.training_fraction = training_fraction
      self.daily_event_df_dict = daily_event_df_dict
      self.daily_event_neighbor_impact = daily_event_neighbor_impact
      self.daily_event_shifted_effect = daily_event_shifted_effect
      self.fs_components_df = fs_components_df
      self.autoreg_dict = autoreg_dict
      self.past_df = past_df
      self.lagged_regressor_dict = lagged_regressor_dict
      self.changepoints_dict = changepoints_dict
      self.seasonality_changepoints_dict = seasonality_changepoints_dict
      self.changepoint_detector = changepoint_detector
      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.adjust_anomalous_dict = adjust_anomalous_dict
      self.impute_dict = impute_dict
      self.regression_weight_col = regression_weight_col
      self.forecast_horizon = forecast_horizon
      self.simulation_based = simulation_based
      self.simulation_num = simulation_num
      self.fast_simulation = fast_simulation
      self.remove_intercept = remove_intercept
      self.validate_inputs()
  
file_path: greykite/greykite/sklearn/estimator/silverkite_estimator.py
imports: import pandas
  from sklearn.metrics import mean_squared_error
  from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
  from greykite.common import constants
  from greykite.common.python_utils import update_dictionary
  from greykite.sklearn.estimator.base_silverkite_estimator import BaseSilverkiteEstimator
label_desc: Use image node_id 1 to create a new SilverkiteEstimator object from inherited base classes: BaseSilverkiteEstimator with example: obj = SilverkiteEstimator(silverkite, score_func, coverage, null_model_params, freq, origin_for_time_vars, extra_pred_cols, drop_pred_cols, explicit_pred_cols, train_test_thresh, training_fraction, fit_algorithm_dict, daily_event_df_dict, daily_event_neighbor_impact, daily_event_shifted_effect, fs_components_df, autoreg_dict, past_df, lagged_regressor_dict, changepoints_dict, seasonality_changepoints_dict, changepoint_detector, min_admissible_value, max_admissible_value, uncertainty_dict, normalize_method, adjust_anomalous_dict, impute_dict, regression_weight_col, forecast_horizon, simulation_based, simulation_num, fast_simulation, remove_intercept)