docstr: Parameters
  ----------
  term_doc_mat : TermDocMatrix
          Basis for scores
  category : str
          Category to score. Only important when finding neutral categories.
  not_categories : list
    List of categories to score against.  If None (the default) the list
     is the remaining
  starting_count : float
          Default 0.01. Add this count to each term seen. If zero, terms not in background counts will be removed.
  term_ranker : TermRanker
          Function to get term frequency convention_df
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code: def __init__(
      self,
      term_doc_mat,
      category=None,
      not_categories=None,
      starting_count=0.0001,
      term_ranker=AbsoluteFrequencyRanker,
  ):
      """
      Parameters
      ----------
      term_doc_mat : TermDocMatrix
              Basis for scores
      category : str
              Category to score. Only important when finding neutral categories.
      not_categories : list
        List of categories to score against.  If None (the default) the list
         is the remaining
      starting_count : float
              Default 0.01. Add this count to each term seen. If zero, terms not in background counts will be removed.
      term_ranker : TermRanker
              Function to get term frequency convention_df
      """
      self.term_doc_mat = term_doc_mat
      self.relevant_categories = []
      if category:
          self.category = category
          assert category in term_doc_mat.get_categories()
          self.relevant_categories += [category]
      else:
          self.category = None
      if not_categories == None:
          not_categories = [
              c for c in term_doc_mat.get_categories() if c != category
          ]
      else:
          assert (
              set(not_categories) - set(term_doc_mat.get_categories())
              == set()
          )
      self.relevant_categories += not_categories
      self.not_categories = not_categories
      self.starting_count = starting_count
      self.term_ranker = term_ranker(term_doc_mat)
      self.use_preset_term_frequencies = False
      self.priors = pd.Series(
          np.zeros(len(self.term_doc_mat.get_terms())),
          index=term_doc_mat.get_terms(),
      )
  
imports: import pandas
  import numpy
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termsignificance.LogOddsRatioInformativeDirichletPiror import LogOddsRatioInformativeDirichletPrior
label_desc: Use image node_id 1 to create a new PriorFactory object from inherited base classes: object with example: obj = PriorFactory(term_doc_mat, category, not_categories, starting_count, term_ranker)