fix(qav3): fix array like shape for rmse

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 602b6ab0cf6af5fb032269433a4cdf1673016a9f..8a2c9c7f43407a3aa1867469de1fd41289cc1a68 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -614,9 +614,9 @@ def period_objective(
         y_pred[i : i + label_period_candles]
         for i in np.arange(0, label_period_candles * n_windows, label_period_candles)
     ]
-    y_test = np.array([window for window in y_test_windows])
-    test_weights = np.concatenate(np.array([window for window in test_weights_windows]))
-    y_pred = np.array([window for window in y_pred_windows])
+    y_test = [window for window in y_test_windows]
+    test_weights = np.concatenate([window for window in test_weights_windows])
+    y_pred = [window for window in y_pred_windows]
 
     error = sklearn.metrics.root_mean_squared_error(
         y_test, y_pred, sample_weight=test_weights

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index a9e03843b534f1ad2466ebe3a2db041bb68462c4..54a7ca3c31f297cd85aea6b021e595bdefaeda10 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -622,9 +622,9 @@ def period_objective(
         y_pred[i : i + label_period_candles]
         for i in np.arange(0, label_period_candles * n_windows, label_period_candles)
     ]
-    y_test = np.array([window for window in y_test_windows])
-    test_weights = np.concatenate(np.array([window for window in test_weights_windows]))
-    y_pred = np.array([window for window in y_pred_windows])
+    y_test = [window for window in y_test_windows]
+    test_weights = np.concatenate([window for window in test_weights_windows])
+    y_pred = [window for window in y_pred_windows]
 
     error = sklearn.metrics.root_mean_squared_error(
         y_test, y_pred, sample_weight=test_weights