Move check and add log warning

freqtrade/freqai/data_kitchen.py

@@ -241,6 +241,7 @@ class FreqaiDataKitchen:
             self.data["filter_drop_index_training"] = drop_index
 
         else:
+            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
             drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -460,7 +461,7 @@ class FreqaiDataKitchen:
 
         return df
 
-    def check_pred_labels(self, df_predictions: DataFrame) -> None:
+    def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
         """
         Check that prediction feature labels match training feature labels.
         :params:
@@ -468,9 +469,15 @@ class FreqaiDataKitchen:
         """
         train_labels = self.data_dictionary["train_features"].columns
         pred_labels = df_predictions.columns
-        if len(train_labels.difference(pred_labels)) != 0:
-            self.data_dictionary["prediction_features"] = df_predictions[train_labels]
-        return
+        num_diffs = len(pred_labels.difference(train_labels))
+        if num_diffs != 0:
+            df_predictions = df_predictions[train_labels]
+            logger.warning(
+                f"Removed {num_diffs} features from prediction features, "
+                f"these were likely considered constant values during most recent training."
+            )
+
+        return df_predictions
 
     def principal_component_analysis(self) -> None:
         """

freqtrade/freqai/freqai_interface.py

@@ -492,8 +492,6 @@ class IFreqaiModel(ABC):
         # ensure user is feeding the correct indicators to the model
         self.check_if_feature_list_matches_strategy(dk)
 
-        dk.check_pred_labels(dk.data_dictionary['prediction_features'])
-
         if ft_params.get('inlier_metric_window', 0):
             dk.compute_inlier_metric(set_='predict')