class_docstr: Zeta function from:
  
  Pervez Rizvi. The interpretation of Zeta test results.
  Digital Scholarship in the Humanities, Volume 34, Issue 2, June 2019, Pages 401–418,
  
  The score assumes that each document ("segment" in this literature) is approximately of equal size.
  Rizvi reports segment sizes being 900-6,000 words.
  
  Z_t = (# segments with type t labeled in the target category)/(# segments in target category) +
   (# segments with type t labeled not in the target category)/(# segments not in target category)
class_docstr_tok: ["Zeta","function","from",":","Pervez","Rizvi",".","The","interpretation","of","Zeta","test","results",".","Digital","Scholarship","in","the","Humanities",",","Volume","34",",","Issue","2",",","June","2019",",","Pages","401\u2013418",",","The","score","assumes","that","each","document","(","``","segment","''","in","this","literature",")","is","approximately","of","equal","size",".","Rizvi","reports","segment","sizes","being","900-6,000","words",".","Z_t","=","(","#","segments","with","type","t","labeled","in","the","target","category",")","\/","(","#","segments","in","target","category",")","+","(","#","segments","with","type","t","labeled","not","in","the","target","category",")","\/","(","#","segments","not","in","target","category",")"]
code: def _set_scorer_args(self, **kwargs):
      self.constant = kwargs.get("constant", 0.01)
  
file_path: scattertext/scattertext/termscoring/craigs_zeta.py
imports: import pandas
  import numpy
  from scattertext.termranking import OncePerDocFrequencyRanker
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 1 for calling the CraigsZetaScorer obj's underlying member method code with example usage: obj._set_scorer_args() without return types