44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
import argparse
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class Evaluator:
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"""A simple evaluator for exact match."""
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def __init__(self):
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pass
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def eval_exact_match(self, pred, label):
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# 比较预测的SQL和标准SQL是否完全相同
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return pred == label
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def evaluate(gold, predict, db):
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with open(gold) as f:
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gold_sqls = [line.strip() for line in f.readlines()]
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with open(predict) as f:
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predict_sqls = [line.strip() for line in f.readlines()]
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assert len(gold_sqls) == len(predict_sqls), "The number of gold and predict SQLs must be equal"
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evaluator = Evaluator()
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exact_matches = []
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for g_sql, p_sql in zip(gold_sqls, predict_sqls):
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# gStructured = get_sql(schema, g_sql)
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# pStructured = get_sql(schema, p_sql) # getsql主要功能是获取表格的别名，但是我们的数据里没有，所以不需要这个步骤
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exact_match = evaluator.eval_exact_match(p_sql, g_sql)
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exact_matches.append(exact_match)
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# 输出精确匹配的结果
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exact_match_rate = sum(exact_matches) / len(exact_matches)
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print(f"Exact Match Rate: {exact_match_rate:.2f} ({sum(exact_matches)}/{len(exact_matches)})")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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gold_path = "/home/zhangxj/WorkFile/LCA-GPT/LLM-SQL/data/tb_process/gold.txt"
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pred_path = "/home/zhangxj/WorkFile/LCA-GPT/LLM-SQL/data/tb_process/base_pred.txt"
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db_path = "/home/zhangxj/WorkFile/Text2Sql/data/spider_data/database/academic/academic.sqlite"
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parser.add_argument('--gold', dest='gold', type=str, help="the path to the gold sql queries", default = gold_path)
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parser.add_argument('--pred', dest='pred', type=str, help="the path to the predicted sql queries", default = pred_path)
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parser.add_argument('--db', dest='db', type=str, help="the path to the database", default=db_path)
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args = parser.parse_args()
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evaluate(args.gold, args.pred, args.db) |