maqiuping59 commited on
Commit
56dcd48
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1 Parent(s): a37d1ca

Update metric.py

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Files changed (1) hide show
  1. metric.py +6 -14
metric.py CHANGED
@@ -40,14 +40,7 @@ Examples:
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  _CITATION = """
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- @article{ChineseChartExtractor,
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- title={Research on Chinese Chart Data Extraction Methods},
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- author={Qiuping Ma,Hangshuo Bi,Qi Zhang,Xiaofan Zhao},
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- journal={None},
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- volume={0},
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- pages={0--0},
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- year={2025}
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- }
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  """
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@@ -123,7 +116,6 @@ class Accuracy(evaluate.Metric):
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  true_positives += nested_metrics['true_positives']
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  false_positives += nested_metrics['false_positives']
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  false_negatives += nested_metrics['false_negatives']
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-
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  elif true_value == 0 and abs(pred_value) < 0.05:
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  true_positives += 1
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  elif true_value != 0 and abs((pred_value - true_value) / true_value) < 0.05:
@@ -133,11 +125,11 @@ class Accuracy(evaluate.Metric):
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  false_negatives += 1
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  else:
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  false_positives += 1
 
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  for key in true_table:
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  if key not in pred_table:
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  false_negatives += 1
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-
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  precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
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  recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
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  f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
@@ -160,16 +152,16 @@ class Accuracy(evaluate.Metric):
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  def main():
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  accuracy_metric = Accuracy()
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-
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  results = accuracy_metric.compute(
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  predictions=["""
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  | | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 5 | 8 | 7 | 5 | 9 || wage | 1 | 5 | 3 | 8 | 5 |
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- """],
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  references=["""
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  | | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 1 | 6 | 7 | 5 | 9 || wage | 1 | 5 | 2 | 8 | 5 |
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- """],
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  )
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- print(results)
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  if __name__ == '__main__':
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  main()
 
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  _CITATION = """
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+
 
 
 
 
 
 
 
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  """
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  true_positives += nested_metrics['true_positives']
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  false_positives += nested_metrics['false_positives']
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  false_negatives += nested_metrics['false_negatives']
 
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  elif true_value == 0 and abs(pred_value) < 0.05:
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  true_positives += 1
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  elif true_value != 0 and abs((pred_value - true_value) / true_value) < 0.05:
 
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  false_negatives += 1
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  else:
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  false_positives += 1
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+
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  for key in true_table:
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  if key not in pred_table:
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  false_negatives += 1
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  precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
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  recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
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  f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
 
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  def main():
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  accuracy_metric = Accuracy()
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+ # 计算指标
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  results = accuracy_metric.compute(
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  predictions=["""
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  | | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 5 | 8 | 7 | 5 | 9 || wage | 1 | 5 | 3 | 8 | 5 |
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+ """], # 预测的表格
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  references=["""
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  | | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 1 | 6 | 7 | 5 | 9 || wage | 1 | 5 | 2 | 8 | 5 |
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+ """], # 参考的表格
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  )
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+ print(results) # 输出结果
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  if __name__ == '__main__':
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  main()