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 get_freq_scores(self, group_similar_spans=True):
      try:
          from wordfreq import zipf_frequency
  
          _has_wordfreq = True
      except (AttributeError, ImportError):
          logger.error(
              "wordfreq is not installed, please install nlp_architect with [all] package. "
              + "for example: pip install nlp_architect[all]"
          )
          _has_wordfreq = False
  
      if _has_wordfreq:
          phrases_and_scores = {}
          for _, noun_phrases in zip(
              self.documents, self.doc_text_spans
          ):
              for p in noun_phrases:
                  if p not in phrases_and_scores:
                      phrases_and_scores[p] = zipf_frequency(
                          p.text, "en"
                      )
          return self._maybe_group_and_sort(
              group_similar_spans, phrases_and_scores
          )
      return None
  
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 6 for calling the TextSpanScoring obj's underlying member method code with example usage: obj.get_freq_scores(group_similar_spans) and returns: None, self