code: def correlation_by_exemplar(brands, exemplars, validation_scores, analyze_fn_str, outf):
      
      analyze_fn = getattr(analyze, analyze_fn_str)
      keys = sorted(k for k in validation_scores.keys() if k in set(x[0] for x in brands))
      truth = [validation_scores[k] for k in keys]
      result = {}
      outf.write('exemplar\tcorr\tn_followers\n')
      outf.flush()
      for exemplar in exemplars:
          single_exemplar = {exemplar: exemplars[exemplar]}
          social_scores = analyze_fn(brands, single_exemplar)
          predicted = [social_scores[k] for k in keys]
          outf.write('%s\t%g\t%d\n' % (exemplar, scistat.pearsonr(predicted, truth)[0], len(exemplars[exemplar])))
          outf.flush()
          result[exemplar] = scistat.pearsonr(predicted, truth)[0]
      outf.close()
      return result
docs: Report the overall correlation with the validation scores using each exemplar in isolation.