refactor(qav3): cleanup predictions sorting code

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index 8390f53dbe16de80be600c525680d91c424c177b..f1eaffa939dc33b7ba10e6066e742f254c1fa8fe 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -250,13 +250,10 @@ def min_max_pred(
 def __min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ):
-    pred_df_sorted = pd.DataFrame()
-    for label in pred_df:
-        if pred_df[label].dtype == object:
-            continue
-        pred_df_sorted[label] = pred_df[label]
-    pred_df_sorted = pred_df_sorted.apply(
-        lambda col: col.sort_values(ascending=False, ignore_index=True)
+    pred_df_sorted = (
+        pred_df.select_dtypes(exclude=["object"])
+        .copy()
+        .apply(lambda col: col.sort_values(ascending=False, ignore_index=True))
     )
 
     frequency = fit_live_predictions_candles / label_period_candles

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 4b771cb1d3d7b96109c55b85fbf392eb40b81ab0..e910125f9a137b7dd1fff7fa282765fb2412ab26 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -253,13 +253,10 @@ def min_max_pred(
 def __min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ):
-    pred_df_sorted = pd.DataFrame()
-    for label in pred_df:
-        if pred_df[label].dtype == object:
-            continue
-        pred_df_sorted[label] = pred_df[label]
-    pred_df_sorted = pred_df_sorted.apply(
-        lambda col: col.sort_values(ascending=False, ignore_index=True)
+    pred_df_sorted = (
+        pred_df.select_dtypes(exclude=["object"])
+        .copy()
+        .apply(lambda col: col.sort_values(ascending=False, ignore_index=True))
     )
 
     frequency = fit_live_predictions_candles / label_period_candles