debug classifier with predict proba

freqtrade/freqai/data_drawer.py

@@ -358,12 +358,7 @@ class FreqaiDataDrawer:
 
         dk.find_features(dataframe)
 
-        added_labels = []
-        if dk.unique_classes:
-            for label in dk.unique_classes:
-                added_labels += dk.unique_classes[label]
-   
-        full_labels = dk.label_list + added_labels
+        full_labels = dk.label_list + dk.unique_class_list
 
         for label in full_labels:
             dataframe[label] = 0

freqtrade/freqai/data_kitchen.py

@@ -91,6 +91,7 @@ class FreqaiDataKitchen:
         self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
         self.train_dates: DataFrame = pd.DataFrame()
         self.unique_classes: Dict[str, list] = {}
+        self.unique_class_list: list = []
 
     def set_paths(
         self,
@@ -338,7 +339,7 @@ class FreqaiDataKitchen:
         """
 
         for label in df.columns:
-            if df[label].dtype == object:
+            if df[label].dtype == object or label in self.unique_class_list:
                 continue
             df[label] = (
                 (df[label] + 1)
@@ -995,6 +996,10 @@ class FreqaiDataKitchen:
             f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
             self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
 
+        # incase targets are classifications
+        for label in self.unique_class_list:
+            self.data["labels_mean"][label], self.data["labels_std"][label] = 0, 0
+
         return
 
     def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
@@ -1014,3 +1019,7 @@ class FreqaiDataKitchen:
         for key in self.label_list:
             if dataframe[key].dtype == object:
                 self.unique_classes[key] = dataframe[key].dropna().unique()
+
+        if self.unique_classes:
+            for label in self.unique_classes:
+                self.unique_class_list += list(self.unique_classes[label])

freqtrade/freqai/freqai_interface.py

@@ -320,6 +320,8 @@ class IFreqaiModel(ABC):
             # allows FreqUI to show full return values.
             pred_df, do_preds = self.predict(dataframe, dk)
             self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
+            if pair not in self.dd.historic_predictions:
+                self.set_initial_historic_predictions(pred_df, dk, pair)
             dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
             return
         elif self.dk.check_if_model_expired(trained_timestamp):
@@ -336,6 +338,9 @@ class IFreqaiModel(ABC):
             # historical accuracy reasons.
             pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
 
+        self.dd.save_historic_predictions_to_disk()
+        if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
+            self.fit_live_predictions(dk)
         self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
         dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
 
@@ -503,7 +508,7 @@ class IFreqaiModel(ABC):
             self.dd.purge_old_models()
 
     def set_initial_historic_predictions(
-        self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
+        self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str
     ) -> None:
         """
         This function is called only if the datadrawer failed to load an
@@ -528,12 +533,6 @@ class IFreqaiModel(ABC):
         :param: dk: FreqaiDataKitchen = object containing methods for data analysis
         :param: pair: str = current pair
         """
-        num_candles = self.freqai_info.get('fit_live_predictions_candles', 600)
-        if not num_candles:
-            num_candles = 600
-        df_tail = df.tail(num_candles)
-        trained_predictions = model.predict(df_tail)
-        pred_df = DataFrame(trained_predictions, columns=dk.label_list)
 
         pred_df = dk.denormalize_labels_from_metadata(pred_df)
 
@@ -560,9 +559,12 @@ class IFreqaiModel(ABC):
         """
         import scipy as spy
 
+        # add classes from classifier label types if used
+        full_labels = dk.label_list + dk.unique_class_list
+
         num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
-        for label in dk.label_list:
+        for label in full_labels:
             if self.dd.historic_predictions[dk.pair][label].dtype == object:
                 continue
             f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))

freqtrade/freqai/prediction_models/BaseRegressionModel.py

@@ -62,15 +62,6 @@ class BaseRegressionModel(IFreqaiModel):
 
         model = self.fit(data_dictionary)
 
-        if pair not in self.dd.historic_predictions:
-            self.set_initial_historic_predictions(
-                data_dictionary['train_features'], model, dk, pair)
-
-        if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
-            self.fit_live_predictions(dk)
-
-        self.dd.save_historic_predictions_to_disk()
-
         logger.info(f"--------------------done training {pair}--------------------")
 
         return model