Merge branch 'develop' into backtest_live_models

freqtrade/freqai/data_kitchen.py

@@ -245,6 +245,7 @@ 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)
@@ -487,6 +488,24 @@ 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

freqtrade/freqai/freqai_interface.py

@@ -200,16 +200,15 @@ class IFreqaiModel(ABC):
             (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
 
             dk = FreqaiDataKitchen(self.config, self.live, pair)
-            dk.set_paths(pair, trained_timestamp)
             (
                 retrain,
                 new_trained_timerange,
                 data_load_timerange,
             ) = dk.check_if_new_training_required(trained_timestamp)
-            dk.set_paths(pair, new_trained_timerange.stopts)
 
             if retrain:
                 self.train_timer('start')
+                dk.set_paths(pair, new_trained_timerange.stopts)
                 try:
                     self.extract_data_and_train_model(
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
@@ -290,9 +289,7 @@ class IFreqaiModel(ABC):
             if dk.backtest_live_models:
                 timestamp_model_id = int(tr_backtest.startts)
 
-            dk.data_path = Path(
-                dk.full_path / f"sub-train-{pair.split('/')[0]}_{timestamp_model_id}"
-                )
+            dk.set_paths(pair, timestamp_model_id)
 
             dk.set_new_model_names(pair, timestamp_model_id)
 

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -1,4 +1,5 @@
 import logging
+from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostClassifier, Pool
@@ -31,8 +32,9 @@ class CatboostClassifier(BaseClassifierModel):
         )
 
         cbr = CatBoostClassifier(
-            allow_writing_files=False,
+            allow_writing_files=True,
             loss_function='MultiClass',
+            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -1,4 +1,5 @@
 import logging
+from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
@@ -41,7 +42,8 @@ class CatboostRegressor(BaseRegressionModel):
         init_model = self.get_init_model(dk.pair)
 
         model = CatBoostRegressor(
-            allow_writing_files=False,
+            allow_writing_files=True,
+            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -1,4 +1,5 @@
 import logging
+from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
@@ -26,7 +27,8 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
         """
 
         cbr = CatBoostRegressor(
-            allow_writing_files=False,
+            allow_writing_files=True,
+            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )