code: def get_classification_result(hyp_file, ref_file):
      hyp_lines = [line for line in hyp_file]
      ref_lines = [line for line in ref_file]
      hyp_list = []
      ref_list = []
      for line_count in range(len(hyp_lines)):
          hyp_list.append(hyp_lines[line_count].split(" ")[0])
          ref_list.append(ref_lines[line_count].split(" ")[0])
      macro_f1 = f1_score(
          ref_list,
          hyp_list,
          average="macro",
          labels=["Positive", "Neutral", "Negative"],
      )
      weighted_f1 = f1_score(
          ref_list,
          hyp_list,
          average="weighted",
          labels=["Positive", "Neutral", "Negative"],
      )
      micro_f1 = f1_score(
          ref_list,
          hyp_list,
          average="micro",
          labels=["Positive", "Neutral", "Negative"],
      )
      return macro_f1, weighted_f1, micro_f1
  
imports: import argparse
  import os
  import re
  import sys
  import pandas
  from sklearn.metrics import f1_score
label_desc: Use image node_id 1 for calling a global function with example usage: get_classification_result(hyp_file, ref_file) and returns: macro_f1,  weighted_f1,  micro_f1