Update metric.py

metric.py

@@ -1,184 +1,3 @@
-# import re
-# import json
-# import evaluate
-# import datasets
-
-# _DESCRIPTION = """
-# Table evaluation metrics for assessing the matching degree between predicted and reference tables. It calculates the following metrics:
-
-# 1. Precision: The ratio of correctly predicted cells to the total number of cells in the predicted table
-# 2. Recall: The ratio of correctly predicted cells to the total number of cells in the reference table
-# 3. F1 Score: The harmonic mean of precision and recall
-
-# These metrics help evaluate the accuracy of table data extraction or generation.
-# """
-
-
-# _KWARGS_DESCRIPTION = """
-# Args:
-#     predictions (`str`): Predicted table in Markdown format.
-#     references (`str`): Reference table in Markdown format.
-
-# Returns:
-#     dict: A dictionary containing the following metrics:
-#         - precision (`float`): Precision score, range [0,1]
-#         - recall (`float`): Recall score, range [0,1]
-#         - f1 (`float`): F1 score, range [0,1]
-#         - true_positives (`int`): Number of correctly predicted cells
-#         - false_positives (`int`): Number of incorrectly predicted cells
-#         - false_negatives (`int`): Number of cells that were not predicted
-
-# Examples:
-#     >>> accuracy_metric = evaluate.load("accuracy")
-#     >>> results = accuracy_metric.compute(
-#     ...     predictions="|  | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 5 | 8 | 7 | 5 | 9 || wage | 1 | 5 | 3 | 8 | 5 |",
-#     ...     references="|  | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 1 | 6 | 7 | 5 | 9 || wage | 1 | 5 | 2 | 8 | 5 |"
-#     ... )
-#     >>> print(results)
-#     {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'true_positives': 7, 'false_positives': 3, 'false_negatives': 3}
-# """
-
-
-# _CITATION = """
-# @article{scikit-learn,
-#   title={Scikit-learn: Machine Learning in {P}ython},
-#   author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
-#          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
-#          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
-#          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
-#   journal={Journal of Machine Learning Research},
-#   volume={12},
-#   pages={2825--2830},
-#   year={2011}
-# }
-# """
-
-
-    
-# @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-# class Accuracy(evaluate.Metric):
-#     def _info(self):
-#         return evaluate.MetricInfo(
-#             description=_DESCRIPTION,
-#             citation=_CITATION,
-#             inputs_description=_KWARGS_DESCRIPTION,
-#             features=datasets.Features(
-#                 {
-#                     "predictions": datasets.Value("string"),
-#                     "references": datasets.Value("string"),
-#                 }
-#             ),
-#             reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"],
-#         )
-#     def _extract_markdown_table(self,text):
-#         text = text.replace('\n', '')
-#         text = text.replace(" ","")
-#         pattern = r'\|(?:[^|]+\|)+[^|]+\|'
-#         matches = re.findall(pattern, text)
-        
-#         if matches:
-#             return ''.join(matches)
-        
-#         return None
-    
-#     def _table_to_dict(self,table_str):
-#         result_dict = {}
-
-#         table_str = table_str.lstrip("|").rstrip("|")
-#         parts = table_str.split('||')
-#         parts = [part for part in parts if "--" not in part]
-#         legends = parts[0].split("|")
-
-#         rows = len(parts)
-#         if rows == 2:
-#             nums = parts[1].split("|")
-#             for i in range(len(nums)):
-#                 result_dict[legends[i]]=float(nums[i])
-#         elif rows >=3:
-#             for i in range(1,rows):
-#                 pre_row = parts[i]
-#                 pre_row = pre_row.split("|")
-#                 label = pre_row[0]
-#                 result_dict[label] = {}
-#                 for j in range(1,len(pre_row)):
-#                     result_dict[label][legends[j-1]] = float(pre_row[j])
-#         else:
-#             return None
-        
-#         return result_dict
-#     def _markdown_to_dict(self,markdown_str):
-#         table_str = self._extract_markdown_table(markdown_str)
-#         if table_str:
-#             return self._table_to_dict(table_str)
-#         else:
-#             return None
-    
-#     def _calculate_table_metrics(self,pred_table, true_table):
-#         true_positives = 0
-#         false_positives = 0
-#         false_negatives = 0
-
-#         # 遍历预测表格的所有键值对
-#         for key, pred_value in pred_table.items():
-#             if key in true_table:
-#                 true_value = true_table[key]
-#                 if isinstance(pred_value, dict) and isinstance(true_value, dict):
-#                     nested_metrics = self._calculate_table_metrics(pred_value, true_value)
-#                     true_positives += nested_metrics['true_positives']
-#                     false_positives += nested_metrics['false_positives']
-#                     false_negatives += nested_metrics['false_negatives']
-#                 # 如果值相等
-#                 elif pred_value == true_value:
-#                     true_positives += 1
-#                 else:
-#                     false_positives += 1
-#                     false_negatives += 1
-#             else:
-#                 false_positives += 1
-
-#         # 计算未匹配的真实值
-#         for key in true_table:
-#             if key not in pred_table:
-#                 false_negatives += 1
-
-#         # 计算指标
-#         precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
-#         recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
-#         f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
-
-#         return {
-#             'precision': precision,
-#             'recall': recall,
-#             'f1': f1,
-#             'true_positives': true_positives,
-#             'false_positives': false_positives,
-#             'false_negatives': false_negatives
-#         }
-
-#     def _compute(self, predictions, references):
-#         predictions = "".join(predictions)
-#         references = "".join(references)
-#         return self._calculate_table_metrics(self._markdown_to_dict(predictions), self._markdown_to_dict(references))
-    
-
-# def main():
-#     accuracy_metric = Accuracy()
-
-#     # 计算指标
-#     results = accuracy_metric.compute(
-#         predictions=["""
-# |  | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 5 | 8 | 7 | 5 | 9 || wage | 1 | 5 | 3 | 8 | 5 |
-# """],  # 预测的表格
-#         references=["""
-# |  | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 1 | 6 | 7 | 5 | 9 || wage | 1 | 5 | 2 | 8 | 5 |
-# """],   # 参考的表格
-#     )
-#     print(results)  # 输出结果
-
-# if __name__ == '__main__':
-#     main()
-
-
 import re
 import json
 import evaluate