class_docstr: Log likelihood ratio (inspired by https://github.com/Zeta-and-Company/pydistinto/blob/main/scripts/measures/LLR.py)
class_docstr_tok: ["Log","likelihood","ratio","(","inspired","by","https",":","\/\/github.com\/Zeta-and-Company\/pydistinto\/blob\/main\/scripts\/measures\/LLR.py",")"]
docstr: In this case, args aren't used, since this information is taken
  directly from the corpus categories.
  
  Returns
  -------
  np.array, scores
docstr_tok: ["In","this","case",",","args","are","n't","used",",","since","this","information","is","taken","directly","from","the","corpus","categories",".","Returns","--","--","--","-","np.array",",","scores"]
code: def get_scores(self, *args) -> pd.Series:
      """
      In this case, args aren't used, since this information is taken
      directly from the corpus categories.
  
      Returns
      -------
      np.array, scores
      """
      X = self._get_X() > 0
      cat_X, ncat_X = self._get_cat_and_ncat(X)
      return pd.Series(
          self._get_llr_score(cat_X, ncat_X).statistic,
          index=self._get_terms(),
      )
  
file_path: scattertext/scattertext/termscoring/loglikelihoodratio.py
imports: import pandas
  from scipy.stats import power_divergence
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 2 for calling the LogLikelihoodRatio obj's underlying member method code with example usage: obj.get_scores() and returns: pd