pre-commit format

data/inference/make_datasets/utils.py

@@ -1,5 +1,4 @@
 import re
-import re
 
 
 def extract_diff(response):

data/inference/run.py

@@ -8,11 +8,9 @@
 
 try:
     # 设置你想要传递给脚本的命令行参数
-    sys.argv = ['run_api.py', '--dataset_name_or_path', 'princeton-nlp/SWE-bench_oracle', '--output_dir',
-                './outputs']
+    sys.argv = ["run_api.py", "--dataset_name_or_path", "princeton-nlp/SWE-bench_oracle", "--output_dir", "./outputs"]
     # 执行脚本
-    runpy.run_path(path_name='run_api.py', run_name='__main__')
+    runpy.run_path(path_name="run_api.py", run_name="__main__")
 finally:
     # 恢复原始的sys.argv以避免对后续代码的潜在影响
     sys.argv = original_argv
-

data/inference/run_api.py

@@ -10,12 +10,12 @@
 from tenacity import retry, stop_after_attempt, wait_random_exponential
 from tqdm.auto import tqdm
 
+from data.inference.const import SCIKIT_LEARN_IDS
 from metagpt.config2 import config
 from metagpt.logs import logger
 from metagpt.roles.di.data_interpreter import DataInterpreter
 from metagpt.utils import count_string_tokens
 from metagpt.utils.recovery_util import save_history
-from data.inference.const import SCIKIT_LEARN_IDS
 
 # Replace with your own
 MAX_TOKEN = 128000
@@ -71,7 +71,7 @@ async def openai_inference(
         for datum in tqdm(test_dataset, desc=f"Inference for {model_name_or_path}"):
             di = DataInterpreter(use_reflection=use_reflection)
             instance_id = datum["instance_id"]
-            
+
             if instance_id in existing_ids:
                 continue
             output_dict = {"instance_id": instance_id}

metagpt/roles/di/data_interpreter.py

@@ -43,66 +43,66 @@ class DataInterpreter(Role):
     tool_recommender: ToolRecommender = None
     react_mode: Literal["plan_and_act", "react"] = "plan_and_act"
     max_react_loop: int = 10  # used for react mode
-    
+
     @model_validator(mode="after")
     def set_plan_and_tool(self) -> "Interpreter":
         self._set_react_mode(react_mode=self.react_mode, max_react_loop=self.max_react_loop, auto_run=self.auto_run)
         self.use_plan = (
-                self.react_mode == "plan_and_act"
+            self.react_mode == "plan_and_act"
         )  # create a flag for convenience, overwrite any passed-in value
         if self.tools:
             self.tool_recommender = BM25ToolRecommender(tools=self.tools)
         self.set_actions([WriteAnalysisCode])
         self._set_state(0)
         return self
-    
+
     @property
     def working_memory(self):
         return self.rc.working_memory
-    
+
     async def _think(self) -> bool:
         """Useful in 'react' mode. Use LLM to decide whether and what to do next."""
         user_requirement = self.get_memories()[0].content
         context = self.working_memory.get()
-        
+
         if not context:
             # just started the run, we need action certainly
             self.working_memory.add(self.get_memories()[0])  # add user requirement to working memory
             self._set_state(0)
             return True
-        
+
         prompt = REACT_THINK_PROMPT.format(user_requirement=user_requirement, context=context)
         rsp = await self.llm.aask(prompt)
         rsp_dict = json.loads(CodeParser.parse_code(block=None, text=rsp))
         self.working_memory.add(Message(content=rsp_dict["thoughts"], role="assistant"))
         need_action = rsp_dict["state"]
         self._set_state(0) if need_action else self._set_state(-1)
-        
+
         return need_action
-    
+
     async def _act(self) -> Message:
         """Useful in 'react' mode. Return a Message conforming to Role._act interface."""
         code, _, _ = await self._write_and_exec_code()
         return Message(content=code, role="assistant", cause_by=WriteAnalysisCode)
-    
+
     async def _plan_and_act(self) -> Message:
         rsp = await super()._plan_and_act()
         await self.execute_code.terminate()
         return rsp
-    
+
     async def _act_on_task(self, current_task: Task) -> TaskResult:
         """Useful in 'plan_and_act' mode. Wrap the output in a TaskResult for review and confirmation."""
         code, result, is_success = await self._write_and_exec_code()
         task_result = TaskResult(code=code, result=result, is_success=is_success)
         return task_result
-    
+
     async def _write_and_exec_code(self, max_retry: int = 3):
         counter = 0
         success = False
-        
+
         # plan info
         plan_status = self.planner.get_plan_status() if self.use_plan else ""
-        
+
         # tool info
         if self.tools:
             context = (
@@ -112,66 +112,68 @@ async def _write_and_exec_code(self, max_retry: int = 3):
             tool_info = await self.tool_recommender.get_recommended_tool_info(context=context, plan=plan)
         else:
             tool_info = ""
-        
+
         # data info
         await self._check_data()
-        
+
         while not success and counter < max_retry:
             ### write code ###
             code, cause_by = await self._write_code(counter, plan_status, tool_info)
-            
+
             self.working_memory.add(Message(content=code, role="assistant", cause_by=cause_by))
-            
+
             ### execute code ###
-            import pdb;pdb.set_trace()
+            import pdb
+
+            pdb.set_trace()
             result, success = await self.execute_code.run(code)
             print(result)
-            
+
             self.working_memory.add(Message(content=result, role="user", cause_by=ExecuteNbCode))
-            
+
             ### process execution result ###
             counter += 1
-            
+
             if not success and counter >= max_retry:
                 logger.info("coding failed!")
                 review, _ = await self.planner.ask_review(auto_run=False, trigger=ReviewConst.CODE_REVIEW_TRIGGER)
                 if ReviewConst.CHANGE_WORDS[0] in review:
                     counter = 0  # redo the task again with help of human suggestions
-        
+
         return code, result, success
-    
+
     async def _write_code(
-            self,
-            counter: int,
-            plan_status: str = "",
-            tool_info: str = "",
+        self,
+        counter: int,
+        plan_status: str = "",
+        tool_info: str = "",
     ):
         todo = self.rc.todo  # todo is WriteAnalysisCode
         logger.info(f"ready to {todo.name}")
         use_reflection = counter > 0 and self.use_reflection  # only use reflection after the first trial
-        
+
         user_requirement = self.get_memories()[0].content
-        
+
         code = await todo.run(
             user_requirement=user_requirement,
             plan_status=plan_status,
             tool_info=tool_info,
             working_memory=self.working_memory.get(),
             use_reflection=use_reflection,
         )
-        
+
         return code, todo
-    
+
     async def _check_data(self):
         if (
-                not self.use_plan
-                or not self.planner.plan.get_finished_tasks()
-                or self.planner.plan.current_task.task_type
-                not in [
-            TaskType.DATA_PREPROCESS.type_name,
-            TaskType.FEATURE_ENGINEERING.type_name,
-            TaskType.MODEL_TRAIN.type_name,
-        ]
+            not self.use_plan
+            or not self.planner.plan.get_finished_tasks()
+            or self.planner.plan.current_task.task_type
+            not in [
+                TaskType.DATA_PREPROCESS.type_name,
+                TaskType.FEATURE_ENGINEERING.type_name,
+                TaskType.MODEL_TRAIN.type_name,
+            ]
         ):
             return
         logger.info("Check updated data")