Merge pull request #7544 from th0rntwig/prediction-shape

Remove constant labels from prediction

freqtrade/freqai/data_kitchen.py

@@ -243,6 +243,7 @@ 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)
@@ -462,6 +463,24 @@ class FreqaiDataKitchen:
 
         return df
 
+    def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
+        """
+        Check that prediction feature labels match training feature labels.
+        :params:
+        :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."
+            )
+
+        return df_predictions
+
     def principal_component_analysis(self) -> None:
         """
         Performs Principal Component Analysis on the data for dimensionality reduction

tests/freqai/conftest.py

@@ -107,6 +107,8 @@ def make_unfiltered_dataframe(mocker, freqai_conf):
     unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
                 strategy, corr_dataframes, base_dataframes, freqai.dk.pair
             )
+    for i in range(5):
+        unfiltered_dataframe[f'constant_{i}'] = i
 
     unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
 

tests/freqai/test_freqai_interface.py

@@ -157,7 +157,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
         ("CatboostClassifier", 6, "freqai_test_classifier")
     ],
     )
-def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
+def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
     freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     freqai_conf['runmode'] = RunMode.BACKTEST
     Trade.use_db = False
@@ -181,12 +181,23 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
     corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+    for i in range(5):
+        df[f'%-constant_{i}'] = i
+        # df.loc[:, f'%-constant_{i}'] = i
 
     metadata = {"pair": "LTC/BTC"}
     freqai.start_backtesting(df, metadata, freqai.dk)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
 
     assert len(model_folders) == num_files
+    assert log_has_re(
+        "Removed features ",
+        caplog,
+    )
+    assert log_has_re(
+        "Removed 5 features from prediction features, ",
+        caplog,
+    )
     Backtesting.cleanup()
     shutil.rmtree(Path(freqai.dk.full_path))
 
@@ -256,6 +267,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
     corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+
     freqai.start_backtesting(df, metadata, freqai.dk)
 
     assert log_has_re(
@@ -312,6 +324,7 @@ def test_follow_mode(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
+
     freqai.start_live(df, metadata, strategy, freqai.dk)
 
     assert len(freqai.dk.return_dataframe.index) == 5702