class_docstr: Implements relative entropy approach from
  Peter Fankhauser, Jorg Knappen, Elke Teich. Exploring and visualizing variation in language resources.
  LREC 2014.
  
  ```
  term_scorer = (RelativeEntropy(corpus, smoothing_lambda = 0.05, minimum_p_value=0.05)
          .set_categories('Positive', ['Negative'], ['Plot']))
  
  html = st.produce_frequency_explorer(
          corpus,
          category='Positive',
          not_categories=['Negative'],
          neutral_categories=['Plot'],
          term_scorer=term_scorer,
          metadata=rdf['movie_name'],
          grey_threshold=0,
          show_neutral=True
  )
  file_name = 'rotten_fresh_fre.html'
  open(file_name, 'wb').write(html.encode('utf-8'))
  IFrame(src=file_name, width=1300, height=700)
  ```
docstr: In this case, parameters a and b aren't used, since this information is taken
  directly from the corpus categories.
  
  Returns
  -------
docstr_tok: ["In","this","case",",","parameters","a","and","b","are","n't","used",",","since","this","information","is","taken","directly","from","the","corpus","categories",".","Returns","--","--","--","-"]
code: def get_scores(self, *args):
      """
      In this case, parameters a and b aren't used, since this information is taken
      directly from the corpus categories.
  
      Returns
      -------
  
      """
  
      def jelinek_mercer_smoothing(cat):
          p_hat_w = self.tdf_[cat] * 1.0 / self.tdf_[cat].sum()
          c_hat_w = (
              (self.smoothing_lambda_)
              * self.tdf_.sum(axis=1)
              * 1.0
              / self.tdf_.sum().sum()
          )
          return (
              1 - self.smoothing_lambda_
          ) * p_hat_w + self.smoothing_lambda_ * c_hat_w
  
      p_w = jelinek_mercer_smoothing("cat")
      q_w = jelinek_mercer_smoothing("ncat")
  
      kl_divergence = p_w * np.log(p_w / q_w) / np.log(2)
  
      tt, pvals = self.get_t_statistics()
  
      return kl_divergence * (pvals < self.min_p_)
  
imports: import numpy
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 2 for calling the RelativeEntropy obj's underlying member method code with example usage: obj.get_scores() and returns: unknown, unknown