reformat code

freqtrade/freqai/base_models/PyTorchModelTrainer.py

@@ -69,7 +69,7 @@ class PyTorchModelTrainer:
 
         self.model.eval()
         epochs = self.calc_n_epochs(
-            n_obs=len(data_dictionary[f'test_features']),
+            n_obs=len(data_dictionary['test_features']),
             batch_size=self.batch_size,
             n_iters=self.eval_iters
         )
@@ -101,8 +101,11 @@ class PyTorchModelTrainer:
                 torch.from_numpy(data_dictionary[f'{split}_features'].values).float(),
                 torch.from_numpy(data_dictionary[f'{split}_labels'].astype(float).values)
                 .long()
-                .view(labels_view)  # todo currently assuming class labels are strings ['0.0', '1.0' .. n_classes]. need to resolve it per ClassifierModel
+                .view(labels_view)
             )
+            # todo currently assuming class labels are strings ['0.0', '1.0' .. n_classes].
+            #  need to resolve it per ClassifierModel
+
             data_loader = DataLoader(
                 dataset,
                 batch_size=self.batch_size,

freqtrade/freqai/data_drawer.py

@@ -24,7 +24,6 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.strategy.interface import IStrategy
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -90,8 +89,8 @@ class FreqaiDataDrawer:
         self.metric_tracker_lock = threading.Lock()
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
-                "model_filename": "", "trained_timestamp": 0,
-                "data_path": "", "extras": {}}
+            "model_filename": "", "trained_timestamp": 0,
+            "data_path": "", "extras": {}}
         self.model_type = self.freqai_info.get('model_save_type', 'joblib')
 
     def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
@@ -446,9 +445,9 @@ class FreqaiDataDrawer:
             dump(model, save_path / f"{dk.model_filename}_model.joblib")
         elif self.model_type == 'keras':
             model.save(save_path / f"{dk.model_filename}_model.h5")
-        elif 'stable_baselines' in self.model_type or\
-                'sb3_contrib' == self.model_type or\
-                'pytorch' == self.model_type:
+        elif ('stable_baselines' in self.model_type or
+              'sb3_contrib' == self.model_type or
+              'pytorch' == self.model_type):
             model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
@@ -581,16 +580,16 @@ class FreqaiDataDrawer:
                     if len(df_dp.index) == 0:
                         continue
                     if str(hist_df.iloc[-1]["date"]) == str(
-                        df_dp.iloc[-1:]["date"].iloc[-1]
+                            df_dp.iloc[-1:]["date"].iloc[-1]
                     ):
                         continue
 
                     try:
                         index = (
-                            df_dp.loc[
-                                df_dp["date"] == hist_df.iloc[-1]["date"]
-                            ].index[0]
-                            + 1
+                                df_dp.loc[
+                                    df_dp["date"] == hist_df.iloc[-1]["date"]
+                                    ].index[0]
+                                + 1
                         )
                     except IndexError:
                         if hist_df.iloc[-1]['date'] < df_dp['date'].iloc[0]:
@@ -643,7 +642,7 @@ class FreqaiDataDrawer:
                 )
 
     def get_base_and_corr_dataframes(
-        self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
+            self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
     ) -> Tuple[Dict[Any, Any], Dict[Any, Any]]:
         """
         Searches through our historic_data in memory and returns the dataframes relevant