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_for_category(
      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
      """
      beta = self.beta_
      # import pdb; pdb.set_trace()
      assert len(cat_word_counts) == len(not_cat_word_counts)
      old_cat_word_counts = None
      if type(cat_word_counts) == pd.Series:
          assert all(cat_word_counts.index == not_cat_word_counts.index)
  
          old_cat_word_counts = cat_word_counts
          cat_word_counts = cat_word_counts.values
      if type(not_cat_word_counts) == pd.Series:
          not_cat_word_counts = not_cat_word_counts.values
      if self.priors_ is not None:
          p = self.priors_
          assert len(p) == len(cat_word_counts)
          precision = (cat_word_counts + p * 1.0) / (
              cat_word_counts + not_cat_word_counts + 2 * p
          )
          recall = (
              (cat_word_counts + p)
              * 1.0
              / (cat_word_counts.sum() + p.sum())
          )
      else:
          precision = (
              cat_word_counts
              * 1.0
              / (cat_word_counts + not_cat_word_counts)
          )
          recall = cat_word_counts * 1.0 / cat_word_counts.sum()
      precision_normcdf = ScaledFScore._safe_scaler(
          self.scaler_algo_, precision
      )
      recall_normcdf = ScaledFScore._safe_scaler(
          self.scaler_algo_, recall
      )
      scores = self._weighted_h_mean(precision_normcdf, recall_normcdf)
      scores[np.isnan(scores)] = 0.0
      if old_cat_word_counts is not None:
          return pd.Series(scores, index=old_cat_word_counts.index)
      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 5 for calling the ScaledFScorePresets obj's underlying member method code with example usage: obj.get_scores_for_category(cat_word_counts, not_cat_word_counts) and returns: scores, pd