class_docstr: Text spans scoring class.
  Contains misc scoring algorithms for scoring text fragments extracted
  from a corpus.
  
  Arguments:
      documents(list): List of spaCy documents.
      spans(list[list]): List of spaCy spans representing noun phrases of documents
          document.
class_docstr_tok: ["Text","spans","scoring","class",".","Contains","misc","scoring","algorithms","for","scoring","text","fragments","extracted","from","a","corpus",".","Arguments",":","documents","(","list",")",":","List","of","spaCy","documents",".","spans","(","list","[","list","]",")",":","List","of","spaCy","spans","representing","noun","phrases","of","documents","document","."]
code: def normalize_minmax(phrases_list, invert=False):
      _, scores = list(zip(*phrases_list))
      max_score = max(scores)
      min_score = min(scores)
      norm_list = []
      for p, s in phrases_list:
          if max_score - min_score > 0:
              new_score = (s - min_score) / (max_score - min_score)
          else:
              new_score = 0
          norm_list.append([p, new_score])
      if invert:
          for e in norm_list:
              e[1] = 1.0 - e[1]
      return norm_list
  
imports: import itertools
  import math
  import numpy
  import logging
  from spacy.tokens.doc import Doc
  from spacy.tokens.span import Span
  from spacy.tokens.token import Token
label_desc: Use image node_id 9 for calling the TextSpanScoring obj's underlying member method code with example usage: obj.normalize_minmax(phrases_list, invert) and returns: norm_list