move prediction denormalization into datakitchen. remove duplicate associated code. avoid normalization/denormalization for string dtypes.

freqtrade/freqai/data_kitchen.py

@@ -356,7 +356,7 @@ class FreqaiDataKitchen:
 
         return self.data_dictionary
 
-    def normalize_data(self, data_dictionary: Dict, do_labels: bool = True) -> Dict[Any, Any]:
+    def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
         """
         Normalize all data in the data_dictionary according to the training dataset
         :params:
@@ -378,24 +378,26 @@ class FreqaiDataKitchen:
             self.data[item + "_max"] = train_max[item]
             self.data[item + "_min"] = train_min[item]
 
-        if do_labels:
-            train_labels_max = data_dictionary["train_labels"].max()
-            train_labels_min = data_dictionary["train_labels"].min()
+        for item in data_dictionary["train_labels"].keys():
+            if data_dictionary["train_labels"][item].dtype == str:
+                continue
+            train_labels_max = data_dictionary["train_labels"][item].max()
+            train_labels_min = data_dictionary["train_labels"][item].min()
             data_dictionary["train_labels"] = (
                 2
-                * (data_dictionary["train_labels"] - train_labels_min)
+                * (data_dictionary["train_labels"][item] - train_labels_min)
                 / (train_labels_max - train_labels_min)
                 - 1
             )
-            data_dictionary["test_labels"] = (
+            data_dictionary["test_labels"][item] = (
                 2
                 * (data_dictionary["test_labels"] - train_labels_min)
                 / (train_labels_max - train_labels_min)
                 - 1
             )
 
-            self.data["labels_max"] = train_labels_max.to_dict()
-            self.data["labels_min"] = train_labels_min.to_dict()
+            self.data[f"{item}_max"] = train_labels_max  # .to_dict()
+            self.data[f"{item}_min"] = train_labels_min  # .to_dict()
 
         return data_dictionary
 
@@ -417,6 +419,25 @@ class FreqaiDataKitchen:
 
         return df
 
+    def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
+        """
+        Normalize a set of data using the mean and standard deviation from
+        the associated training data.
+        :params:
+        :df: Dataframe of predictions to be denormalized
+        """
+
+        for label in self.label_list:
+            if df[label].dtype == str:
+                continue
+            df[label] = (
+                (df[label] + 1)
+                * (self.data[f"{label}_max"] - self.data[f"{label}_min"])
+                / 2
+            ) + self.data[f"{label}_min"]
+
+        return df
+
     def split_timerange(
         self, tr: str, train_split: int = 28, bt_split: int = 7
     ) -> Tuple[list, list]:

freqtrade/freqai/freqai_interface.py

@@ -501,12 +501,8 @@ class IFreqaiModel(ABC):
     ) -> None:
         trained_predictions = model.predict(df)
         pred_df = DataFrame(trained_predictions, columns=dk.label_list)
-        for label in dk.label_list:
-            pred_df[label] = (
-                (pred_df[label] + 1)
-                * (dk.data["labels_max"][label] - dk.data["labels_min"][label])
-                / 2
-            ) + dk.data["labels_min"][label]
+
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
 
         self.dd.historic_predictions[pair] = pd.DataFrame()
         self.dd.historic_predictions[pair] = copy.deepcopy(pred_df)

freqtrade/freqai/prediction_models/BaseRegressionModel.py

@@ -107,11 +107,6 @@ class BaseRegressionModel(IFreqaiModel):
         predictions = self.model.predict(dk.data_dictionary["prediction_features"])
         pred_df = DataFrame(predictions, columns=dk.label_list)
 
-        for label in dk.label_list:
-            pred_df[label] = (
-                (pred_df[label] + 1)
-                * (dk.data["labels_max"][label] - dk.data["labels_min"][label])
-                / 2
-            ) + dk.data["labels_min"][label]
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
 
         return (pred_df, dk.do_predict)