Merge branch 'freqtrade:develop' into develop

freqtrade/freqai/data_kitchen.py

@@ -210,7 +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.data['constant_features_list'] = const_cols
                 logger.warning(f"Removed features {const_cols} with constant values.")
+            else:
+                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
@@ -241,7 +244,8 @@ class FreqaiDataKitchen:
             self.data["filter_drop_index_training"] = drop_index
 
         else:
-            filtered_df = self.check_pred_labels(filtered_df)
+            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
             drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -464,18 +468,16 @@ class FreqaiDataKitchen:
     def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
         """
         Check that prediction feature labels match training feature labels.
-        :params:
-        :df_predictions: incoming predictions
+        :param df_predictions: incoming predictions
         """
-        train_labels = self.data_dictionary["train_features"].columns
-        pred_labels = df_predictions.columns
-        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."
-            )
+        constant_labels = self.data['constant_features_list']
+        df_predictions = df_predictions.filter(
+            df_predictions.columns.difference(constant_labels)
+        )
+        logger.warning(
+            f"Removed {len(constant_labels)} features from prediction features, "
+            f"these were considered constant values during most recent training."
+        )
 
         return df_predictions
 

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -26,9 +26,8 @@ class XGBoostRFClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+            all the training and test data/labels.
         """
 
         X = data_dictionary["train_features"].to_numpy()
@@ -65,7 +64,7 @@ class XGBoostRFClassifier(BaseClassifierModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param  unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

@@ -29,6 +29,7 @@ class XGBoostRFRegressor(BaseRegressionModel):
 
         if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
             eval_set = None
+            eval_weights = None
         else:
             eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
             eval_weights = [data_dictionary['test_weights']]

freqtrade/freqai/prediction_models/XGBoostRegressor.py

@@ -29,6 +29,7 @@ class XGBoostRegressor(BaseRegressionModel):
 
         if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
             eval_set = None
+            eval_weights = None
         else:
             eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
             eval_weights = [data_dictionary['test_weights']]