class_docstr: priors = (st.PriorFactory(unigram_corpus,
                        category='Positive',
                        not_categories=['Negative'],
                        starting_count=0.01)
        .use_neutral_categories()
            .get_priors())
  html = st.produce_frequency_explorer(
      unigram_corpus,
      category='Positive',
      category_name='Negative',
      not_category_name='Negative',
      term_scorer=st.LogOddsRatioInformativePriorScorer,
      term_scorer_kwargs={'prior_scale': 6},
      metadata=get_heading,
      minimum_term_frequency=0,
      grey_threshold=0,
  )
class_docstr_tok: ["priors","=","(","st.PriorFactory","(","unigram_corpus",",","category='Positive","'",",","not_categories=","[","'Negative","'","]",",","starting_count=0.01",")",".use_neutral_categories","(",")",".get_priors","(",")",")","html","=","st.produce_frequency_explorer","(","unigram_corpus",",","category='Positive","'",",","category_name='Negative","'",",","not_category_name='Negative","'",",","term_scorer=st.LogOddsRatioInformativePriorScorer",",","term_scorer_kwargs=","{","'prior_scale","'",":","6","}",",","metadata=get_heading",",","minimum_term_frequency=0",",","grey_threshold=0",",",")"]
code: def _set_scorer_args(self, prior_counts, prior_scale):
      self.prior_counts = prior_counts
      self.prior_scale = prior_scale
  
file_path: scattertext/scattertext/termscoring/LogOddsRatio.py
imports: import numpy
  import pandas
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 1 for calling the LogOddsRatioInformativePriorScorer obj's underlying member method code with example usage: obj._set_scorer_args(prior_counts, prior_scale) without return types