code: def f_score(evaluated_sentences, reference_sentences, weight=1.0):
      """
      Computation of F-Score measure. It is computed as
      F(E) = ( (W^2 + 1) * P(E) * R(E) ) / ( W^2 * P(E) + R(E) ), where:
  
      - P(E) is precision metrics of extract E.
      - R(E) is recall metrics of extract E.
      - W is a weighting factor that favours P(E) metrics
        when W > 1 and favours R(E) metrics when W < 1.
  
      If W = 1.0 (default value) basic F-Score is computed.
      It is equivalent to F(E) = (2 * P(E) * R(E)) / (P(E) + R(E)).
  
      :parameter iterable evaluated_sentences:
          Sentences of evaluated extract.
      :parameter iterable reference_sentences:
          Sentences of reference extract.
      :returns float:
          Returns 0.0 <= P(E) <= 1.0
      """
      p = precision(evaluated_sentences, reference_sentences)
      r = recall(evaluated_sentences, reference_sentences)
  
      weight **= 2  # weight = weight^2
      denominator = weight * p + r
      if denominator == 0.0:
          return 0.0
      else:
          return ((weight + 1) * p * r) / denominator
  
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
label_desc: Use image node_id 1 for calling a global function with example usage: f_score(evaluated_sentences, reference_sentences, weight) and returns: int, unknown