class_docstr: A class to store regression model summary statistics.
  
  The class can be printed to get a well formatted model summary.
  
  Attributes
  ----------
  x : `numpy.array`
      The design matrix.
  beta : `numpy.array`
      The estimated coefficients.
  y : `numpy.array`
      The response.
  pred_cols : `list` [ `str` ]
      List of predictor names.
  pred_category : `dict`
      Predictor category, returned by
      `~greykite.algo.common.col_name_utils.create_pred_category`.
  fit_algorithm : `str`
      The name of algorithm to fit the regression.
  ml_model : `class`
      The trained machine learning model class.
  max_colwidth : `int`
      The maximum length for predictors to be shown in their original name.
      If the maximum length of predictors exceeds this parameter, all
      predictors name will be suppressed and only indices are shown.
  info_dict : `dict`
      The model summary dictionary, output of
      `~greykite.algo.common.model_summary.ModelSummary._get_summary`
  html_str : `str`
      An html formatting of the string representation of the model summary.
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docstr: Gets the coefficient summary filtered by conditions.
  
  Parameters
  ----------
  is_intercept : `bool` or `None`, default `None`
      Intercept or not.
  is_time_feature : `bool` or `None`, default `None`
      Time features or not.
      Time features belong to `~greykite.common.constants.TimeFeaturesEnum`.
  is_event : `bool` or `None`, default `None`
      Event features or not.
      Event features have `~greykite.common.constants.EVENT_PREFIX`.
  is_trend : `bool` or `None`, default `None`
      Trend features or not.
      Trend features have `~greykite.common.constants.CHANGEPOINT_COL_PREFIX` or "cp\d".
  is_seasonality : `bool` or `None`, default `None`
      Seasonality feature or not.
      Seasonality features have `~greykite.common.constants.SEASONALITY_REGEX`.
  is_lag : `bool` or `None`, default `None`
      Lagged features or not.
      Lagged features have "lag".
  is_regressor : 0 or 1
      Extra features provided by users.
      They are provided through ``extra_pred_cols`` in the fit function.
  is_interaction : `bool` or `None`, default `None`
      Interaction feature or not.
      Interaction features have ":".
  return_df : `bool`, default `False`
     If True, the filtered coefficient summary df is also returned.
      Otherwise, the filtered coefficient summary df is printed only.
  
  Returns
  -------
  filtered_coef_summary : `pandas.DataFrame` or `None`
      If ``return_df`` is set to True, returns the filtered coefficient summary
      df filtered by the given conditions.
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code: def get_coef_summary(
      self,
      is_intercept=None,
      is_time_feature=None,
      is_event=None,
      is_trend=None,
      is_seasonality=None,
      is_lag=None,
      is_regressor=None,
      is_interaction=None,
      return_df=False,
  ):
      """Gets the coefficient summary filtered by conditions.
  
      Parameters
      ----------
      is_intercept : `bool` or `None`, default `None`
          Intercept or not.
      is_time_feature : `bool` or `None`, default `None`
          Time features or not.
          Time features belong to `~greykite.common.constants.TimeFeaturesEnum`.
      is_event : `bool` or `None`, default `None`
          Event features or not.
          Event features have `~greykite.common.constants.EVENT_PREFIX`.
      is_trend : `bool` or `None`, default `None`
          Trend features or not.
          Trend features have `~greykite.common.constants.CHANGEPOINT_COL_PREFIX` or "cp\\d".
      is_seasonality : `bool` or `None`, default `None`
          Seasonality feature or not.
          Seasonality features have `~greykite.common.constants.SEASONALITY_REGEX`.
      is_lag : `bool` or `None`, default `None`
          Lagged features or not.
          Lagged features have "lag".
      is_regressor : 0 or 1
          Extra features provided by users.
          They are provided through ``extra_pred_cols`` in the fit function.
      is_interaction : `bool` or `None`, default `None`
          Interaction feature or not.
          Interaction features have ":".
      return_df : `bool`, default `False`
         If True, the filtered coefficient summary df is also returned.
          Otherwise, the filtered coefficient summary df is printed only.
  
      Returns
      -------
      filtered_coef_summary : `pandas.DataFrame` or `None`
          If ``return_df`` is set to True, returns the filtered coefficient summary
          df filtered by the given conditions.
      """
      filtered_coef_summary = filter_coef_summary(
          coef_summary=self.info_dict["coef_summary_df"],
          pred_category=self.pred_category,
          is_intercept=is_intercept,
          is_time_feature=is_time_feature,
          is_event=is_event,
          is_trend=is_trend,
          is_seasonality=is_seasonality,
          is_lag=is_lag,
          is_regressor=is_regressor,
          is_interaction=is_interaction,
      )
      # excludes column categories
      cols = [
          col
          for col in filtered_coef_summary.columns
          if "is_" not in col
      ]
      print(
          format_summary_df(filtered_coef_summary[cols]).to_string(
              index=False
          )
      )
      if return_df:
          return filtered_coef_summary
  
file_path: greykite/greykite/algo/common/model_summary.py
imports: from greykite.algo.common.col_name_utils import filter_coef_summary
  from greykite.algo.common.model_summary_utils import format_summary_df
  from greykite.algo.common.model_summary_utils import get_info_dict_lm
  from greykite.algo.common.model_summary_utils import get_info_dict_tree
  from greykite.algo.common.model_summary_utils import print_summary
  from greykite.algo.common.model_summary_utils import process_intercept
label_desc: Use image node_id 5 for calling the ModelSummary obj's underlying member method code with example usage: obj.get_coef_summary(is_intercept, is_time_feature, is_event, is_trend, is_seasonality, is_lag, is_regressor, is_interaction, return_df) and returns: filtered_coef_summary