fix(qav3): reset predictions normal distribution fitting

Signed-off-by: Jérôme Benoit <jerome.benoit@sap.com>

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index ae470cecfe2182c6868dcd75c0f2080e11fd85cc..37f4d79e8a8deeac40fa8e27f3731b8438ed7640 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -200,15 +200,14 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
             dk.data["extra_returns_per_train"][MAXIMA_THRESHOLD_COLUMN] = max_pred
 
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
-        for label in dk.label_list + dk.unique_class_list:
+        for label in dk.label_list:
             if pred_df_full[label].dtype == object:
                 continue
-            if not warmed_up:
-                f = [0, 0]
-            else:
-                f = spy.stats.norm.fit(pred_df_full[label])
-            dk.data["labels_mean"][label] = f[0]
-            dk.data["labels_std"][label] = f[1]
+            # if not warmed_up:
+            f = [0, 0]
+            # else:
+            #     f = spy.stats.norm.fit(pred_df_full[label])
+            dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
 
         # fit the DI_threshold
         if not warmed_up:

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index d0fd9c4e9842c82ce9933855656d2678a0b5f206..f83e83ddbe1e0361553ebb96d6084fa8b59873ec 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -201,15 +201,14 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
             dk.data["extra_returns_per_train"][MAXIMA_THRESHOLD_COLUMN] = max_pred
 
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
-        for label in dk.label_list + dk.unique_class_list:
+        for label in dk.label_list:
             if pred_df_full[label].dtype == object:
                 continue
-            if not warmed_up:
-                f = [0, 0]
-            else:
-                f = spy.stats.norm.fit(pred_df_full[label])
-            dk.data["labels_mean"][label] = f[0]
-            dk.data["labels_std"][label] = f[1]
+            # if not warmed_up:
+            f = [0, 0]
+            # else:
+            #     f = spy.stats.norm.fit(pred_df_full[label])
+            dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
 
         # fit the DI_threshold
         if not warmed_up: