black format

examples/highfreq/highfreq_processor.py

@@ -70,7 +70,7 @@ class HighFreqNorm(Processor):
             columns=["FEATURE_%d" % i for i in range(12 * 240)],
         ).sort_index()
         return df_new_features
-    
+
     def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time

examples/highfreq/workflow.py

@@ -177,8 +177,8 @@ class HighfreqWorkflow(object):
         dataset_backtest.setup_data(handler_kwargs={})
 
         ##=============get data=============
-        xtest, = dataset.prepare(["test"])
-        backtest_test, = dataset_backtest.prepare(["test"])
+        (xtest,) = dataset.prepare(["test"])
+        (backtest_test,) = dataset_backtest.prepare(["test"])
 
         print(xtest, backtest_test)
         return

examples/rolling_process_data/workflow.py

@@ -105,7 +105,7 @@ class RollingDataWorkflow(object):
                     handler_kwargs={
                         "start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
                         "end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
-                        "processor_kwargs":{
+                        "processor_kwargs": {
                             "fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
                             "fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
                         },
@@ -126,7 +126,9 @@ class RollingDataWorkflow(object):
                     },
                 )
                 dataset.setup_data(
-                    handler_kwargs={"init_type": DataHandlerLP.IT_FIT_SEQ,}
+                    handler_kwargs={
+                        "init_type": DataHandlerLP.IT_FIT_SEQ,
+                    }
                 )
 
             dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])