fix(experiment): 🐛 Update repeat_id to run_id for consistency in experiment logging

abses/core/experiment.py

@@ -122,10 +122,10 @@ def run_single(
         hooks:
             The hooks to run after the model is run.
     """
-    job_id, repeat_id = key
+    job_id, run_id = key
     model = model_cls(
         parameters=cfg,
-        run_id=repeat_id,
+        run_id=run_id,
         seed=seed,
         **kwargs,
     )
@@ -135,7 +135,7 @@ def run_single(
         for hook_name, hook_func in hooks.items():
             logger.info(f"Running hook {hook_name}.")
             _call_hook_with_optional_args(
-                hook_func, model, job_id=job_id, repeat_id=repeat_id
+                hook_func, model, job_id=job_id, run_id=run_id
             )
     return key, seed, results
 
@@ -370,45 +370,17 @@ def _load_hydra_cfg(
 
         return cfg
 
-    # def _get_logging_mode(self, repeat_id: Optional[int] = None) -> str | bool:
-    #     log_mode = self.exp_config.get("logging", "once")
-    #     if log_mode == "once":
-    #         if repeat_id == 1:
-    #             logging: bool | str = self.name
-    #         else:
-    #             return False
-    #     elif bool(log_mode):
-    #         logging = f"{self.name}_{repeat_id}"
-    #     else:
-    #         logging = False
-    #     return logging
-
-    # def _update_log_config(
-    #     self, config, repeat_id: Optional[int] = None
-    # ) -> bool:
-    #     """Update the log configuration."""
-    #     if isinstance(config, dict):
-    #         config = DictConfig(config)
-    #     OmegaConf.set_struct(config, False)
-    #     log_name = self._get_logging_mode(repeat_id=repeat_id)
-    #     if not log_name:
-    #         config["log"] = False
-    #         return config
-    #     logging_cfg = OmegaConf.create({"log": {"name": log_name}})
-    #     config = OmegaConf.merge(config, logging_cfg)
-    #     return config
-
-    def _get_seed(self, repeat_id: int, job_id: Optional[int] = None) -> Optional[int]:
+    def _get_seed(self, run_id: int, job_id: Optional[int] = None) -> Optional[int]:
         """获取每次运行的随机种子
 
         使用基础种子初始化随机数生成器，为每次运行生成唯一的随机种子。
         这样可以保证：
         1. 如果基础种子相同，生成的种子序列也相同
-        2. 不同的 job_id 和 repeat_id 组合会得到不同的种子
+        2. 不同的 job_id 和 run_id 组合会得到不同的种子
         3. 种子序列具有更好的随机性
 
         Args:
-            repeat_id: 重复实验的ID
+            run_id: 重复实验的ID
 
         Returns:
             如果没有设置基础种子则返回 None，否则返回生成的随机种子
@@ -419,7 +391,7 @@ def _get_seed(self, repeat_id: int, job_id: Optional[int] = None) -> Optional[in
         if job_id is None:
             job_id = self.job_id
         # 使用基础种子和 job_id 创建随机数生成器
-        r = random.Random(self._base_seed + job_id * 1000 + repeat_id)
+        r = random.Random(self._base_seed + job_id * 1000 + run_id)
         return r.randrange(2**32)
 
     def _get_logging_mode(self) -> str:
@@ -431,13 +403,13 @@ def _get_logging_mode(self) -> str:
         return get_log_mode(self._cfg)
 
     def _get_log_file_path(
-        self, log_name: str, repeat_id: int, logging_mode: str
+        self, log_name: str, run_id: int, logging_mode: str
     ) -> Optional[Path]:
         """Get log file path for a specific repeat.
 
         Args:
             log_name: Base log file name.
-            repeat_id: Repeat ID (1-indexed).
+            run_id: Repeat ID (1-indexed).
             logging_mode: Logging mode.
 
         Returns:
@@ -449,7 +421,7 @@ def _get_log_file_path(
             outpath=self.outpath,
             log_name=log_name,
             logging_mode=logging_mode,
-            repeat_id=repeat_id,
+            run_id=run_id,
         )
 
     def _log_experiment_info(
@@ -518,18 +490,18 @@ def _batch_run_repeats(
         if self._is_hydra_parallel() or number_process == 1:
             # Hydra 并行或指定单进程时，顺序执行
             disable = repeats == 1 or not display_progress
-            for repeat_id in tqdm(
+            for run_id in tqdm(
                 range(1, repeats + 1),
                 disable=disable,
                 desc=f"Job {self.job_id} repeats {repeats} times.",
             ):
                 # Log separator for merge mode
-                if logging_mode == "merge" and repeat_id > 1:
+                if logging_mode == "merge" and run_id > 1:
                     # Note: Separator will be logged in model setup
                     pass
 
                 # Get log file path for this repeat
-                log_path = self._get_log_file_path(log_name, repeat_id, logging_mode)
+                log_path = self._get_log_file_path(log_name, run_id, logging_mode)
 
                 # Display log file location for separate mode
                 # This should only go to stdout, not to model run log files
@@ -539,14 +511,14 @@ def _batch_run_repeats(
                     and log_path is not None
                 ):
                     # Use print instead of logger to avoid writing to model run log files
-                    print(f"Repeat {repeat_id}: Logging to {log_path}")
+                    print(f"Repeat {run_id}: Logging to {log_path}")
 
                 run_single(
                     model_cls=self.model_cls,
                     cfg=cfg,
-                    key=(self.job_id, repeat_id),
+                    key=(self.job_id, run_id),
                     outpath=self.outpath,
-                    seed=self._get_seed(repeat_id),
+                    seed=self._get_seed(run_id),
                     hooks=self._manager.hooks,
                     **self._extra_kwargs,
                 )
@@ -564,13 +536,13 @@ def _batch_run_repeats(
                 delayed(run_single)(
                     model_cls=self.model_cls,
                     cfg=cfg,
-                    key=(self.job_id, repeat_id),
+                    key=(self.job_id, run_id),
                     outpath=self.outpath,
-                    seed=self._get_seed(repeat_id),
+                    seed=self._get_seed(run_id),
                     hooks=self._manager.hooks,
                     **self._extra_kwargs,
                 )
-                for repeat_id in tqdm(
+                for run_id in tqdm(
                     range(1, repeats + 1),
                     disable=not display_progress,
                     desc=f"Job {self.job_id} repeats {repeats} times, with {number_process} processes.",
@@ -644,22 +616,22 @@ def _call_hook_with_optional_args(
     hook_func: Callable,
     model: MainModelProtocol,
     job_id: Optional[int] = None,
-    repeat_id: Optional[int] = None,
+    run_id: Optional[int] = None,
 ) -> Any:
     """根据钩子函数的参数签名动态调用函数
 
     Args:
         hook_func: 要调用的钩子函数
         model: 模型实例
         job_id: 可选的任务ID
-        repeat_id: 可选的重复实验ID
+        run_id: 可选的重复实验ID
     """
     sig = inspect.signature(hook_func)
     hook_args = {}
 
     if "job_id" in sig.parameters:
         hook_args["job_id"] = job_id
-    if "repeat_id" in sig.parameters:
-        hook_args["repeat_id"] = repeat_id
+    if "run_id" in sig.parameters:
+        hook_args["run_id"] = run_id
 
     return hook_func(model, **hook_args)

abses/core/job_manager.py

@@ -7,6 +7,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type
 
 import pandas as pd
@@ -102,7 +103,7 @@ def update_result(
         """更新实验结果
 
         Args:
-            key: (job_id, repeat_id) tuple
+            key: (job_id, run_id) tuple
             overrides: Configuration overrides for this run
             datasets: Row-like mapping of metrics/values to store
             seed: Random seed used for this run
@@ -115,25 +116,44 @@ def dict_to_df(self, results: dict) -> pd.DataFrame:
         """将嵌套字典转换为 DataFrame
 
         Args:
-            results: 形如 {(job_id, repeat_id): {'metric': value}} 的字典
+            results: 形如 {(job_id, run_id): {'metric': value}} 的字典
 
         Returns:
-        包含 job_id, repeat_id 和指标值的 DataFrame
+        包含 job_id, run_id 和指标值的 DataFrame
         """
         return pd.DataFrame(results.values(), index=self.index)
 
     def get_datasets(
         self,
         seed: bool = True,
     ) -> pd.DataFrame:
-        """获取所有实验结果的 DataFrame"""
+        """获取所有实验结果的 DataFrame
+
+        Note:
+            The ``repeat_id`` column is **deprecated** and will be removed in a
+            future version. Please use the ``run_id`` column instead.
+        """
         to_concat = []
         to_concat.append(self.dict_to_df(self._overrides))
         if seed:
             seed = pd.Series(self._seeds, name="seed", index=self.index)
             to_concat.append(seed)
         to_concat.append(self.dict_to_df(self._datasets))
-        return pd.concat(to_concat, axis=1).reset_index()
+        df = pd.concat(to_concat, axis=1).reset_index()
+
+        # Backward compatibility: if legacy results contain a `repeat_id` column
+        # (e.g. from older versions or custom datasets), mirror it into `run_id`
+        # and emit a deprecation warning. New code should only rely on `run_id`.
+        if "repeat_id" in df.columns and "run_id" not in df.columns:
+            warnings.warn(
+                "Column 'repeat_id' is deprecated and will be removed in a future "
+                "version. Please use 'run_id' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            df["run_id"] = df["repeat_id"]
+
+        return df
 
     def add_a_hook(
         self,

abses/core/model.py

@@ -140,7 +140,9 @@ def __init__(
         tracker_backend = create_tracker(tracker_cfg, model=self)
         collector_cfg = prepare_collector_config(tracker_cfg)
         self.datacollector: ABSESpyDataCollector = ABSESpyDataCollector(
-            reports=collector_cfg, tracker=tracker_backend
+            reports=collector_cfg,
+            tracker=tracker_backend,
+            run_id=run_id,
         )
 
         # Setup logging BEFORE initialize() so user logs in initialize() are captured
@@ -347,7 +349,7 @@ def _setup_logger(self, log_cfg: Dict[str, Any]) -> None:
             rotation=rotation,
             retention=retention,
             logging_mode=logging_mode,
-            repeat_id=self.run_id,
+            run_id=self.run_id,
             file_level=file_level,
             file_format=file_format,
             file_datefmt=file_datefmt,

abses/utils/datacollector.py

@@ -116,15 +116,18 @@ def __init__(
         self,
         reports: Dict[ReportType, Dict[str, Reporter]] | None = None,
         tracker: Optional[TrackerProtocol] = None,
+        run_id: Optional[int] = None,
     ):
         """Initialize data collector.
 
         Args:
             reports: Reporters configuration.
             tracker: Optional tracker backend.
+            run_id: Optional run id.
         """
         reports = reports or {}
         self.tracker = tracker
+        self.run_id = run_id
         self.model_reporters: Dict[str, Reporter] = {}
         self.final_reporters: Dict[str, Reporter] = {}
         self.agent_reporters: Dict[str, Dict[str, Reporter]] = {}
@@ -162,6 +165,13 @@ def add_reporters(
         for name, reporter in reporters.items():
             self._new_agent_reporter(breed=item, name=name, reporter=reporter)
 
+    def _add_run_id_to_data(
+        self, data: pd.DataFrame | Dict[str, Any]
+    ) -> pd.DataFrame | Dict[str, Any]:
+        if self.run_id is not None:
+            data["run_id"] = self.run_id
+        return data
+
     def _new_model_reporter(self, name: str, reporter: Reporter) -> None:
         """Add a new model-level reporter to collect data.
 
@@ -216,8 +226,9 @@ def get_model_vars_dataframe(self):
             logger.warning(
                 "No model reporters have been definedreturning empty DataFrame."
             )
-
-        return pd.DataFrame(self.model_vars)
+        df = pd.DataFrame(self.model_vars)
+        df = self._add_run_id_to_data(df)
+        return df
 
     def get_agent_vars_dataframe(self, breed: Optional[str] = None) -> pd.DataFrame:
         """获取某种 Agents 的 DataFrame"""
@@ -229,8 +240,12 @@ def get_agent_vars_dataframe(self, breed: Optional[str] = None) -> pd.DataFrame:
         if not self.agent_reporters:
             logger.warning("No agent reporters have been defined in the DataCollector.")
         if results := self._agent_records.get(breed):
-            return pd.concat([pd.DataFrame(res) for res in results])
-        return pd.DataFrame()
+            df = pd.concat([pd.DataFrame(res) for res in results])
+        else:
+            logger.warning(f"No agent records found for breed {breed}.")
+            df = pd.DataFrame()
+        df = self._add_run_id_to_data(data=df)
+        return df
 
     def get_final_vars_report(self, model: MainModel) -> Dict[str, Any]:
         """Report at the end of this model.
@@ -239,11 +254,10 @@ def get_final_vars_report(self, model: MainModel) -> Dict[str, Any]:
             A dictionary mapping variable names to their computed values.
         """
         if not self.final_reporters:
-            logger.warning(
-                "No final reporters have been defined, returning empty dict."
-            )
+            logger.info("No final reporters have been defined.")
             return {}
         results = {var: func(model) for var, func in self.final_reporters.items()}
+        self._add_run_id_to_data(results)
         if self.tracker is not None:
             self.tracker.log_final_metrics(results)
         return results

abses/utils/log_config.py

@@ -229,15 +229,15 @@ def determine_log_file_path(
     outpath: Optional[Path],
     log_name: str,
     logging_mode: str = "once",
-    repeat_id: Optional[int] = None,
+    run_id: Optional[int] = None,
 ) -> Optional[Path]:
     """Determine log file path based on logging mode.
 
     Args:
         outpath: Output directory for log files.
         log_name: Base log file name (without extension).
         logging_mode: Logging mode - 'once', 'separate', or 'merge'.
-        repeat_id: Repeat ID for the current run (1-indexed).
+        run_id: Run ID for the current run (1-indexed).
 
     Returns:
         Path to log file, or None if logging should be disabled.
@@ -250,21 +250,21 @@ def determine_log_file_path(
 
     if logging_mode == "once":
         # Only log the first repeat
-        if repeat_id is None or repeat_id == 1:
+        if run_id is None or run_id == 1:
             return outpath / f"{log_name}.log"
         return None
     elif logging_mode == "separate":
         # Each repeat gets its own file
-        # In separate mode, repeat_id must be provided
-        if repeat_id is None:
+        # In separate mode, run_id must be provided
+        if run_id is None:
             return None  # Don't create default file in separate mode
-        return outpath / f"{log_name}_{repeat_id}.log"
+        return outpath / f"{log_name}_{run_id}.log"
     elif logging_mode == "merge":
         # All repeats go to the same file
         return outpath / f"{log_name}.log"
     else:
         # Unknown mode, default to once behavior
-        if repeat_id is None or repeat_id == 1:
+        if run_id is None or run_id == 1:
             return outpath / f"{log_name}.log"
         return None