black format

qlib/contrib/eva/alpha.py

@@ -1,12 +1,12 @@
-'''
+"""
 Here is a batch of evaluation functions.
 
 The interface should be redesigned carefully in the future.
-'''
+"""
 import pandas as pd
 
 
-def calc_ic(pred: pd.Series, label: pd.Series, date_col='datetime', dropna=False) -> (pd.Series, pd.Series):
+def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> (pd.Series, pd.Series):
     """calc_ic.
 
     Parameters
@@ -23,9 +23,9 @@ def calc_ic(pred: pd.Series, label: pd.Series, date_col='datetime', dropna=False
     (pd.Series, pd.Series)
         ic and rank ic
     """
-    df = pd.DataFrame({'pred': pred, 'label': label})
-    ic = df.groupby(date_col).apply(lambda df: df['pred'].corr(df['label']))
-    ric = df.groupby(date_col).apply(lambda df: df['pred'].corr(df['label'], method='spearman'))
+    df = pd.DataFrame({"pred": pred, "label": label})
+    ic = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"]))
+    ric = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"], method="spearman"))
     if dropna:
         return ic.dropna(), ric.dropna()
     else:

qlib/data/dataset/__init__.py

@@ -100,11 +100,13 @@ class DatasetH(Dataset):
         self._handler = init_instance_by_config(handler, accept_types=DataHandler)
         self._segments = segments.copy()
 
-    def prepare(self,
-                segments: Union[List[str], Tuple[str], str, slice],
-                col_set=DataHandler.CS_ALL,
-                data_key=DataHandlerLP.DK_I,
-                **kwargs) -> Union[List[pd.DataFrame], pd.DataFrame]:
+    def prepare(
+        self,
+        segments: Union[List[str], Tuple[str], str, slice],
+        col_set=DataHandler.CS_ALL,
+        data_key=DataHandlerLP.DK_I,
+        **kwargs,
+    ) -> Union[List[pd.DataFrame], pd.DataFrame]:
         """
         prepare the data for learning and inference
 
@@ -132,8 +134,8 @@ class DatasetH(Dataset):
         logger = get_module_logger("DatasetH")
         fetch_kwargs = {"col_set": col_set}
         fetch_kwargs.update(kwargs)
-        if "data_key"in getfullargspec(self._handler.fetch).args:
-            fetch_kwargs['data_key'] = data_key
+        if "data_key" in getfullargspec(self._handler.fetch).args:
+            fetch_kwargs["data_key"] = data_key
         else:
             logger.info(f"data_key[{data_key}] is ignored.")
 

qlib/utils/serial.py

@@ -10,14 +10,14 @@ class Serializable:
     Serializable behaves like pickle.
     But it only saves the state whose name **does not** start with `_`
     """
+
     def __init__(self):
         self._dump_all = False
         self._exclude = []
 
     def __getstate__(self) -> dict:
         return {
-            k: v
-            for k, v in self.__dict__.items() if k not in self.exclude and (self.dump_all or not k.startswith("_"))
+            k: v for k, v in self.__dict__.items() if k not in self.exclude and (self.dump_all or not k.startswith("_"))
         }
 
     def __setstate__(self, state: dict):

qlib/workflow/expm.py

@@ -251,7 +251,7 @@ class MLflowExpManager(ExpManager):
             self.active_experiment = None
 
     def create_exp(self, experiment_name=None):
-        assert(experiment_name is not None)
+        assert experiment_name is not None
         # init experiment
         experiment_id = self.client.create_experiment(experiment_name)
         experiment = MLflowExperiment(experiment_id, experiment_name, self.uri)

qlib/workflow/record_temp.py

@@ -119,7 +119,7 @@ class SignalRecord(RecordTemp):
                 raw_label = DatasetH.prepare(**params)
             except TypeError:
                 # The argument number is not right
-                del params['data_key']
+                del params["data_key"]
                 # The backend handler should be DataHandler
                 raw_label = DatasetH.prepare(**params)
             self.recorder.save_objects(**{"label.pkl": raw_label})
@@ -147,7 +147,7 @@ class SigAnaRecord(SignalRecord):
             "IC": ic.mean(),
             "ICIR": ic.mean() / ic.std(),
             "Rank IC": ric.mean(),
-            "Rank ICIR": ric.mean() / ric.std()
+            "Rank ICIR": ric.mean() / ric.std(),
         }
         self.recorder.log_metrics(**metrics)
         self.recorder.save_objects(**{"ic.pkl": ic, "ric.pkl": ric}, artifact_path=self.artifact_path_sig)