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)
  ```
code: def _set_scorer_args(self, **kwargs):
      self.min_p_ = kwargs.get("minimum_p_value", 0.05)
      self.smoothing_lambda_ = kwargs.get("smoothing_lambda", 0.05)
  
imports: import numpy
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 1 for calling the RelativeEntropy obj's underlying member method code with example usage: obj._set_scorer_args() without return types