Auto injecting model and dataset for Recorder

qlib/data/ops.py

@@ -13,7 +13,7 @@
 from typing import Union, List, Type
 from scipy.stats import percentileofscore
 
-from .base import Expression, ExpressionOps
+from .base import Expression, ExpressionOps, Feature
 from ..log import get_module_logger
 from ..utils import get_callable_kwargs
 
@@ -1485,6 +1485,7 @@ def __init__(self, feature_left, feature_right, N):
     IdxMax,
     IdxMin,
     If,
+    Feature,
 ]
 
 
@@ -1517,7 +1518,7 @@ def register(self, ops_list: List[Union[Type[ExpressionOps], dict]]):
             else:
                 _ops_class = _operator
 
-            if not issubclass(_ops_class, ExpressionOps):
+            if not issubclass(_ops_class, Expression):
                 raise TypeError("operator must be subclass of ExpressionOps, not {}".format(_ops_class))
 
             if _ops_class.__name__ in self._ops:

qlib/model/trainer.py

@@ -70,19 +70,19 @@ def fill_placeholder(config: dict, config_extend: dict):
     # bfs
     top = 0
     tail = 1
-    item_quene = [config]
+    item_queue = [config]
     while top < tail:
-        now_item = item_quene[top]
+        now_item = item_queue[top]
         top += 1
         if isinstance(now_item, list):
             item_keys = range(len(now_item))
         elif isinstance(now_item, dict):
             item_keys = now_item.keys()
         for key in item_keys:
             if isinstance(now_item[key], list) or isinstance(now_item[key], dict):
-                item_quene.append(now_item[key])
+                item_queue.append(now_item[key])
                 tail += 1
-            elif now_item[key] in config_extend.keys():
+            elif isinstance(now_item[key], str) and now_item[key] in config_extend.keys():
                 now_item[key] = config_extend[now_item[key]]
     return config
 
@@ -114,10 +114,19 @@ def end_task_train(rec: Recorder, experiment_name: str) -> Recorder:
         task_config = fill_placeholder(task_config, placehorder_value)
         # generate records: prediction, backtest, and analysis
         records = task_config.get("record", [])
-        if isinstance(records, dict):  # prevent only one dict
+        if isinstance(records, dict):  # uniform the data format to list
             records = [records]
+
         for record in records:
-            r = init_instance_by_config(record, recorder=rec)
+            # Some recorder require the parameter `model` and `dataset`.
+            # try to automatically pass in them to the initialization function
+            # to make defining the tasking easier
+            r = init_instance_by_config(
+                record,
+                recorder=rec,
+                default_module="qlib.workflow.record_temp",
+                try_kwargs={"model": model, "dataset": dataset},
+            )
             r.generate()
     return rec
 

qlib/utils/__init__.py

@@ -27,7 +27,7 @@
 import numpy as np
 import pandas as pd
 from pathlib import Path
-from typing import Union, Tuple, Any, Text, Optional
+from typing import Dict, Union, Tuple, Any, Text, Optional
 from types import ModuleType
 from urllib.parse import urlparse
 
@@ -232,7 +232,11 @@ def get_callable_kwargs(config: Union[dict, str], default_module: Union[str, Mod
 
 
 def init_instance_by_config(
-    config: Union[str, dict, object], default_module=None, accept_types: Union[type, Tuple[type]] = (), **kwargs
+    config: Union[str, dict, object],
+    default_module=None,
+    accept_types: Union[type, Tuple[type]] = (),
+    try_kwargs: Dict = {},
+    **kwargs,
 ) -> Any:
     """
     get initialized instance with config
@@ -270,6 +274,10 @@ def init_instance_by_config(
         Optional. If the config is a instance of specific type, return the config directly.
         This will be passed into the second parameter of isinstance.
 
+    try_kwargs: Dict
+        Try to pass in kwargs in `try_kwargs` when initialized the instance
+        If error occurred, it will fail back to initialization without try_kwargs.
+
     Returns
     -------
     object:
@@ -286,7 +294,14 @@ def init_instance_by_config(
                 return pickle.load(f)
 
     klass, cls_kwargs = get_callable_kwargs(config, default_module=default_module)
-    return klass(**cls_kwargs, **kwargs)
+
+    try:
+        return klass(**cls_kwargs, **try_kwargs, **kwargs)
+    except (TypeError,):
+        # TypeError for handling errors like
+        # 1: `XXX() got multiple values for keyword argument 'YYY'`
+        # 2: `XXX() got an unexpected keyword argument 'YYY'
+        return klass(**cls_kwargs, **kwargs)
 
 
 @contextlib.contextmanager