code: def fbeta(log_preds, targs, beta, thresh=0.5, epsilon=1e-8):
      
      assert beta > 0, 'beta needs to be greater than 0'
      beta2 = beta ** 2
      rec = recall(log_preds, targs, thresh)
      prec = precision(log_preds, targs, thresh)
      return (1 + beta2) * prec * rec / (beta2 * prec + rec + epsilon)
docs: Calculates the F-beta score (the weighted harmonic mean of precision and recall).
      This is the micro averaged version where the true positives, false negatives and
      false positives are calculated globally (as opposed to on a per label basis).
  
      beta == 1 places equal weight on precision and recall, b < 1 emphasizes precision and
      beta > 1 favors recall.