From 72101f059dad268c7977d5eb2227766d5df86da6 Mon Sep 17 00:00:00 2001
From: robcaulk <rob.caulk@gmail.com>
Date: Fri, 16 Jun 2023 13:20:35 +0200
Subject: [PATCH] feat: ensure full backwards compatibility

---
 freqtrade/freqai/data_kitchen.py     |  8 ++------
 freqtrade/freqai/freqai_interface.py | 26 +++++++++++++++++++++++++-
 2 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 3f8d0fb4b..3a91c8551 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -962,8 +962,7 @@ class FreqaiDataKitchen:
                        " This can be achieved by following the migration guide at "
                        f"{ft}/strategy_migration/#freqai-new-data-pipeline "
                        "We added a basic pipeline for you, but this will be removed "
-                       "in a future version.\n"
-                       "This version does not include any outlier configurations")
+                       "in a future version.")
 
         return data_dictionary
 
@@ -977,11 +976,8 @@ class FreqaiDataKitchen:
                        " This can be achieved by following the migration guide at "
                        f"{ft}/strategy_migration/#freqai-new-data-pipeline "
                        "We added a basic pipeline for you, but this will be removed "
-                       "in a future version.\n"
-                       "This version does not include any outlier configurations")
+                       "in a future version.")
 
         pred_df, _, _ = self.label_pipeline.inverse_transform(df)
-        self.DI_values = np.zeros(len(pred_df.index))
-        self.do_predict = np.ones(len(pred_df.index))
 
         return pred_df
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index a6e5d40ed..4ca5467b6 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -979,6 +979,23 @@ class IFreqaiModel(ABC):
                        " This can be achieved by following the migration guide at "
                        f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline")
         dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+        dd = dk.data_dictionary
+        (dd["train_features"],
+         dd["train_labels"],
+         dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
+                                                                  dd["train_labels"],
+                                                                  dd["train_weights"])
+
+        (dd["test_features"],
+         dd["test_labels"],
+         dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
+                                                             dd["test_labels"],
+                                                             dd["test_weights"])
+
+        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
+
+        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
+        dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
         return
 
     def data_cleaning_predict(self, dk: FreqaiDataKitchen, pair: str):
@@ -989,5 +1006,12 @@ class IFreqaiModel(ABC):
                        " data pipeline. Please update your model to use the new data pipeline."
                        " This can be achieved by following the migration guide at "
                        f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline")
-        dk.label_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+        dd = dk.data_dictionary
+        dd["predict_features"], outliers, _ = dk.feature_pipeline.transform(
+            dd["predict_features"], outlier_check=True)
+        if self.freqai_info.get("DI_threshold", 0) > 0:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(len(outliers.index))
+        dk.do_predict = outliers.to_numpy()
         return