class_docstr: Results for Variable Screening
  
  Note: Indices except for exog_idx and in the iterated case also
  idx_nonzero_batches are based on the combined [exog_keep, exog] array.
  
  Attributes
  ----------
  results_final : instance
      Results instance returned by the final fit of the penalized model, i.e.
      after trimming exog with params below trimming threshold.
  results_pen : results instance
      Results instance of the penalized model before trimming. This includes
      variables from the last forward selection
  idx_nonzero
      index of exog columns in the final selection including exog_keep
  idx_exog
      index of exog columns in the final selection for exog candidates, i.e.
      without exog_keep
  idx_excl
      idx of excluded exog based on combined [exog_keep, exog] array. This is
      the complement of idx_nonzero
  converged : bool
      True if the iteration has converged and stopped before maxiter has been
      reached. False if maxiter has been reached.
  iterations : int
      number of iterations in the screening process. Each iteration consists
      of a forward selection step and a trimming step.
  history : dict of lists
      results collected for each iteration during the screening process
      'idx_nonzero' 'params_keep'].append(start_params)
          history['idx_added'].append(idx)
  
  The ScreeningResults returned by `screen_exog_iterator` has additional
  attributes:
  
  idx_nonzero_batches : ndarray 2-D
      Two-dimensional array with batch index in the first column and variable
      index withing batch in the second column. They can be used jointly as
      index for the data in the exog_iterator.
  exog_final_names : list[str]
      'var<bidx>_<idx>' where `bidx` is the batch index and `idx` is the
      index of the selected column withing batch `bidx`.
  history_batches : dict of lists
      This provides information about the selected variables within each
      batch during the first round screening
      'idx_nonzero' is based ond the array that includes exog_keep, while
      'idx_exog' is the index based on the exog of the batch.
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code: def __init__(self, screener, **kwds):
      self.screener = screener
      self.__dict__.update(**kwds)
  
file_path: statsmodels/statsmodels/base/_screening.py
imports: from collections import defaultdict
  import numpy
  from statsmodels.base._penalties import SCADSmoothed
label_desc: Use image node_id 1 to create a new ScreeningResults object  with example: obj = ScreeningResults(screener)