Change storage loc and fix test fail

freqtrade/freqai/data_kitchen.py

@@ -71,7 +71,6 @@ class FreqaiDataKitchen:
         self.data_path = Path()
         self.label_list: List = []
         self.training_features_list: List = []
-        self.constant_features_list: List = []
         self.model_filename: str = ""
         self.backtesting_results_path = Path()
         self.backtest_predictions_folder: str = "backtesting_predictions"
@@ -211,10 +210,10 @@ class FreqaiDataKitchen:
             const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
             if const_cols:
                 filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
-                self.constant_features_list = const_cols
+                self.data['constant_features_list'] = const_cols
                 logger.warning(f"Removed features {const_cols} with constant values.")
             else:
-                self.constant_features_list = []
+                self.data['constant_features_list'] = []
             # we don't care about total row number (total no. datapoints) in training, we only care
             # about removing any row with NaNs
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
@@ -245,7 +244,7 @@ class FreqaiDataKitchen:
             self.data["filter_drop_index_training"] = drop_index
 
         else:
-            if len(self.constant_features_list):
+            if len(self.data['constant_features_list']):
                 filtered_df = self.check_pred_labels(filtered_df)
             # we are backtesting so we need to preserve row number to send back to strategy,
             # so now we use do_predict to avoid any prediction based on a NaN
@@ -472,7 +471,7 @@ class FreqaiDataKitchen:
         :params:
         :df_predictions: incoming predictions
         """
-        constant_labels = self.constant_features_list
+        constant_labels = self.data['constant_features_list']
         df_predictions = df_predictions.filter(
             df_predictions.columns.difference(constant_labels)
         )