perf(qav3): ensure hyperopt choose the labelling period optimally

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 227b9af0b88fdb2845d15f29f96392d164821186..59ede95839f41458068fd521e5a29a21619e34ee 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -608,8 +608,12 @@ def period_objective(
         min_label_period_candles,
         max_label_period_candles,
     )
-    y_test = y_test.tail(label_period_candles)
-    y_pred = y_pred[-label_period_candles:]
+    y_test_length = len(y_test)
+    y_pred_length = len(y_pred)
+    y_test = y_test.tail(y_test_length - (y_test_length % label_period_candles))
+    y_pred = y_pred[-(y_pred_length - (y_pred_length % label_period_candles)) :]
+    y_test.reshape(len(y_test) // label_period_candles, label_period_candles)
+    y_pred.reshape(len(y_pred) // label_period_candles, label_period_candles)
 
     error = sklearn.metrics.root_mean_squared_error(y_test, y_pred)
 

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index f0860ca958f9da98917def928d477d372839305d..3b02ebf6d6bced444598f2a85a4f85d034f1ebb7 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -612,8 +612,12 @@ def period_objective(
         min_label_period_candles,
         max_label_period_candles,
     )
-    y_test = y_test.tail(label_period_candles)
-    y_pred = y_pred[-label_period_candles:]
+    y_test_length = len(y_test)
+    y_pred_length = len(y_pred)
+    y_test = y_test.tail(y_test_length - (y_test_length % label_period_candles))
+    y_pred = y_pred[-(y_pred_length - (y_pred_length % label_period_candles)) :]
+    y_test.reshape(len(y_test) // label_period_candles, label_period_candles)
+    y_pred.reshape(len(y_pred) // label_period_candles, label_period_candles)
 
     error = sklearn.metrics.root_mean_squared_error(y_test, y_pred)