(improve)(benchmark) improve benchmark, add analysis of parsing results (#2215)

.gitignore

@@ -21,3 +21,4 @@ __pycache__/
 /dict
 assembly/build/*-SNAPSHOT
 **/node_modules/
+benchmark/res/

benchmark/benchmark.py

@@ -15,6 +15,68 @@ import requests
 import time
 import jwt
 import traceback
+import os
+from datetime import datetime
+
+
+class DataFrameAppender:
+    def __init__(self,file_name = "output"):
+        # 定义表头
+        columns = ['问题', '解析状态', '解析耗时', '执行状态', '执行耗时', '总耗时']
+        # 创建只有表头的 DataFrame
+        self.df = pd.DataFrame(columns=columns)
+        self.file_name = file_name
+
+    def append_data(self, new_data):
+        # 假设 new_data 是一维数组，将其转换为字典
+        columns = ['问题', '解析状态', '解析耗时', '执行状态', '执行耗时', '总耗时']
+        new_dict = dict(zip(columns, new_data))
+        # 使用 loc 方法追加数据
+        self.df.loc[len(self.df)] = new_dict
+    def print_analysis_result(self):
+        # 测试样例总数
+        total_samples = len(self.df)
+
+        # 解析成功数量
+        parse_success_count = (self.df['解析状态'] == '解析成功').sum()
+
+        # 执行成功数量
+        execute_success_count = (self.df['执行状态'] == '执行成功').sum()
+
+        # 解析平均耗时，保留两位小数
+        avg_parse_time = round(self.df['解析耗时'].mean(), 2)
+
+        # 执行平均耗时，保留两位小数
+        avg_execute_time = round(self.df['执行耗时'].mean(), 2)
+
+        # 总平均耗时，保留两位小数
+        avg_total_time = round(self.df['总耗时'].mean(), 2)
+
+        # 最长耗时，保留两位小数
+        max_time = round(self.df['总耗时'].max(), 2)
+
+        # 最短耗时，保留两位小数
+        min_time = round(self.df['总耗时'].min(), 2)
+
+        print(f"测试样例总数 : {total_samples}")
+        print(f"解析成功数量 : {parse_success_count}")
+        print(f"执行成功数量 : {execute_success_count}")
+        print(f"解析平均耗时 : {avg_parse_time} 秒")
+        print(f"执行平均耗时 : {avg_execute_time} 秒")
+        print(f"总平均耗时 : {avg_total_time} 秒")
+        print(f"最长耗时 : {max_time} 秒")
+        print(f"最短耗时 : {min_time} 秒")
+
+    def write_to_csv(self):
+        # 检查 data 文件夹是否存在，如果不存在则创建
+        if not os.path.exists('res'):
+            os.makedirs('res')
+        # 获取当前时间戳
+        timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
+        # 生成带时间戳的文件名
+        file_path = os.path.join('res', f'{self.file_name}_{timestamp}.csv')
+        self.df.to_csv(file_path, index=False)
+        print(f"测试结果已保存到 {file_path}")
 
 class BatchTest:
     def __init__(self, url, agentId, chatId, userName):
@@ -70,18 +132,35 @@ class BatchTest:
 def benchmark(url:str, agentId:str, chatId:str, filePath:str, userName:str):
     batch_test = BatchTest(url, agentId, chatId, userName)
     df = batch_test.read_question_from_csv(filePath)
+    appender = DataFrameAppender(os.path.basename(filePath))
     for index, row in df.iterrows():
         question = row['question']
         print('start to ask question:', question)
         # 捕获异常，防止程序中断
         try:
             parse_resp = batch_test.parse(question)
-            batch_test.execute(agentId, question, parse_resp['data']['queryId'])
+            parse_status = '解析失败'
+            if parse_resp.get('data').get('errorMsg') is None:
+                parse_status = '解析成功'
+            parse_cost = parse_resp.get('data').get('parseTimeCost').get('parseTime')
+            execute_resp = batch_test.execute(agentId, question, parse_resp['data']['queryId'])
+            execute_status = '执行失败'
+            execute_cost = 0
+            if parse_status == '解析成功' and execute_resp.get('data').get('errorMsg') is None:
+                execute_status = '执行成功'
+                execute_cost = execute_resp.get('data').get('queryTimeCost')
+            res = [question.replace(',', '#'),parse_status,parse_cost/1000,execute_status,execute_cost/1000,(parse_cost+execute_cost)/1000]
+            appender.append_data(res)
+
         except Exception as e:
             print('error:', e)
             traceback.print_exc()
             continue
         time.sleep(1)
+    # 打印分析结果
+    appender.print_analysis_result()
+    # 分析明细输出
+    appender.write_to_csv()
 
 if __name__ == '__main__':