Enhance Task Dict Var

examples/model_rolling/task_manager_rolling.py

@@ -17,7 +17,7 @@
 from qlib.workflow.task.manage import TaskManager, run_task
 from qlib.workflow.task.collect import RecorderCollector
 from qlib.model.ens.group import RollingGroup
-from qlib.model.trainer import TrainerRM, task_train
+from qlib.model.trainer import TrainerR, TrainerRM, task_train
 from qlib.tests.config import CSI100_RECORD_LGB_TASK_CONFIG, CSI100_RECORD_XGBOOST_TASK_CONFIG
 
 
@@ -29,7 +29,7 @@ def __init__(
         task_url="mongodb://10.0.0.4:27017/",
         task_db_name="rolling_db",
         experiment_name="rolling_exp",
-        task_pool="rolling_task",
+        task_pool=None,  # if user want to  "rolling_task"
         task_config=None,
         rolling_step=550,
         rolling_type=RollingGen.ROLL_SD,
@@ -43,14 +43,19 @@ def __init__(
         }
         qlib.init(provider_uri=provider_uri, region=region, mongo=mongo_conf)
         self.experiment_name = experiment_name
-        self.task_pool = task_pool
+        if task_pool is None:
+            self.trainer = TrainerR(experiment_name=self.experiment_name)
+        else:
+            self.task_pool = task_pool
+            self.trainer = TrainerRM(self.experiment_name, self.task_pool)
         self.task_config = task_config
         self.rolling_gen = RollingGen(step=rolling_step, rtype=rolling_type)
 
     # Reset all things to the first status, be careful to save important data
     def reset(self):
         print("========== reset ==========")
-        TaskManager(task_pool=self.task_pool).remove()
+        if isinstance(self.trainer, TrainerRM):
+            TaskManager(task_pool=self.task_pool).remove()
         exp = R.get_exp(experiment_name=self.experiment_name)
         for rid in exp.list_recorders():
             exp.delete_recorder(rid)
@@ -66,10 +71,10 @@ def task_generating(self):
 
     def task_training(self, tasks):
         print("========== task_training ==========")
-        trainer = TrainerRM(self.experiment_name, self.task_pool)
-        trainer.train(tasks)
+        self.trainer.train(tasks)
 
     def worker(self):
+        # NOTE: this is only used for TrainerRM
         # train tasks by other progress or machines for multiprocessing. It is same as TrainerRM.worker.
         print("========== worker ==========")
         run_task(task_train, self.task_pool, experiment_name=self.experiment_name)

qlib/model/trainer.py

@@ -86,10 +86,61 @@ def begin_task_train(task_config: dict, experiment_name: str, recorder_name: str
         return R.get_recorder()
 
 
+def get_item_from_obj(config: dict, name_path: str) -> object:
+    """
+    Follow the name_path to get values from config
+    For example:
+    If we follow the example in in the Parameters section,
+        Timestamp('2008-01-02 00:00:00') will be returned
+
+    Parameters
+    ----------
+    config : dict
+        e.g.
+        {'dataset': {'class': 'DatasetH',
+          'kwargs': {'handler': {'class': 'Alpha158',
+                                 'kwargs': {'end_time': '2020-08-01',
+                                            'fit_end_time': '<dataset.kwargs.segments.train.1>',
+                                            'fit_start_time': '<dataset.kwargs.segments.train.0>',
+                                            'instruments': 'csi100',
+                                            'start_time': '2008-01-01'},
+                                 'module_path': 'qlib.contrib.data.handler'},
+                     'segments': {'test': (Timestamp('2017-01-03 00:00:00'),
+                                           Timestamp('2019-04-08 00:00:00')),
+                                  'train': (Timestamp('2008-01-02 00:00:00'),
+                                            Timestamp('2014-12-31 00:00:00')),
+                                  'valid': (Timestamp('2015-01-05 00:00:00'),
+                                            Timestamp('2016-12-30 00:00:00'))}}
+        }}
+    name_path : str
+        e.g.
+        "dataset.kwargs.segments.train.1"
+
+    Returns
+    -------
+    object
+        the retrieved object
+    """
+    cur_cfg = config
+    for k in name_path.split("."):
+        if isinstance(cur_cfg, dict):
+            cur_cfg = cur_cfg[k]
+        elif k.isdigit():
+            cur_cfg = cur_cfg[int(k)]
+        else:
+            raise ValueError(f"Error when getting {k} from cur_cfg")
+    return cur_cfg
+
+
 def fill_placeholder(config: dict, config_extend: dict):
     """
     Detect placeholder in config and fill them with config_extend.
     The item of dict must be single item(int, str, etc), dict and list. Tuples are not supported.
+    There are two type of variables:
+    - user-defined variables :
+        e.g. when config_extend is `{"<MODEL>": model, "<DATASET>": dataset}`, "<MODEL>" and "<DATASET>" in `config` will be replaced with `model` `dataset`
+    - variables extracted from `config` :
+        e.g. the variables like "<dataset.kwargs.segments.train.0>" will be replaced with the values from `config`
 
     Parameters
     ----------
@@ -122,8 +173,13 @@ def fill_placeholder(config: dict, config_extend: dict):
             if isinstance(now_item[key], list) or isinstance(now_item[key], dict):
                 item_queue.append(now_item[key])
                 tail += 1
-            elif isinstance(now_item[key], str) and now_item[key] in config_extend.keys():
-                now_item[key] = config_extend[now_item[key]]
+            elif isinstance(now_item[key], str):
+                if now_item[key] in config_extend.keys():
+                    now_item[key] = config_extend[now_item[key]]
+                else:
+                    m = re.match(r"<(?P<name_path>[^<>]+)>", now_item[key])
+                    if m is not None:
+                        now_item[key] = get_item_from_obj(config, m.groupdict()["name_path"])
     return config
 
 

qlib/tests/config.py

@@ -50,8 +50,8 @@
 def get_data_handler_config(
     start_time="2008-01-01",
     end_time="2020-08-01",
-    fit_start_time="2008-01-01",
-    fit_end_time="2014-12-31",
+    fit_start_time="<dataset.kwargs.segments.train.0>",
+    fit_end_time="<dataset.kwargs.segments.train.1>",
     instruments=CSI300_MARKET,
 ):
     return {