docstr: Parameters
  ----------
  term_ranker : TermRanker, default is OncePerDocFrequencyRanker
  background_frequencies : BackgroundFrequencies
  rerank_ranks : bool, False by default
          orders scores from 0 to 1 by their dense rank
docstr_tok: ["Parameters","--","--","--","--","--","term_ranker",":","TermRanker",",","default","is","OncePerDocFrequencyRanker","background_frequencies",":","BackgroundFrequencies","rerank_ranks",":","bool",",","False","by","default","orders","scores","from","0","to","1","by","their","dense","rank"]
code: def __init__(
      self,
      term_ranker=AbsoluteFrequencyRanker,
      background_frequencies=DefaultBackgroundFrequencies,
      rerank_ranks=False,
  ):
      """
      Parameters
      ----------
      term_ranker : TermRanker, default is OncePerDocFrequencyRanker
      background_frequencies : BackgroundFrequencies
      rerank_ranks : bool, False by default
              orders scores from 0 to 1 by their dense rank
      """
      self.term_ranker = term_ranker
      self.background_frequencies = background_frequencies
      self.rerank_ranks = rerank_ranks
  
file_path: scattertext/scattertext/characteristic/DenseRankCharacteristicness.py
imports: import pandas
  from scipy.stats import rankdata
  from scattertext.Scalers import scale
  from scattertext.frequencyreaders.DefaultBackgroundFrequencies import DefaultBackgroundFrequencies
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termscoring.RankDifference import RankDifference
label_desc: Use image node_id 1 to create a new CharacteristicScorer object from inherited base classes: object with example: obj = CharacteristicScorer(term_ranker, background_frequencies, rerank_ranks)