get_cls_kwargs renamed get_callable_kwargs

qlib/contrib/data/handler.py

@@ -3,7 +3,7 @@
 
 from ...data.dataset.handler import DataHandlerLP
 from ...data.dataset.processor import Processor
-from ...utils import get_cls_kwargs
+from ...utils import get_callable_kwargs
 from ...data.dataset import processor as processor_module
 from ...log import TimeInspector
 from inspect import getfullargspec
@@ -14,7 +14,7 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time):
     new_l = []
     for p in proc_l:
         if not isinstance(p, Processor):
-            klass, pkwargs = get_cls_kwargs(p, processor_module)
+            klass, pkwargs = get_callable_kwargs(p, processor_module)
             args = getfullargspec(klass).args
             if "fit_start_time" in args and "fit_end_time" in args:
                 assert (

qlib/data/dataset/loader.py

@@ -12,7 +12,7 @@ from typing import Tuple, Union, List, Type
 from qlib.data import D
 from qlib.data import filter as filter_module
 from qlib.data.filter import BaseDFilter
-from qlib.utils import load_dataset, init_instance_by_config, time_to_slc_point, get_cls_kwargs
+from qlib.utils import load_dataset, init_instance_by_config, time_to_slc_point, get_callable_kwargs
 from qlib.log import get_module_logger
 
 
@@ -212,7 +212,7 @@ class QlibDataLoader(DLWParser):
                 raise ValueError(f"sample method error, only pandas.DataFrame.resample is supported")
         elif isinstance(_method, dict):
             # module_path && func name
-            _method, _ = get_cls_kwargs(_method, obj_type="func")
+            _method, _ = get_callable_kwargs(_method)
         else:
             raise TypeError(f"sample_method only supports [str, dict], currently it is {_method}")
         return _method

qlib/data/ops.py

@@ -15,7 +15,7 @@ from scipy.stats import percentileofscore
 
 from .base import Expression, ExpressionOps
 from ..log import get_module_logger
-from ..utils import get_cls_kwargs
+from ..utils import get_callable_kwargs
 
 try:
     from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi
@@ -1513,7 +1513,7 @@ class OpsWrapper:
         """
         for _operator in ops_list:
             if isinstance(_operator, dict):
-                _ops_class, _ = get_cls_kwargs(_operator)
+                _ops_class, _ = get_callable_kwargs(_operator)
             else:
                 _ops_class = _operator
 

qlib/model/trainer.py

@@ -17,7 +17,7 @@ from typing import Callable, List
 from qlib.data.dataset import Dataset
 from qlib.log import get_module_logger
 from qlib.model.base import Model
-from qlib.utils import flatten_dict, get_cls_kwargs, init_instance_by_config
+from qlib.utils import flatten_dict, get_callable_kwargs, init_instance_by_config
 from qlib.workflow import R
 from qlib.workflow.record_temp import SignalRecord
 from qlib.workflow.recorder import Recorder
@@ -71,7 +71,7 @@ def end_task_train(rec: Recorder, experiment_name: str) -> Recorder:
         if isinstance(records, dict):  # prevent only one dict
             records = [records]
         for record in records:
-            cls, kwargs = get_cls_kwargs(record, default_module="qlib.workflow.record_temp")
+            cls, kwargs = get_callable_kwargs(record, default_module="qlib.workflow.record_temp")
             if cls is SignalRecord:
                 rconf = {"model": model, "dataset": dataset, "recorder": rec}
             else:

qlib/utils/__init__.py

@@ -189,9 +189,7 @@ def get_module_by_module_path(module_path: Union[str, ModuleType]):
     return module
 
 
-def get_cls_kwargs(
+def get_callable_kwargs(config: Union[dict, str], default_module: Union[str, ModuleType] = None) -> (type, dict):
-    config: Union[dict, str], default_module: Union[str, ModuleType] = None, obj_type: str = "class"
-) -> (type, dict):
     """
     extract class/func and kwargs from config info
 
@@ -205,8 +203,6 @@ def get_cls_kwargs(
         This function will load class from the config['module_path'] first.
         If config['module_path'] doesn't exists, it will load the class from default_module.
 
-    obj_type: str
-        "class" or "func"
     Returns
     -------
     (type, dict):
@@ -216,16 +212,16 @@ def get_cls_kwargs(
         module = get_module_by_module_path(config.get("module_path", default_module))
 
         # raise AttributeError
-        _obj = getattr(module, config[obj_type])
+        _callable = getattr(module, config["class" if "class" in config else "func"])
         kwargs = config.get("kwargs", {})
     elif isinstance(config, str):
         module = get_module_by_module_path(default_module)
 
-        _obj = getattr(module, config)
+        _callable = getattr(module, config)
         kwargs = {}
     else:
         raise NotImplementedError(f"This type of input is not supported")
-    return _obj, kwargs
+    return _callable, kwargs
 
 
 def init_instance_by_config(
@@ -276,7 +272,7 @@ def init_instance_by_config(
             with open(os.path.join(pr.netloc, pr.path), "rb") as f:
                 return pickle.load(f)
 
-    klass, cls_kwargs = get_cls_kwargs(config, default_module=default_module)
+    klass, cls_kwargs = get_callable_kwargs(config, default_module=default_module)
     return klass(**cls_kwargs, **kwargs)
 
 

qlib/workflow/online/utils.py

@@ -11,7 +11,7 @@ from typing import List, Union
 from qlib.data.dataset import TSDatasetH
 
 from qlib.log import get_module_logger
-from qlib.utils import get_cls_kwargs
+from qlib.utils import get_callable_kwargs
 from qlib.utils.exceptions import LoadObjectError
 from qlib.workflow.online.update import PredUpdater
 from qlib.workflow.recorder import Recorder
@@ -172,7 +172,7 @@ class OnlineToolR(OnlineTool):
             hist_ref = 0
             task = rec.load_object("task")
             # Special treatment of historical dependencies
-            cls, kwargs = get_cls_kwargs(task["dataset"], default_module="qlib.data.dataset")
+            cls, kwargs = get_callable_kwargs(task["dataset"], default_module="qlib.data.dataset")
             if issubclass(cls, TSDatasetH):
                 hist_ref = kwargs.get("step_len", TSDatasetH.DEFAULT_STEP_LEN)
             try: