Add ground work for TensorFlow models, add protections from common mistakes

freqtrade/freqai/data_kitchen.py

@@ -57,6 +57,7 @@ class FreqaiDataKitchen:
         self.live = live
         self.pair = pair
         self.svm_model: linear_model.SGDOneClassSVM = None
+        self.keras = self.freqai_config.get("keras", False)
         self.set_all_pairs()
         if not self.live:
             self.full_timerange = self.create_fulltimerange(
@@ -92,7 +93,7 @@ class FreqaiDataKitchen:
 
         return
 
-    def save_data(self, model: Any, coin: str = "", keras_model=False, label=None) -> None:
+    def save_data(self, model: Any, coin: str = "", label=None) -> None:
         """
         Saves all data associated with a model for a single sub-train time range
         :params:
@@ -106,7 +107,7 @@ class FreqaiDataKitchen:
         save_path = Path(self.data_path)
 
         # Save the trained model
-        if not keras_model:
+        if not self.keras:
             dump(model, save_path / f"{self.model_filename}_model.joblib")
         else:
             model.save(save_path / f"{self.model_filename}_model.h5")
@@ -140,7 +141,7 @@ class FreqaiDataKitchen:
 
         return
 
-    def load_data(self, coin: str = "", keras_model=False) -> Any:
+    def load_data(self, coin: str = "") -> Any:
         """
         loads all data required to make a prediction on a sub-train time range
         :returns:
@@ -174,7 +175,7 @@ class FreqaiDataKitchen:
         # try to access model in memory instead of loading object from disk to save time
         if self.live and self.model_filename in self.dd.model_dictionary:
             model = self.dd.model_dictionary[self.model_filename]
-        elif not keras_model:
+        elif not self.keras:
             model = load(self.data_path / str(self.model_filename + "_model.joblib"))
         else:
             from tensorflow import keras
@@ -559,6 +560,13 @@ class FreqaiDataKitchen:
         predict: bool = If true, inference an existing SVM model, else construct one
         """
 
+        if self.keras:
+            logger.warning("SVM outlier removal not currently supported for Keras based models. "
+                           "Skipping user requested function.")
+            if predict:
+                self.do_predict = np.ones(len(self.data_dictionary["prediction_features"]))
+            return
+
         if predict:
             assert self.svm_model, "No svm model available for outlier removal"
             y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"])