perf(qav3): fine tune label_period_candles HPO

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 07e7d759e9700893299e45c83094690cc5241105..befe599410608fd5f7f802fafd9fec18e72da45a 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -276,14 +276,14 @@ def objective(
     )
     y_pred = model.predict(X_test)
 
+    min_label_period_candles = 1
+    max_label_period_candles = int(fit_live_predictions_candles / 2)
+    if max_label_period_candles < min_label_period_candles:
+        max_label_period_candles = min_label_period_candles
     label_period_candles = trial.suggest_int(
         "label_period_candles",
-        int(fit_live_predictions_candles / 20)
-        if fit_live_predictions_candles > 20
-        else 1,
-        int(fit_live_predictions_candles / 2)
-        if fit_live_predictions_candles > 2
-        else fit_live_predictions_candles,
+        min_label_period_candles,
+        max_label_period_candles,
     )
     y_test = y_test.tail(label_period_candles)
     y_pred = y_pred[-label_period_candles:]

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index fcffac6f0f4b81399effde42cacd5b3aa1bed920..d59c28907c0fca09a0e3c45e9a0e095e4bc1b45b 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -281,14 +281,12 @@ def objective(
     )
     y_pred = model.predict(X_test)
 
+    min_label_period_candles = 1
+    max_label_period_candles = int(fit_live_predictions_candles / 2)
+    if max_label_period_candles < min_label_period_candles:
+        max_label_period_candles = min_label_period_candles
     label_period_candles = trial.suggest_int(
-        "label_period_candles",
-        int(fit_live_predictions_candles / 20)
-        if fit_live_predictions_candles > 20
-        else 1,
-        int(fit_live_predictions_candles / 2)
-        if fit_live_predictions_candles > 2
-        else fit_live_predictions_candles,
+        "label_period_candles", min_label_period_candles, max_label_period_candles
     )
     y_test = y_test.tail(label_period_candles)
     y_pred = y_pred[-label_period_candles:]