docstr: Parameters
  ----------
  cat_word_counts : np.array
      category counts
  not_cat_word_counts : np.array
      not category counts
  
  Returns
  -------
  np.array
      scores
docstr_tok: ["Parameters","--","--","--","--","--","cat_word_counts",":","np.array","category","counts","not_cat_word_counts",":","np.array","not","category","counts","Returns","--","--","--","-","np.array","scores"]
code: def get_scores(self, cat_word_counts, not_cat_word_counts):
      """
      Parameters
      ----------
      cat_word_counts : np.array
          category counts
      not_cat_word_counts : np.array
          not category counts
  
      Returns
      -------
      np.array
          scores
      """
      cat_scores = self.get_scores_for_category(
          cat_word_counts, not_cat_word_counts
      )
      not_cat_scores = self.get_scores_for_category(
          not_cat_word_counts, cat_word_counts
      )
      if self.use_score_difference_:
          scores = ((cat_scores - not_cat_scores) + 1.0) / 2.0
      else:
          scores = ScoreBalancer.balance_scores(
              cat_scores, not_cat_scores
          )
      if self.one_to_neg_one_:
          return 2 * scores - 1
      else:
          return scores
  
imports: from abc import ABC, abstractmethod
  import numpy
  import pandas
  from scipy.stats import norm, rankdata
  from scattertext.Common import DEFAULT_SCALER_ALGO, DEFAULT_BETA
label_desc: Use image node_id 4 for calling the ScaledFScorePresets obj's underlying member method code with example usage: obj.get_scores(cat_word_counts, not_cat_word_counts) and returns: unknown, scores